HumAIn Podcast

Edge AI Revolution: Building Private Enterprise Automations with Knapsack's Mark HeynenMark Heynen is the Co-founder and Chief Product Officer at Knapsack, where he's building private AI automations for enterprise use. A seasoned entrepreneur and technology executive, Mark has founded five companies and held key positions at tech giants including Google and Meta (formerly Facebook). His career spans from pioneering online pricing analytics in London to expanding mobile technology access in emerging markets.Episode Highlights:[00:00-03:21] From Startup to Big Tech: Heinen's Journey[03:21-06:36] Knapsack's Three Pillars for Enterprise AI[06:36-10:00] Edge Computing Transforms Small Language Models[10:00-15:40] AI Applications Across Industry Sectors[15:40-20:05] AI Automation Reshapes Future of Work[20:05-23:23] Transforming Professional Work Through AI Episode Links:  Knapsack: https://www.knapsack.ai/ Mark Heynen’s LinkedIn: https://www.linkedin.com/in/markheynen/ Mark Heynen’s Twitter: http://x.com/markheynen PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist:   https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter:  https://x.com/dyakobovitch  - LinkedIn:  https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Human Firewall: AI's Double Edge in Cybersecurity with Rob Gurzeev of CyCognitoRob Gurzeev is the CEO and Co-Founder of CyCognito, a cutting-edge cybersecurity company trusted by over 20 of the Global 100 companies. With a background in the elite Israeli Intelligence Corps unit 8200, Rob brings a unique blend of offensive security expertise and innovative thinking to the cybersecurity landscape. Prior to founding CyCognito in 2017, he led the Offensive Security group at C4 (later acquired by Elbit Systems), where he developed intelligence-gathering platforms for agencies.Episode Highlights:[00:00] Introduction: HumAIn and Rob Gurzeev[01:01] Rob's Journey: Intelligence to Silicon Valley[02:03] Technology Potential vs. Implementation Gap[04:02] Application Security's Coverage Problem[06:20] Attackers Exploit Path of Least Resistance[09:03] AI: Double-Edged Sword in Cybersecurity[11:35] AI Revolutionizing Reconnaissance[15:40] Precision and Recall in Security AI[17:19] Asset Classification and Attribution Challenges[21:01] Scale of Vulnerability Management[26:04] Critical Thinking in AI Age[28:39] CyCognito's External Attack Surface Management[30:51] Closing ThoughtsEpisode Links:  CyCognito: https://www.cycognito.com/ Rob Gurzeev’s LinkedIn: https://www.linkedin.com/in/gurzeev/ Rob Gurzeev’s Twitter: https://x.com/Rob_Gurz PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist:   https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter:  https://x.com/dyakobovitch  - LinkedIn:  https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyy Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Beyond Spreadsheets: How Ambient AI is Reshaping Financial Planning with Runway’s CEO Siqi ChenSiqi Chen is the CEO and Founder of Runway, a finance platform revolutionizing business planning and analysis. With a diverse background spanning gaming, social media, and technology, Siqi has been a serial entrepreneur and leader in the tech industry for over two decades. He previously served as CEO of Sandbox VR and held executive positions at Postmates and Zynga. Siqi's experience ranges from software engineering at NASA's Jet Propulsion Laboratory to founding and selling a gaming company to Zynga. His expertise in product development, growth strategies, and financial planning has led him to create Runway, a platform that aims to disrupt the $80 trillion business industry by integrating ambient intelligence into financial planning and analysis. Siqi is also an angel investor, supporting various successful startups in the tech ecosystem. He holds a BA in Mathematics and Computer Science from the University of California, San Diego.Episode Highlights:[00:03] Introducing Runway: Revolutionizing Financial Planning[01:39] Redefining Finance Through Software[03:30] Sandbox VR: Catalyst for Financial Innovation[06:12] Reimagining Interfaces: Design-First Financial Approach[08:07] Ambient Intelligence: New AI Paradigm[10:46] Building Complex Products: Challenges and Innovations[13:44] Common Pain Points in Financial Planning[16:33] Disrupting Finance: Overcoming Industry Challenges[18:25] Integrations: Creating Holistic Business Simulations[20:52] Future of Finance Teams: Strategic PartnersEpisode Links:  Runway: https://runway.com/ Siqi Chen’s LinkedIn: https://www.linkedin.com/in/siqic/ Siqi Chen’s Twitter: https://x.com/blader PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist:   https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter:  https://x.com/dyakobovitch  - LinkedIn:  https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyy Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Secure RAG Systems: A DeepTech Exploration with Protecto’s COO, Protik MukhopadhyayProtik Mukhopadhyay is the Chief Operating Officer (COO) at Protecto.ai, a venture-backed company specializing in secure and privacy-focused Retrieval-Augmented Generation (RAG) solutions. With over 15 years of experience in artificial intelligence, large language models, and data privacy, Protik is a seasoned entrepreneur and thought leader in the AI industry.OUTLINE:3:03 RAG Systems Key Dimensions5:46 RAG Implementation Challenges8:31 Effective RAG Use Cases11:16 AI Ethics in RAG14:01 Protecto's Data Protection Approach17:31 RAG Development Lessons Learned20:16 On-premise vs. SaaS Deployment22:46 Role-based Access in RAGEpisode Links:  Protecto AI: https://www.protecto.aiWhitepaper: https://www.protecto.ai/trustworthy-ai-whitepaper Sign up for a GenAI Strategy Roadmap Session: https://aistrategynow.com/  Protik Mukhopadhyay’s LinkedIn: https://www.linkedin.com/in/protikm/ Protik Mukhopadhyay’s Twitter: https://twitter.com/protik_m PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist:  https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter: https://x.com/dyakobovitch  - LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Scaling AI For Enterprise: Inflection AI’s Roadmap to Human-Centered Solutions with CEO Sean WhiteSean White is the CEO of Inflection AI, a pioneer in human-centered artificial intelligence. With a career spanning decades in tech innovation, Sean has been at the forefront of computer vision and AI technology. His experience includes key roles at Mozilla as Chief R&D Officer, work on augmented reality at the Smithsonian and Columbia University, and contributions to early web-based email systems. Sean's passion for human-computer interaction and collaborative intelligence drives Inflection AI's mission to create AI systems that enhance human capabilities and improve organizations.OUTLINE:0:00 - Introduction and Sean's background4:09 - Inflection AI's position in the AI landscape8:43 - Balancing consumer and enterprise AI products10:14 - Inflection AI Studio approach12:59 - Emotional intelligence in AI development15:12 - Open source philosophy in AI18:09 - Ideal use cases for Inflection AI in enterprises21:00 - Rollout strategy for enterprise AI solutions23:16 - Closing thoughts and call to actionEpisode Links:  Inflection AI: https://inflection.ai/ Request Inflection AI API Access: https://docs.google.com/forms/d/e/1FAIpQLScM9Iz1KzaRlfgDrYrldoPDnXbhO5LW3-hqmQCd56YpheEN7g/viewform Sean White’s LinkedIn: https://www.linkedin.com/in/seanwhite/Sean White’s Twitter: https://twitter.com/seanwhite PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist:  https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter: https://x.com/dyakobovitch  - LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

AI Strategy Unveiled: Former IBM Chief AI Officer on Enterprise AI SuccessSeth Dobrin is a prominent figure in the AI and data science industry. He is the co-founder and GP of One Infinity Ventures, a venture fund focused on deep tech and responsible AI. Seth is also the founder of Quantum AI, a consulting company specializing in AI strategy, governance, and education for Fortune 500 companies and governments worldwide. Previously, he served as the Chief AI Officer at IBM. With nearly two decades of experience in data and AI transformations, Seth is the author of "AIQ: For a Human-Focused Future," which outlines his methodology for successfully implementing AI in enterprise settings.OUTLINE:01:04 Introduction of Seth Dobrin and his background04:58 Early corporate AI initiatives described as a "scam"08:30 Aligning AI with business strategy10:41 The concept of AI IQ13:01 Role of the Chief AI Officer16:37 Data quality and governance19:23 Coexistence of traditional AI/ML and Gen AI21:30 Balancing innovation with ethical considerations23:31 Early warning signs of AI initiatives going off track24:55 Fostering an AI-ready culture28:23 Challenges and opportunities in AI adoption29:52 Closing remarks and book promotionEpisode Links:  Seth Dobrin’s LinkedIn: https://www.linkedin.com/in/sdobrin/ Seth Dobrin Website: https://drsethdobrin.com/ PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist:  https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter: https://x.com/dyakobovitch  - LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Beyond ChatGPT: Unlocking Enterprise Value in the Age of Generative AIPaul Baier is the CEO of GAI Insights, a company specializing in generative AI strategies for enterprises. With over 25 years of experience in B2B sales and venture-backed companies, Paul has become a thought leader in the AI space. He previously worked at FirstFuel, using traditional AI for building efficiency analysis. Paul is known for developing frameworks like "Own Your Own Intelligence" (OYOI) and WINS, which help businesses navigate the rapidly evolving landscape of generative AI. He also leads weekly Gen AI learning labs and is actively involved in initiatives to grow AI talent in Massachusetts.0:00 - Introduction2:15 - Paul's AI journey4:30 - OYOI concept7:45 - WINS framework11:20 - Gen AI learning labs15:40 - AI Blueprint for MA19:30 - Embracing AI change22:45 - GAI Insights initiatives24:15 - Closing remarksEpisode Links:  Paul Baier’s LinkedIn: https://www.linkedin.com/in/paulbaier GAI Insights OYOI: https://gaiinsights.com/own-your-own-intelligence AI Blueprint: https://ai-blueprint-ma.com/ GAI Insights News: https://gaiinsights.com/news-1-0 GAI Insights Learning Lab: https://gaiinsights.com/learning-lab PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist: https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter: https://x.com/dyakobovitch  - LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ethical AI in Action: How Plainsight is Transforming Business IntelligenceKit Merker is the CEO of Plainsight Technologies, a company specializing in computer vision and AI solutions. With over 20 years of experience in the tech industry, Kit has held positions at major companies like Microsoft and Google, where he was an early team member for Kubernetes. His expertise spans developer tools, DevOps, cloud computing, and now AI applications for business. Kit is passionate about responsible AI development and implementing ethical practices in the rapidly evolving field of artificial intelligence.OUTLINE:0:00 - Introduction and Kit's background4:08 - Plainsight's mission and technology7:57 - Evolution of computing power and AI applications11:12 - Ethical AI and the future of work15:41 - AI demos and technological hype20:40 - Responsible AI and data usage in business28:38 - Importance of ethical AI implementation30:09 - Conclusion and call to actionEpisode Links:  Kit Merker’s LinkedIn: https://www.linkedin.com/in/kitmerker/ Plainsights Website: http://plainsight.ai/filters PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Full episodes playlist: https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter: https://x.com/dyakobovitch  - LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyy Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Next Frontier in AI: Multi-Agent Frameworks and the Path to AGIMartin Musiol is an AI expert, founder, and CEO of GenerativeAI.net. With a background from the Technical University of Munich, Martin has been at the forefront of generative AI since 2016. He created the world's first online course on generative AI and has worked as the Gen AI lead for Europe at Infosys. Martin is the author of "Generative AI: Navigating the Course to the Artificial General Intelligence Future" and is currently building a startup focused on multi-agent AI frameworks.0:00 - Introduction3:00 - GANs to Transformers8:00 - Mamba architecture14:00 - Current state of Generative AI20:00 - Martin's book on Generative AI and AGI27:00 - AI-powered robotics in industry29:30 - RAG systems34:30 - Context windows in language models35:10 - Martin's new venture39:00 - Closing thoughts on Generative AI economyEpisode Links:  Martin Musiol LinkedIn: https://www.linkedin.com/in/martinmusiol1/ MartinMusiol Website: https://generativeai.net/ Generative AI Book: https://www.amazon.com/dp/1394205910 PODCAST INFO:Podcast website: https://www.humainpodcast.com  Apple Podcasts: https://apple.co/4cCF6PZ Spotify: https://spoti.fi/2SsKHzg RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9Full episodes playlist:  https://www.humainpodcast.com/episodes/ SOCIAL:- Twitter: https://x.com/dyakobovitch  - LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ - Events: https://lu.ma/tpn - Newsletter: https://bit.ly/3XbGZyyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Javier Luraschi: AI as Your Co-pilot: Hal9's CEO on Reshaping Enterprise WorkflowsBio: Javier Luraschi is the CEO and Founder of Hal9. With over 15 years of experience in software engineering, Javier has worked at companies like Microsoft Research, RStudio (now Posit), and SAP. He co-created open-source tools such as MLflow and ported PyTorch and Spark to R. Javier is passionate about democratizing AI and helping enterprises leverage generative AI technologies.Show Notes:From Microsoft Access to Modern Data Science: Javier's Journey Through Tech EvolutionThe Transition from Statistics to Data Science and Machine LearningAI2 and the Genesis of Hal9: Focusing on Code Generation for Enterprise AIDemocratizing AI: Empowering Business Users with Code Generation ToolsThe Future of Work: Human-AI Collaboration vs. Full AutomationRedefining Roles: From Prompt Engineer to Prompt SpecialistHal9's Enterprise Solution: Bridging the Gap Between ChatGPT and Custom AI IntegrationThe Strategic Value of AI Customization for BusinessesThe Future Landscape of LLMs in Enterprise: Diversity and CustomizationCall to Action: Exploring Hal9 for Personal and Enterprise UseEpisode Links:  Javier Luraschi LinkedIn: https://www.linkedin.com/in/javierluraschi/ ESG Flo Website: https://hal9.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Support and Social Media:  – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Max Cho is the CEO and co-founder of Coverage Cat, a startup revolutionizing the insurance industry through data-driven solutions. With a diverse background in technology and finance, Max has held key positions at industry giants including Google, Two Sigma, and Microsoft. His expertise spans software reliability, quantitative analysis, and consumer-focused product development. Driven by personal experiences with insurance complexities, Max founded Coverage Cat to simplify the insurance buying process and empower consumers with transparent, optimized insurance options. His unique blend of technical knowledge and entrepreneurial spirit positions him at the forefront of innovation in the InsurTech sector.In this episode we discuss:Max Cho's Journey: From Tech Giants to Revolutionizing InsuranceThe Birth of Coverage Cat: Addressing Personal Pain Points in InsuranceUnveiling Inefficiencies: The Current Landscape of the Insurance IndustryCrisis Management: Navigating Insurance Challenges in Florida and BeyondAI's Double-Edged Sword: Potential and Pitfalls in InsuranceGlobal Perspective: Comparing U.S. Insurance Complexities with International MarketsCoverage Cat's Innovation: Data-Driven Solutions for Insurance ConsumersRegulatory Reform: Shaping a More Transparent Insurance IndustryEmpowering Consumers: Expert Advice on Navigating Insurance ChoicesEpisode Links:  Max Cho LinkedIn: https://www.linkedin.com/in/maxrcho/Coverage Cat Website: https://www.coveragecat.com/Learn More:https://www.coveragecat.com/umbrella-insurance https://www.coveragecat.com/carrier-comparisonAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Data Dilemma: How Blind Insight is Revolutionizing Secure Analytics for Enterprises ft. Jackie Peters and Nick SullivanJackie Peters: Co-founder and CEO of Blind Insight, Jackie brings over 25 years of experience in tech, with a strong focus on healthcare and privacy. Her career spans product development, health tech, and decentralized technologies, including a role as the founding product person at Orchid.Nick Sullivan: Technical co-founder of Blind Insight, Nick has extensive expertise in cryptography, security, and privacy-enhancing technologies. With a decade of experience building security and cryptography systems at Cloudflare, Nick is passionate about applying privacy technologies to solve real-world data security challenges.In this episode we discuss:Encrypted Database InnovationFounders' Diverse Tech BackgroundsData-Driven Economy in 2024Privacy and Security Challenges in Data UtilizationBlind Insight's Encrypted Analytics SolutionPublic Beta Launch and Current CapabilitiesDeveloper-Centric Product DesignExpanding Encrypted Data OperationsPioneering "Encryption in Use" MarketEpisode Links:  Jackie Peters LinkedIn: https://www.linkedin.com/in/jackiepeters/ Nick Sullivan LinkedIn: https://www.linkedin.com/in/ntsullivan/Blind Insight Website: https://www.blindinsight.comSign up for the Beta - free for 30 days no credit card. beta.blindinsight.ioAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Patrick Obeid: How AI Simplifies ESG Reporting and Data Infrastructure w/ ESG Flo[Audio] Patrick Obeid, is the founder of ESG Flo, the leading ESG software that leverages artificial intelligence to seamlessly automate the collection and transformation of ESG data into audit-ready metrics.In this episode we discuss:Introduction to the HumAIn podcast and ESG FlowPatrick's journey from consultant to entrepreneurTransition from advisor to operator in tech industryDiscovery process: Interviewing 100 executives in 60 daysIdentifying the need for non-financial data infrastructureWhy ESG matters now: Climate crisis and wealth gapESG Flow's focus on heavy industries and key metricsThree-layer approach to ESG data managementCSRD compliance and creating the ESG auditability marketEpisode Links:  Patrick Obeid LinkedIn: https://www.linkedin.com/in/patrick-obeid-esg/ESG Flo Website: https://www.esgflo.com/Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Support and Social Media:  – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Max Galka: How AI Transforms Decision-making on the Blockchain[Audio] Max Galka is the CEO of Elementus, the first universal search engine for blockchain and institutional grade crypto forensics solution.In this episode, we talk about all things Blockchain, Bitcoin, Data, and AI.Episode Links:  Max Galka LinkedIn: https://www.linkedin.com/in/maxgalka/Elementus Website: https://www.elementus.io/Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 Support and Social Media:  – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Steven Banerjee: How Machine Intelligence, NLP and AI is changing Health Care  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSteven Banerjee is the CEO of NExTNet Inc. NExTNet is a Silicon Valley based technology startup pioneering natural language based Explainable AI platform to accelerate drug discovery and development. Steven is also the founder of Mekonos, a Silicon Valley based biotechnology company backed by world-class Institutional investors (pre-Series B) — pioneering proprietary cell and gene-engineering platforms to advance personalized medicine. He also advises Lumen Energy, a company that uses a radically simplified approach to deploy commercial solar. Lumen Energy makes it easy for building owners to get clean energy.  Please support this podcast by checking out our sponsors:Episode Links:  Steven Banerjee LinkedIn: https://www.linkedin.com/in/steven-banerjee/ Steven Banerjee Website: https://www.nextnetinc.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (05:20)- So I am a mechanical engineer by training. And I started my graduate research in semiconductor technologies with applications in biotech almost more than a decade ago, in the early 2010s. I was a Doctoral Fellow at IBM labs here in San Jose, California. And then I also ended up writing some successful federal grants with a gene sequencing pioneer at Stanford, and Ron Davis, before I went, ended up going to UC Berkeley for grad school research, and then I became a visiting researcher.  (09:28)- An average cost of bringing a drug to market is around $2.6 billion. It takes around 10 to 15 years, like from the earliest days of discovery, to launching into the market. And unfortunately, more than 96% of all drug R&D actually fails . This is a really bad social model. This creates this enormous burden on our society and our healthcare spending as well. One of the reasons I started NextNet was when I was running Mekonos, I kept on seeing a lot of our customers had this tremendous pain point of, where you go, there's all this demand and subject matter experts, as scientists, they're actually working with very little of the available biomedical evidence out there. And a lot of the times that actually leads to false discoveries. (13:40)- And so there are tools, they're all this plethora of bioinformatics tools and software and databases out there that are plagued with program bugs. They mostly lack documentation or have very complicated documentation and best, very technical UI’s. And for an average scientist or an average person in this industry, you really need to have a fairly deep grasp or a sophisticated understanding of database schemas and SQL querying and statistical modeling and coding and data science.  (22:36)- So, a transformer is potentially one of the greatest breakthroughs that has happened in NLP recently. It's basically a neural net architecture that was incorporated into NLP models by Google Brain researchers that came along in 2017 and 2018. And before transformers, your state of the art models and NLP basically were like, LSTM, like long term memories are the widely used architecture. (27:24)- So Sapiens is, our goal here is to really make biomedical data accessible and useful for scientific inquiry, using this platform, so that, your average person and industry, let's say a wet lab or dry lab scientist, or a VP of R&D or CSO, or let's say a director of research can ask and answer complex biological questions. And a better frame hypothesis to understand is very complex, multifactorial diseases. And a lot of the insights that Sapiens is extracting from all this, with publicly available data sources are proprietary to the company. And then you can map and upload your own internal data, and begin to really contextualize all that information, by uploading onto the Sapiens.  (31:34)- We are definitely looking for early adopters. This includes biotech companies, pharma, academic research labs, that would like to test out Sapiens and like this to be a part of their journey of their biomedical R&D. We're definitely, as I said, looking for investors who would like to partner with us, as we continue on this journey of building this probably one of the most sophisticated natural language based platforms, or as we call it, an excellent AI platform.  Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSteve received his PhD from Johns Hopkins University in Cognitive Science where he began his AI research and also taught Statistics at Towson State University. After receiving his PhD in 1979, AI pioneer Roger Schank invited Steve to join the Yale University faculty as a postdoctoral researcher in Computer Science. In 1981, Roger asked Steve to help him start one of the first AI companies, Cognitive Systems, which progressed to a public offering in 1986.  Steve then started Esperant, which produced one of the leading Business Intelligence products of the 1990s. During the 1980s, Steve published 35 articles and a book on AI, spoke at many AI conferences, and received two commercial patents on AI. As the AI Winter of the 1990s set in, Steve transitioned into a career as a successful serial software entrepreneur and investor and created several companies that were either acquired or had a public offering.  He tries to use his unique perspective as an early AI researcher and statistician to both explain how AI works in simple terms, to explain why people should not worry about intelligent robots taking over the world, and to explain the steps we need to take as a society to minimize the negative impacts of AI and maximize the positive impacts. Please support this podcast by checking out our sponsors:Episode Links:  Steven Shwartz LinkedIn: https://www.linkedin.com/in/steveshwartz/ Steven Shwartz Twitter: https://twitter.com/sshwartz Steven Shwartz Website: https://www.device42.com Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(09:42) – So most of the things that are taking jobs for example, is conventional software, not AI software.(10:57)- Exactly. And that's automated but it's conventional software. It's not AI. And most of the examples of where computers are replacing people, it's conventional software. It's not AI software.(14:49)- How you get data quality into your AI models and it's what they do that's really interesting. And I hadn't actually focused on it until I talked to this company. There's a big industry to clean data for tools like business intelligence that have been around for a long time. And there are, there are companies that are multi-billion dollar companies that provide data, cleaning tools, data extraction, and so forth.(17:13)- Everybody thought that with AI, you could diagnose illnesses from medical images better than the radiologists. And it's never actually worked out that way. I have friends who are radiologists, who use those AI tools and they say yes, sometimes they find things that I might've missed. But at the same time, they miss things that we would have found.(22:17)- I think we're seeing a lot of the rollout of a specific type of AI supervised learning, which is a type of machine learning. We're seeing it applied in many different areas. I actually have a database I keep before every time I see a new application of supervised learning and it's fascinating. It's being used in almost every area of business, of government, of the nonprofit world. It is fascinating how much application there is.  (27:06)- And they're not really going to make sense if you drill down into them. So what's going to be the implication of that. Is it only going to be useful if there's all kinds of search engine optimization where you don't really care If what you're right makes sense. We're going to generate a lot of crap using GPT three and put it out there for search engine optimization purposes.(31:19)- And I think there's a lot of opportunity for companies that are helping develop software and services to help companies build non-biased explainable systems. And then you have a whole issue around when you build a machine learning system, it deteriorates over time. So it might only work for a couple of days and then start to go downhill. It might work for weeks, but you have to monitor those systems and go back and retrain them when the performance goes down. And all of that is a lot of effort. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSGianluca Mauro is the CEO of AI Academy, which he founded with the mission of helping people understand what artificial intelligence is and its place in their organizations and their career. Gianluca is the author of the book "Zero to AI - A nontechnical, hype-free guide to prospering in AI era" Over the years, Gianluca and his team have done both technical consulting and training workshops, working with companies like P&G, Merck, Brunello Cucinelli, Daikin, Fater, Bayer, and EIT Innoenergy Gianluca teaches Artificial Intelligence to people without a tech background, without any code or math. Why? Because he believes, the future of artificial intelligence is in the hands of people who can find use cases in their organizations, and then define and run AI projects. Please support this podcast by checking out our sponsors:Episode Links:  Gianluca Mauro LinkedIn: https://www.linkedin.com/in/gianlucamauro/ Gianluca Mauro Twitter: https://twitter.com/gianlucahmd Gianluca Mauro Website: https://ai-academy.com Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (04:15)-Sometimes it's not a concept that people are familiar with. It sounds weird to anybody who works in tech. But, a lot of companies, in these industries, are still struggling with the cloud. So, when you go to these companies and start talking about this technology, they are excited. They're like, this sounds amazing, but you have to keep into account the reality of where they are, they're not in a place where they can invest in hiring a full-blown data science team, because then nobody knows how to interact with them. (09:29)- So, having the right governance for how to use the data, how to keep it in the right shape, and making sure that the quality is what we need, and then actually bring into the laptops of the data scientists that they can make tests and run experiments and make graphs. So, I always like to say it doesn't really matter how good your technology is. How good is your data warehouse or whatever kind of stock you use if using that data is not easy. If using that data it's not straightforward for a data scientist. (17:32)- And in the same way, if we want to use AI for marketing, you need to give tools to the marketers that understand the problem to use AI on their data for their problems. When I talk about sales, well, I understand sales data set and takes me a lot of time to understand the logics of sales, have a sales team of the data that its Sales team works with to a sales team who really understands this data, the right tools to, they don't have to be able to do everything but the list to get started, well, then they know much better than me the data.  (18:17)- So, it's kind of a paradox, because the most important thing of the app is the recommender system. But the reason why that works is not because of the tech, but because of how the UX feeds the tech. And if you think about this, think about this concept, well, then your UX designers, they need to understand this, they need to understand what it means to feed an algorithm with the right data.  (23:40)- And so we have seen cases where these things went wrong. And I may start from the stuff that everybody knows about, the elections in 2016, fake news and all this stuff up until more niche, let's say topics that maybe not a lot of people aren't aware of. But that actually had a strong impact on people. An example is AI in hiring. There was a very interesting research made by MIT Technology Review about how a lot of companies that sell software for hiring and leverage AI are actually biased. (31:01)- And it has been amazing, honestly, because then you'll have people coming from all sorts of backgrounds. I give them the tools and the foundational knowledge that they need to talk about these topics in a way that is productive and they bring the wrong perspectives. They bring their own experience. And I had to say, I've been amazed by the insights that we were able to get from these conversations. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBen Zweig is the CEO of Revelio Labs, a workforce intelligence company. Revelio Labs indexes hundreds of millions of public employment records to create the world’s first universal HR database. This allows Revelio Labs to understand the workforce dynamics of any company. Revelio customers include investors, corporate strategists, HR teams, and governments.Ben worked as a data scientist at IBM where he led analytic teams. He is an economist and entrepreneur and also an adjunct professor at Columbia Business School and NYU Stern School of Business respectively. He teaches courses currently at NYU Stern School of Business including future of work, data boot camp and econometrics.Please support this podcast by checking out our sponsors:Episode Links:  Ben Zweig LinkedIn: https://www.linkedin.com/in/ben-zweig/ Ben Zweig Twitter: https://twitter.com/bjzweig Ben Zweig Website: https://www.reveliolabs.com Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (02:56)- So, I started my career in academia, I was doing a Ph.D. in economics and specialized in labor economics. So I was always very interested in labor data, and understanding occupational dynamics, social mobility, things like that. My first job was a data scientist, this was very early on at a hedge fund in New York. It was an emerging market hedge fund. I started that in 2012. That was kind of interesting. I was like the lone data scientist on the desk. So that was kind of interesting. And then went to work at IBM, in their internal data science team was called the Chief Analytics Office. (08:13)- The workers that were really hardest hit from remote work are really junior employees. They're just getting started and they need that mentorship. And it's much harder to feel like you're developing and learning from others in a remote environment. But as we're sort of going back, the more senior positions, will probably not have that same benefit as junior employees. (15:53)- One phenomenon that we see quite a lot is that companies have a huge contingent workforce that is not reported on their financial statements. So, for example, I mentioned I used to run this workforce analytics team at IBM. And at IBM, we had 330,000 employees, that was like the number that's in their HR database, but you go to their LinkedIn page, and it looks like 550,000 people say that they work at IBM. So, what's going on here? Why are there so many more people that claim to work at a company, then the company claims to work there? And that, of course, is just a sample; only a sample of people actually have online profiles.  (29:33)- But when it comes to human capital data, and employment data, that really does not exist, it's not even really close to that. There's so much data that's siloed in internal HR databases, which like I mentioned before, really only include a fraction of the overall workforce of a company. But what's cool about this is that when an employee is stored in an HR database, that information is mirrored in the public domain. (21:22)- So, we really have to create a taxonomy that updates that changes with an evolving occupational landscape and the changing economy. We also really need to infer the activities that people do, because those are the building blocks of a job, or the job is a bundle of activities. So, we really need to understand that when one person says lawyer and another person says, attorney, those are probably the same occupation, but when one person says Product Manager in Facebook versus a Product Manager at JPMorgan, those might be totally different occupations. (30:21)- So, what are the HR tech companies that are really dominating, and then it gets even specific, who's dominating the self-driving car market, how benefits help retention of women in the workforce, that's something that we've seen some changes in the past couple of years. We did a piece that I really liked, which was tracking the rise and fall of hustle culture. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSEdo Liberty is the CEO of Pinecone, a company hiring exceptional scientists and engineers to solve some of the hardest and most impactful machine learning challenges of our times. Edo also worked at Amazon Web Services where he managed the algorithms group at Amazon AI. As Senior Manager of Research, Amazon SageMaker, Edo and his team built scalable machine learning systems and algorithms used both internally and externally by customers of SageMaker, AWS's flagship machine learning platform. Edo served as Senior Research Director at Yahoo where he was the head of Yahoo's Independent Research in New York with focus on scalable machine learning and data mining for Yahoo critical applications.Edo is a Post Doctoral Research fellow in Applied Mathematics from Yale University. His research focused on randomized algorithms for data mining. In particular: dimensionality reduction, numerical linear algebra, and clustering. He is also interested in the concentration of measure phenomenon. Please support this podcast by checking out our sponsors:Episode Links:  Edo Liberty LinkedIn: https://www.linkedin.com/in/edo-liberty-4380164/ Edo Liberty Twitter: https://twitter.com/pinecone Edo Liberty Website: https://www.pinecone.io Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (06:02)- It's funny how being a scientist and building applications and building platforms are so different. It's kind of like for me it's just by analogy, I mean, kind of a scientist, if you're looking at some achievement, like technical achievement as being a top of a mountain and a scientist is trying to like hike, they're trying to be the first person to the summit. (06:28)- When you build an application, you kind of have to build a road, you have to be able to drive them with a car. And when you're building a platform on AWS or at Pinecone, you have to like build a city there. You have to really like, completely like to cover it. For me, the experience of building platforms and AWS was transformational because the way we think about problems is completely different. It's not about proving that something is possible, it is building the mechanisms that make it possible always for, in any circumstance. (13:43)- And so on and today with machine learning, you don't really have to do any of that. You have pre-trained NLP models that convert a string, like a, take a sentence in English to an embedding, to a high dimensional vector, such that the similarity or either the distance or the angle between them is analogous to the similarity between them in terms of like conceptual smelts semantic similarity.(18:17)- Almost always Pinecone ends up being a lot easier, a lot faster and a lot more production ready than what they would build in house. A lot more functional. We've spent two and a half years now baking a lot of really great features into Pinecone. And we're, we've just launched a version 2.0 that contains all sorts of filtering capabilities and cost reduction measures and you name it.    (21:22)- And so I'm a great believer in knowing your own data and knowing your own customers and training your own models. It doesn't mean that you have to train them from scratch. It doesn't mean you don't have to use the right tools. You don't have to reinvent the wheel, but I'm not a big believer in completely pre-trained, plucked off of a random place in the internet models. I do want to say that there are great models for just feature engineering for objects that don't change so much. So we have language models like BERT that transform text and create great embeddings and they're a good starting point. (31:01)- So I think you'll see two things. First of all, with Pinecone specifically, we're focused on really only two things; making it easy to use and get value out of Pinecone and making it cheaper. That's it! I mean that, those are the only two things we care about. Like if you can get a ton of value out of it and it doesn't cost you too much, that's it, you're a happy customer and we're happy to get you there. So that pretty much sums up all of our focus. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Thor Ernstsson: How To Use Data Science for Stronger Relationships  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSThor Ernstsson is the CEO of Strata, a company that helps customers invest in their networks, no matter how busy they are. Strata enables intelligent outreach recommendations that strengthen professional relationships. With their easy to use platform, clients become more thoughtful and helpful to the most important people in their network.Thor is also the founder of Feedback Loop, which companies use to build real time feedback loops with their target markets. Basically customer development delivered at scale. Used by half of the F100 as well as some of the best tech companies around. Thor previously served as CTO of Audax Health and lead architect at Zynga where helped build up Zynga's first remote studio. Thor and the team at Zynga created and released Frontierville as the company's most successful product launch at the time. Episode Links:  Thor Ernstsson´s LinkedIn: https://www.linkedin.com/in/thorernstsson/Thor Ernstsson´s Twitter: https://twitter.com/ThorErnstssonThor Ernstsson´s Website: https://www.strata.cc/Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:24) – It starts in the very beginning in rural Iceland. I grew up on the Northern coast of Iceland, in a little fishing village. We're about 450 people in technology there, which is a little bit different than how we think of it today. But, in a roundabout way, we ended up in New York, 20 years in the US and 10 in New York and absolutely love it here. And the reason is primarily that there's so much creative energy around, exactly your topic.(03:34) – So what we were doing at Feedback Loop, the core of it is really you take a business question: Is this going to work, for example. Which is not a well-formed research question. So we have to translate it into the intent of the question. What you're intending to do is assess functionality or competitors features or price point or messaging or whatever it is.(07:13) – Because, even though you can only juggle in your mind, let's just say 150, and the number is a bit fuzzy, but let's say that it is 150. You interact with thousands of people throughout your career, and you go to a conference and you meet a bunch of great, interesting people that you want to stay in touch with. You have coworkers that you may have worked with five years ago, 10 years ago, doing either something really fascinating and you want to stay in touch, or they're just friends and you liked interacting with them and you want to stay in touch.(10:10) – Most people, when they first think about it, they're like: I want more out of my network. But when we interview, especially the more senior, and we interview people, what we learn is the same thing over and over. It's not that they want to get something out of their network. It's not that they want to know who they should reach out to for sale or for deal or for VC. You need to stay in touch with their LPs and stuff like that, but it's really more about giving back.(13:31) –You just highlight a perfect example, people can't actually track all the communication again. There are so many things that fall through. So what we do first is we start with a bunch of rules. So there's heuristics around what might be important. It's this sort of static analysis of your communication and your calendar of your stuff like that. And then what we learn over time is who's important to you.(17:30) – The COVID and just in general, digitization of everything and making everything Zoom makes this problem much worse, because before you would get a coffee, you would see somebody in person, you have all these nonverbal cues, you have all these triggers and all those memories that are way more than what you have when it's just pixels on a screen. (21:22) – We're helping you uncover the things you should be doing, even if you don't know what you should be doing. That's kind of the key here is that it's doing the thinking and the heavy lifting for you. You click to accept it. You can reach out. You can action it. You can say like create a task out of it, basically. So that if I say to you in an email, or if you just send many emails ago, like that you used to introduce me to other speakers or podcasts.(24:53) – There's a lot of really interesting work that has been done that we can leverage in your right, that like building this from scratch even 10 years ago would not be possible. It's everything from memory constraints on the actual servers. The fact that I can spin up a 90, it was a 96 or 92 core Amazon instance and just at the click of a button and trained a model. I couldn't have done that before. So it would have been prohibitively expensive and improvely hard, actually, it's just not wasn't there. (25:53) – So there's lots of ways that email threads end, then we're trying to figure out. Can we tell which ones are natural and which ones are effectively errors, where you were when you dropped the ball on something. It's a fascinating problem. We have millions of messages to train on where you can see this. This ended and this didn't, and then we've got to figure out, how do you know if it was intentional or not.(28:55) – It's a combination of things. So, it's definitely the chief of staff in that way, but, arguably, it's more like a social secretary. So it's like helping organize the most important relationships you have. So for example, if you're traveling to Chicago, who should you reach out to? Because I've started heuristics, so obviously people that live there, fine. Second, people you met last time you were there, fine. Third, people you've talked about meeting up with in Chicago. Maybe you will remember that maybe you have a super memory where you're not limited by only 150 relationships and you can actually classify all minus like 30,000 people.(32:37) – We have a few products that we launched: the recommendations where you get three recommendations every week, plus memes and so corporate communication seems to be working. So that's live now called Reconnect. So definitely go to Straddled that CC and sign up for that. Then we're going to be launching the broader platform that I'm talking about that has all these integrated triggers, and nudges, and juristics, and patterns like travel, list building, list sharing, all those things that I suspect just about everybody who's listening to this does right now, and it'd be great to hear feedback.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSStephen Miller is the Cofounder and SVP Engineering at Fyusion Inc. He has conducted research in 3D Perception and Computer Vision with Profs Sebastian Thrun and Vladlen Koltun while at Stanford University. His area of specialization is AI and Robotics, which included 2 years of undergraduate research with Prof Pieter Abbeel. Please support this podcast by checking out our sponsors:Episode Links:  Stephen Miller’s LinkedIn: https://www.linkedin.com/in/sdavidmiller/ Stephen Miller’s Twitter: https://twitter.com/sdavidmiller Stephen Miller’s Website: http://sdavidmiller.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:42) – Started in robotics around 2010, training them to perform human tasks (surgical suturing, laundry folding). Clearest bottleneck was not “How do we get the robot to move properly” but “How do we get the robot to understand the 3D space it operates in?”   (04:05) – The Deep Learning revolution around that era was very focused on 2D images. But it wasn’t always easy to translate those successes into real world systems: the world is not made up of pixels; it’s made up of physical objects in space.(06:57) – When the Microsoft Kinect came out; I became excited about the democratization of 3D, and the possibility that better data was available to the masses. Intuitive data can help us more confidently build solutions. Easier to validate when something fails, easier to give more consistent results. (09:20) – Academia is a vital engine for moving technology forward. In hindsight, for instance, those early days of Deep Learning -- one or two layers, evaluating on simple datasets -- were crucial to ultimately advancing the state of the art we see today. (14:48) – Now that Machine Learning is becoming increasingly commodified, we are starting to see a growing demand for people who can bridge that gap on both sides: conferences requiring code submissions alongside a paper, companies encouraging their engineers to take online ML courses, etc.(17:41) – As we do finally start to see real-time computer vision productized for mobile phones, it does beg the question: won’t this exacerbate the digital divide? Flagship devices, always-on network connectivity: whether computing on the edge or in the cloud, there is going to be a disparity. (20:33) – Because of this, I think the ideal model is to treat AI as one tool among many in a hybrid system. Think smart autocomplete, as opposed to automatic novel writing. AI as an assistant to a human expert: freeing them from the minutia so they can focus on high-level questions; aggregating noise so they can be more consistent and efficient. (23:08) – Computer Vision has gone through a number of hype cycles in the last decade –real-time recognition, real-time reconstruction, etc. But the showiest of these ideas seem to rarely leave the realm of gaming, or tech demonstrator. I suspect this is because many of these ideas require a certain level of perfection to be valuable. It’s easy to imagine replacing my eyes with something that works 100% of the time. But what about 90%? At what point is the hassle of figuring out whether I’m in the 10% bucket or the 90% bucket, outweighing the convenience?Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Nell Watson: How To Teach AI Human Values   [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSNell Watson is an interdisciplinary researcher in emerging technologies such as machine vision and A.I. ethics. Her work primarily focuses on protecting human rights and putting ethics, safety, and the values of the human spirit into technologies such as Artificial Intelligence. Nell serves as Chair & Vice-Chair respectively of the IEEE’s ECPAIS Transparency Experts Focus Group, and P7001 Transparency of Autonomous Systems committee on A.I. Ethics & Safety, engineering credit score-like mechanisms into A.I. to help safeguard algorithmic trust.She serves as an Executive Consultant on philosophical matters for Apple, as well as serving as Senior Scientific Advisor to The Future Society, and Senior Fellow to The Atlantic Council. She also holds Fellowships with the British Computing Society and Royal Statistical Society, among others. Her public speaking has inspired audiences to work towards a brighter future at venues such as The World Bank, The United Nations General Assembly, and The Royal Society.Episode Links:  Nell Watson’s LinkedIn: https://www.linkedin.com/in/nellwatson/ Nell Watson’s Twitter: https://twitter.com/NellWatson Nell Watson’s Website: https://www.nellwatson.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (2:57)- Even though the science of forensics and police work has changed so much in those last two centuries, principles are great, but it's very important that we create something actionable out of that. We create criteria with defined metrics that we can know whether we are achieving those principles and to what degree.(3:25)- With that in mind, I’ve been working with teams at the IEEE Standards Association to create standards for transparency, which are a little bit traditional big document upfront very deep working on many different levels for many different use cases and different people for example, investigators or managers of organizations, etcetera.(9:04)- Transparency is really the foundation of all other aspects of AI and Ethics. We need to understand how an incident occurred, or we need to understand how a system performs a function in order to. I analyze how it might be biased or where there might be some malfunction or what might occur in a certain situation or a certain scenario, or indeed who might be responsible for something having gone through it is really the most basic element of protecting ourselves, protecting our privacy, our autonomy from these kinds of advanced algorithmic systems, there are many different elements that might influence these kinds of systems.(26:35)- We're really coming to a Sputnik moment and AI. We've gotten used to the idea of talking to our embodied smart speakers and asking them about sports results or what tomorrow's weather is going to be. But they're not truly conversational.(32:43)- Fundamentally technologies and a humane society is about putting the human first, putting human needs first and adapting systems to serve those needs and to truly and better the human condition to not sacrifice everything for the sake of efficiency to leave a bit of slack and to ensure that the costs to society of a new innovation or the costs to the environment are properly taken into effect.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ryan McDonald: How To Position People at the Center of AI Native Solutions [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSRyan McDonald is the Chief Scientist at ASAPP working on NLP and ML research focusing on CX and enterprise. He is also an Associate researcher in the NLP group at Athens University of Economics and Business. Ryan was a Research Scientist in the Language Team at Google for 15 years where he helped build state-of-the-art NLP and ML technologies and pushed them to production. He managed research and production teams in New York and London that were responsible for a number of innovations used in Translate, Assistant, Cloud and Search. He was the first NLP research scientist in both New York and London, and helped grow those groups into world-class research organizations. Prior to that, he did his Ph.D. in NLP at the University of Pennsylvania. Episode Links:  Ryan McDonald’s LinkedIn: https://www.linkedin.com/in/ryanmcd/ Ryan McDonald’s Twitter: https://twitter.com/asapp Ryan McDonald’s Website: http://www.ryanmcd.com CX: The Human Factor Report: https://ai.asapp.com/LP-2021-09-CX-The-Human-Factor_Landing-Page.htmlPodcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (3:00)- The kinds of problems that deploying AI runs into for enterprise is more about scalability. Instead of having a single user of the technology, we have hundreds of users of the technology and how can we deliver a unique experience and an excellent experience for each of those users and this necessitates questions around adopting machine learning and natural language processing models to new domains. (10:49)- And this is exactly the technology we're building out. How can we sort of regularize that? How can we look at the conversation and the issue that the customer's happening? That's sort of embodied in the dialogue, up to a point in time and then allow AI to make recommendations to the agent; Here is a workflow that we think you should use and all the steps you need to follow in order to solve this issue(28:33)- So we design everything and that's why it's critical to design these things from the bottom up with AI in mind. All of our artificial intelligence has been designed to serve those latency needs. So to kind of give you a couple of examples, the first is automatic speech recognition. So a huge number of calls that come into call centers are still voice, they're not digital. It's not people call contacting over chat. It's people calling in on their phone. (30:41)- So we've focused on building out something called SRU, which is an architecture where we can take super high, accurate AI models and then distill them into these faster architectures, which allows us to get into these millisecond range. So we can get responses back to agents and milliseconds, and that really is going to affect how much they use those suggestions at the end of the day.(32:38)- Beyond what's happening in the conversation and see everything, all the information and all the actions that the agent can possibly do on their computer. And so agent journey is a product where we, you know, put a piece of software on the agent's computer and it allows us to access into all the tools they're using, how they're using them, how that interacts with the conversation.(33:49)- Agent journey is our efforts in that space to understand everything holistically that the agent is doing to really make headway in task-oriented dialogue.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Humphrey Chen: How AI Can Revolutionize the Way We Consume Video [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSHumphrey Chen is the CEO and Co-Founder of CLIPr. He has a BS in Management Science from MIT. His work in tech specializes in the use of technology to make people and companies more productive.    Please support this podcast by checking out our sponsors:Episode Links:  Humphrey Chen’s LinkedIn: https://www.linkedin.com/in/humphreychen/ Humphrey Chen’s Twitter: https://twitter.com/humphreyc?s=20 Humphrey Chen’s Website: https://aws.amazon.com/es/rekognition/?blog-cards.sort-by=item.additionalFields.createdDate&blog-cards.sort-order=desc Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:36) – CLIPr operating premise is that not all minutes of video content are equally relevant to everyone. So it uses machine learning to fully index that video and make it fully searchable.(05:02) – Watching a whole video can be inefficient when a participant only wants to watch specific sections. CLIPr team's speeds up and accelerates more efficient automations to be helpful for both consumers and enterprises. (06:42) – The tools that CLIPr provides are a way to guarantee target audience engagement rates to be really informative. CLIPr focuses on this video insight when it comes to engagement and interaction around the video itself in a category called video analysis and management.(08:04) – CLIPr aims to hand out the tools to efficiently find content that matters, bookmark it, share it, react to it, comment on it. (08:27) – The tools and the skills required to edit a video are completely opposite from the skills and tools required for editing inside of a document. CLIPr bridges the two effectively, by building a video-based document type.(11:57) – There has not been as much disruption around video. Some use cases that have been thought out include recording customer meetings; customers’ feedback, integrations with a CRM record, and also, provide a score over time around the actual probability of closing a sale based on the relative perception for the customer reaction.(14:20) – AI, additionally with the hospitals and the medical universities and researchers alike are still using antiquated technology and they're not extracting insights from these video moments. CLIPr is also useful in telemedicine. For surgeons, CLIPr means high value, highly visual, high-impact in a short time.(24:26) – Machine learning, in general, it's all about the data and about engagement and interaction and training new models around the data. So, machine learning allows people to create things and bring solutions. Technology is actually going to find meaningful problems to solve more effectively and more efficiently. (28:21) – The purpose of services is to build businesses and to augment either with the stable technology or the experimental technology for what will be the future of AI, of natural language processing of emotion, detection of different technologies. Additional progress still needs to happen beyond the data in telemedicine, EMRs or courtrooms.(31:49) – As new features get uncovered with specific use cases, anyone can benefit from CLIPr video analytics and management platform. There is continued acceleration for product led growth, closing a 5 million seed round with a strategic partner and keeping focus on machine learning and cloud-based services. Rather than just being an endpoint, it analyzes the data, allows for referential utility, allows for collaboration and allows for monthly recurring revenue.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Dave Bechberger: How Connected Data Impacts Our Daily Interactions   [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDave Berchberger is a Senior Graph Architect at Amazon Web Services (AWS). He is known for his expertise in distributed data architecture being a thought leader in graph databases, and the co-author of Graph Databases in Action by Manning Publications. Dave uses his 20+ yrs experience working on and managing teams delivering full-stack software solutions to take a holistic approach to solve complex data problems.    Episode Links:  Dave Bechberger’s LinkedIn: https://www.linkedin.com/in/davebechberger/ Dave Bechberger’s Twitter: https://twitter.com/bechbd?s=20 Dave Bechberger’s Website: https://www.manning.com/books/graph-databases-in-action?a_aid=bechberger Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:29) – Corporate environments need to be able to help solve certain types of problems that traditional relational databases or other data technologies are not very good at solving. The new approach is to build out high-performance data platforms on top of a mix of technologies, focused around solving them with graphic, graph database technologies. (02:53) – Graphs are the mathematical construct of a graph. It's really about networks, connected data of different people connected to other people or things of that nature. It's about building out networks and using those connections to be able to answer specific types of questions and draw insight and information out of that data that isn't necessarily available from other technologies. (06:49) – Fraud is another canonical use case, because it is all about figuring out connections and patterns within data, to be able to discern whether this activity is fraudulent or not. (08:32) – Other technologies don't do a great job linking together entities in such a way that those links and those connections are also treated as first-class citizens inside that data. Graphs bring those connections in your data up to being “first-class citizens”. (09:29) – With a graph, those connections are brought up and given first class status in the languages and queries that you run. It's called traversing them, to be able to move across them, to be able to drive insight from how those connections are made and how those connections basically connect this network of data together.(12:38) – Using Graphs makes developers able to not only process data in a real-time transactional mode, but being able to use those along with something like graph type analytics, and then use that in conjunction with AI and ML technologies to augment data back into your graph in order to provide a better real-time user experience.(14:32) – Any enterprise build or any consumer service build is really about creating a better, faster and easier to use experience for your customers. Those are really the driving forces behind any kind of business initiative. Graphs is one of those technologies.(16:38) – There's certain types of analytics that can be run on top of graphs that are very helpful to be used as inputs into machine learning algorithms of different types. Some examples show working in a fraud area.(18:20) – Machine learning in general and graphs-based machine learning specifically, is this concept of a graph neural network, which is basically a neural network that instead of taking only vector features as input, it actually takes in a graph itself. So, graphs as an input to be able to create predictive models on the output. It's building a graph of different connected objects inside the algorithm itself as it's training and learning.(20:33) – To really be able to build graph-based stack applications or applications on top of graph databases, you don't necessarily need to have all of that very academic understanding. And being able to condense that down into a system that helps people start to think about problems that way was really the purpose with Graph Databases in Action by Manning Publications.(25:54) – The biggest ways graphs are being adopted today is used in conjunction with other technologies, be those relational databases or document databases or key value stores or whatever other technologies that are out there. (28:18) – Graphs is one of those technologies that is definitely a double-edged sword because you're able to drive insights and you'll be able to see connections between things. People could use those connections in nefarious type ways.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Alex Beard: How to Solve for the Global Education Crisis caused by The Pandemic [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAlex Beard is the Senior Director at Teach For All , and author of the book Natural Born Learners. After starting out as an English teacher in a London comprehensive, He completed an MA at the Institute of Education before joining Teach For All. His book, “Natural Born Learners”, is a user's guide to transforming learning in the twenty-first century, taking readers on a global tour into the future of education, from Silicon Valley to Seoul, Helsinki to Hounslow.   Episode Links:  Alex Beard’s LinkedIn: https://www.linkedin.com/in/alex-beard-08901915/ Alex Beard’s Twitter: https://twitter.com/alexfbeard?s=20 Alex Beard’s Website: https://www.alexbeard.org/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:43) –The methods used to teach would probably be familiar to Socrates two and a half thousand years ago in ancient Greece. Few things have been done differently inside the classroom. The gap between what is possible, and what was currently true in the classroom is at the heart of our education crisis.(03:03) – The pandemic has widened the educational divide. The pandemic has exacerbated the crisis and intensified some of these questions about the future of education.(06:30) – Education must consider access and quality. But with schools shut down, access becomes an infrastructure through the internet and that's a relatively technical solution.(07:38) – If you're not going to school, quality of education is knowledge received sitting in your bedroom via your laptop, which has completely disrupted our idea of what a quality education is.(08:19) – The vast majority of primary and middle school kids are just not equipped with self motivation yet, so quality has to mean something about human to human engagement. Learning, for most people, is better when it's social.(13:40) – Practitioners have had to develop new pedagogies, new ways of learning, how to engage kids through the medium of technology. You need to know how to engage a student.(15:16) – We might be strengthening bonds between teachers and parents, as a result of the pandemic to support early learning, virtually, and that involves engaging parents more actively in supporting their kids to learn.(18:48) – Our intelligence is unlimited, and it's teachers in schools that cultivate that potential. We need to be more explicit about the different roles that teachers play, and set up our system to enable teachers as subject specialists who help kids to do better. (21:12) – Teachers need to be experts in tech, at least to understand how they can use the latest tools to outsource bits of their practice to save themselves time. (30:22) – AI is sort of an adversary to help us enhance our own creativity. The dangers are more connected to the intentions. It all comes down to human choices if you deploy technology and in certain ways undermine the ability of humans to get better at things. Lots of people are designing to enhance the humans in the loop, which is how we should be thinking about it.(36:33) – There are great advances to be made in the deployment of technology in education, but the advances will be made not by trying to improve tech, but by trying to improve what the humans who are doing with tech. Investment in people and not an investment in technology. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ivy Lu: How To Organize Data Science Teams and Data Science Projects for Startups [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSIvy Lu is the head of data science and machine learning at Oxygen. Ivy's onboarding marked the launch of Oxygen’s banking platform. She has bachelor's degree in Geographical Information System from Peking University, a Ph.D in Earth Systems and Geoinformation Science and a Master's degree in Geographic Information Science and Cartography both from George Mason University. Episode Links:  Ivy Lu’s LinkedIn: https://www.linkedin.com/in/ivy9lu/ Ivy Lu’s Twitter: https://twitter.com/oxygenbanking Ivy Lu’s Website: https://www.blog.oxygen.us/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:42) – I joined Capital One as a data scientist after my graduation from George Mason University with a PhD in Geographic Information Science. After I moved to the west coast, I joined Apple. So, at Apple, I work on an anti-fraud team where we fight against all kinds of fraud and abuse within the whole Apple ecosystem to bring trust and safety to the Apple customers. Both experiments helped me prepare for my new challenge at Oxygen as a FinTech company. So, that's my career , how I passed from the traditional banking industry to a large technology company. And now I'm at the spin hat company Oxygen. (04:05) – A collaboration challenge, since you are the only one and only data scientist on the team, basically, you are collaborating with so many different teams and departments: from operations to marketing customer support or product features. So, you need to collaborate with every single one in the different departments and understand their needs, understand their pain. That also comes related to the first challenge. Collaboration comes with prioritization.(06:57) – Data science teams should be positioned as the foundation and the cross team within the whole organization. So for each line of the business, data scientists should have domain knowledge about the problem that they are trying to deal with(09:20) – I collaborate with our fraud team to set up a lot of protections in the core sets. We collaborate with different fraud vendors on how to set up all the parameters, all the controls in place in the fraud vendors for our sign up status. After the sign up flow is pretty under control, I built a preliminary machine learning model for the fraudsters, to detect fraudsters after sign up for the behaviors they show with our card.(14:48) – I see these days, as data scientists it may require different skills than before. Nowadays, maybe, coding skills are not required anymore with such a good tool for data scientists and for machine learning engineers. But, ultimately, I still think the important thing is the study section background on the machine learning algorithm, the deep understanding of the machine learning algorithms. Also what's important is the deep understanding of the problem they're solving.(17:41) – There are two types of team structure. One is like the data science team belongs to one centralized team and then people may wear multiple hats. So, one day you may work on project A, then another day and work on project B, versus another one that is more embedded.(20:33) – We launched a new product called Elements. So we are now offering four tiers of the product, with increasing cashback with different saving APRs, as well as other retail and travel benefits like priority pass, launch access, reimbursements, like digital subscriptions, like Netflix, and the Peloton Digital.  (23:08) – We are going to raise our series B soon and a series B is all about metrics. Whether your company is going to be sustainable, what's your retention, what's your user growth. So a lot of KPIs and the metrics you send show to not only our internal business, but also to work presents for our VC.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Asra Nadeem: How the future of media will be enhanced by generative design [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAsra Nadeem is the Co-Founder of Opus AI, a streaming platform powered by proprietary tech that turns plain text into movies and playable 3D worlds in real-time. She is the first female Pakistani venture capitalist. She has a BA in Economics, and has a Masters in Film/TV/Theater and English Literature from Beaconhouse National University.Please support this podcast by checking out our sponsors:Episode Links:  Asra Nadeem’s LinkedIn: https://www.linkedin.com/in/bretgreenstein/Asra Nadeem’s Twitter: https://twitter.com/AsraNadeem?s=20 Asra Nadeem’s Website: https://opus.ai/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:55) –Nadeem’s background and her thesis: There is not any kind of freedom without financial freedom, and technology is a great enabler for that. (07:13) – Through a platform that grants access to some of the most brilliant minds in the world for free, anyone can learn and interact now.(09:02) – ”Naseeb” revolutionized the traditional marriage arrangements in Pakistan, by allowing younger generations to create connections online and get married. (11:26) – Formal education has mainly three purposes: learning something, networking and better job opportunities. Those three things are available through technology. (13:54) – The Big Names in the tech industry don't request a college degree to work for them, only the skills. It's a different world that is crafting narratives and stories, building stories for the creative industry, and this is a space that's a massive opportunity that has not been tapped into yet.(14:59) – Opus.ai, an engine that takes any literary text and converts it into a movie. So you have a code without having to know how to code. It can be that tool to enable digital natives who may not have any coding experience in order to democratize content creation.(23:29) – The technological progress or the leaps and bounds of automation make generative design come of age. Using AI to boost creativity makes anything possible and accessible.(26:01) – New types of film will be generated and created. And creativity generates, potentially, new jobs. There is no match for human creativity. And this inherent desire to explore new places or explore new worlds, that's something that's very uniquely human and not replicable by a machine. (32:14) – Network effects are built into platforms, who want to get users in front of as many people, because that's how they drive ad revenues or eyeballs. Figure out trends that your product market fit, and then that platform creator fit that's working for you. (38:14) – The current conditions are opportunities to reinvent, to try new technology and to show that you as a human, can be part of a new wave. We're continuing to move forward into a world that could be without code, could be no code, low code. Build your creative muscle.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSteven Shillingford is President and CEO of DeepSee.ai, a Knowledge Process Automation (KPA) platform to mine unstructured data, operationalize AI-powered insights, and automate results into real-time action for the enterprise. He is the creator of the Knowledge Process Automation industry category, delivering AI-powered automation and productivity via easy to deploy, cloud-based business flows for critical business operations in the Capital Markets and Insurance verticals. He has led several startup enterprises, building cloud-scale platforms and helped found a successful cybersecurity platform for big data analytics supporting network surveillance systems for a range of verticals, from intelligence agencies to Fortune 500 companies. Please support this podcast by checking out our sponsors:Episode Links:  Steven Shillingford’s LinkedIn: linkedin.com/in/steve-shillingfordSteven Shillingford’s Website: https://deepsee.ai/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(02:31) – Innovation Cycles used to be about features, but now consumers and enterprises look for innovation around processes(06:53) – Using AI to surface the information that is most useful through a configurable tool bias towards action.(13:52) – NLP to support different tools for different types of business problems inside the enterprise(16:29) – A hybrid approach where people need interaction to lead us to “enhanced accelerated productivity”(22:26) – Reducing processing time to offload a non-human optimized work to the machine, keeping Computers working on behalf of the humans (23:42) – Operationalize data science and the innovation that comes from AI around outcomes to achieve knowledge, reduce cost, mitigate risk and improve customer satisfaction, not only in capital markets or insurance, but across a number of industries(26:53) – A platform that matches unstructured data in different business models, but same processes. Automation of checkpoints by a machine using the Deepsee platform as in capital markets(30:27) – Helping research get faster results. Streamlining paper processes to innovate in new therapeutics, new vaccines, medical supplements and medications, as well as the technology used for blockchain(33:50) – More than document digitization it’s document and data analysis, preserving data provenance across all actions to build trust through transparency and achieve wide-scale adoption.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Oliver Schabenberger: How Data, Analytics, Decisions and Intelligence Are Connected  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSOliver Schabenberger is the Chief Innovation Officer at SingleStore. He is a former academician and seasoned technology executive with more than 25 years of global experience in data management, advanced analytics, and AI. Oliver formerly served as COO and CTO of SAS, where he led the design, development, and go-to market effort of massively scalable analytic tools and solutions and helped organizations become more data-driven. Previously, Oliver led the Analytic Server R&D Division at SAS, with responsibilities for multi-threaded and distributed analytic server architecture, event stream processing, cognitive analytics, deep learning, and artificial intelligence. He has contributed thousands of lines of code to cutting-edge projects at SAS, including, SAS Cloud Analytic Services, the engine behind SAS Viya, the next-generation SAS architecture for the open, unified, simple, and powerful cloud. He has a PHD from Virginia Polytechnic Institute and State UniversityPlease support this podcast by checking out our sponsors:Episode Links:  Oliver Schabenberger’s LinkedIn: https://www.linkedin.com/in/oschabenberger/ Oliver Schabenberger’s Twitter: https://twitter.com/oschabenberger?s=20 Oliver Schabenberger’s Website: https://www.singlestore.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:38) – From forestry to statistics to Software development to advance analytics(04:07) – To understand the data is not only to build a mental model, but a probabilistic model of how the data came about, and once that model is accepted, as a good abstraction, then it is used to ask questions about the world. (05:39) – Many of the assumptions into our established models and established thinking about industries and supply chains had to be questioned because of unforeseen events like the pandemic. Scenario modeling is not just making a prediction, it must also guide the decisions and the need to provide the right abstractions.(07:19) – There is an approach steeped in mathematical statistics and probability theory. And a more computationally-driven approach which shows how computer science, as a discipline, changed its focus from focus on compute, to focus on data.(10:34) – There are transactional systems, analytics systems, machine learning and data science, all somewhat based on existing technology purpose-built for a certain use case, and what we're seeing is the use cases coming together. These worlds need to come together through a data foundation where the workloads can all converge. Silos and empires that need to be connected.(16:15) – The explosion of neural network technology over the last 15 years due to the availability of big compute and cloud computing has allowed to solve much deeper problems, and we need larger amounts of data to train those models. (16:33) – Modern AI, data-driven AI and machine learning applications recognize patterns. Neural networks are trained to detect patterns. The next generation of models might be more contextual or build out from individual component models where humans can interact with the system and understand how it drives its conclusion, and then correct it.(20:35) – We need to empower all of us to work with data and to contribute to driving the world with data and driving the world with models more. We need to be more data literate. But we also need better tooling that allows low-code and no-code contributions (23:28) – The future of data science is decision science. (25:38) – We have technology at our disposal, that makes us “prosumers” who consume and produce at the same time. And data should be the same way. We should be able to produce what we need based on data, not just consume. (28:28) – Innovation is key to success in technology. Innovation is about turning creativity and curiosity into value, and value has to be tied to the core of what we do, core of the business, core of what our customer needs. (30:51) – The elements of building technology: connectivity, automation and culture.(32:43) – Turn the data into decisions and drive the business, and that is SingleStore’s specialty.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBrad Schneider is the Founder and CEO of NoMad Data. He was previously the CEO of Adaptive Management. Throughout his career, Brad has focused on using alternative data to improve decision making and prediction. Brad has been a Portfolio Manager at Tiger Management, and Managing Director at Jericho Capital, a $2bn AUM TMT-focused hedge fund. Prior to Jericho, Brad also worked at Palo Alto Investors as an equity analyst and was a co-founder and head of product development for InfoLenz, a predictive analytics company. Brad holds a Bachelor of Science degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and is a CFA charterholder.  Please support this podcast by checking out our sponsors:-Work Patterns: https://www.workpatterns.com-Imagine Golf: https://www.imaginegolf.com-Art of Manliness: https://www.artofmanliness.com/podcast/-Keep Optimising: https://keepoptimising.comEpisode Links:  Brad Schneider’s LinkedIn: https://www.linkedin.com/in/bradschneider/ Brad Schneider’s Twitter: https://twitter.com/bschneider222?s=20 Brad Schneider’s Website: https://www.nomad-data.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:16) –A tech guy who started in the analytics space and moved to the world of investment, which led him back to the field of data(02:33) – Building software over the years helped him, as the user of data, to more easily interact with that data and find ways to connect the use case to the dataset. (03:57) – NoMad Data's goal is at a high level to be the search engine for these datasets, making it a lot easier for people in the AI space, for researchers, for computer science, for marketers, for strategy professionals, consultants, investors, help them connect those everyday business problems that they have to real datasets.(05:33) – Data that is more frequently purchased include credit transaction data and customs data, which allows to see trade flows (06:48) – Data sets are so powerful, but they're also so broad.Customs data set help to understand a single company on the aspect of one company or region and economic competitive wins and losses for factories. And because they're so broad it's very hard to describe on a webpage what this dataset can be used for.(08:07) – The build vs. buy dilemma: it really depends on your timeline and the availability of the data you need. Even if the data we collected was a hundred percent accurate, it would become very challenging, because we wouldn’t have enough data points to even make a simple linear regression model. So, in a lot of cases, it's better to buy. (10:25) – Getting that data from where it started, whoever is creating it or whoever you're purchasing it from, and getting it somewhere that you can write that first query has historically been a bottleneck. Some services like Snowflake are creating these marketplaces where people are putting the data in a common database format.(12:05) – It's hard to fully automate the data search process today, and the main reason being the data you need, the metadata about the data, doesn't really exist, and the term metadata is used very broadly. Cutting edge NLP and machine learning is used to find similar concepts.(13:47) – The biggest change that the pandemic caused was really the need for data. Buyers are looking at more and more datasets to fill in the holes in their understanding. And because of the increasing number of those holes in their knowledge, there's been an increasing need for data.(15:49) – Searching the area that we're focused on is one of the biggest problems holding back the market. People know they want to see something, they want to be able to calculate some statistics, but they don't really know the data that would provide the requirement to do that.(16:33) – Companies need to be really pinpointed on what they focus on, and because people have a really difficult time finding the right data, finding the best data to address their use case, services like Nomad help unlock this industry, which ultimately means you bring more and more buyers into the market. (19:08) – Many of the companies today haven't given much thought to data as they have for software. The data revolution has already started. And the first step in that was companies looking at their internal data. The next frontier is external data or alternative data. It's these data sets that are coming from outside your four walls, and in a lot of different businesses, it gives you a perspective that you don't have. It gives you a perspective that isn't biased by your own internal processes(21:00) – If you're a company where your brand is extremely important, you’d be more reticent to sell data because there's potential brand risk associated with doing that. We support anonymity on both sides of the market. In Nomad, they can post their data. It's completely anonymous.(22:40) – Nomad has raised $1.6 million and that was led by Bloomberg beta and some other higher profile VCs as well. Some great angels in the data space.(23:51) – As we get out three to five years, awareness of this space and interest in this space is going to explode in orders of magnitude growth on both the number of people selling data and the number of people buying data.(24:40) – If you're a startup, NYC is a wonderful environment to be in. It's also helping a lot, that housing is coming down.It’s attracting more and more people. People that don't want to commute here don't have to anymore. It's going to be a Renaissance for the city.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ashutosh Garg: How To Leverage AI To Recognize And Improve Diversity In Hiring [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAshutosh Garg works with startups across the enterprise stack. He is particularly excited about how machine learning and deep learning are reinventing existing software categories and creating new consumer experiences. Ashutosh has invested in AI-enabled business applications (such as marketing technology and HR technology), data platforms, data center infrastructure, security & privacy, as well as online video. Before joining Foundation Capital in 2008, Ashutosh was the general manager for Microsoft’s online-advertising business and led field marketing for the software businesses. Previously, Ashutosh worked at McKinsey & Company, helping technology companies scale their go-to-market efforts. Earlier in his career, Ashutosh founded TringTring.com, one of the first search engines in Asia, set up Unilever’s Nepal operations, and led the marketing and pre-sales teams at Cadence Design Systems.Ashutosh has a bachelor’s degree from the Indian Institute of Technology (IIT) in New Delhi and an MBA from the Indian Institute of Management at Bangalore, where he received the President’s Gold Medal.Episode Links:  Ashutosh Garg’s LinkedIn: https://www.linkedin.com/in/ashugargvc/ Ashutosh Garg’s Twitter: https://twitter.com/ashugarg?s=20 Ashutosh Garg’s Website: https://foundationcapital.com/member/ashu-garg/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:31) –Eightfold.ai was created in 2016 as a talent intelligence platform that is being used by the leading enterprises across the globe to hire, engage, and retain a diverse workforce.(04:21) – Large enterprises’ number one challenge is people. They are not able to hire fast enough. Enterprises should think about diversity, about their own biases, to understand what talent exists. We added exits to bring the right people on board and that is where data and AI comes into play.(05:43) – We can't keep looking for people who have done the work. We have to look at the people who can do the work, and that is a fundamental shift in the mindset.(09:00) – We need to reach out to the people who may not have had all the privileges that we have and support them. We have to look at people beyond what we perceive for their face color, age.(10:14) – Machines have the ability to forget and ignore. We have our biases because of the lack of knowledge. Knowledge and moving out of biases can really help us solve this problem when hiring candidates.(11:59) – There has to be an audit process to ensure that your algorithms are not going crazy and that they are doing the right thing. Let's use them to help humans do a better job. (13:53) – It's all about humans. These systems are designed to come in and replace humans. In that case, not only are you taking the snitch system correctly, you're teasing that: I really don't need to worry about humans, and that has to be front and center.(16:00) – One of the things Eightfold believes is that it's not that people are good or bad, or one is better or worse, but who is the best fit for which flow in that company.(18:24) – You have to really assess the people at their full potential.(22:32) – What Eightfold.ai is trying to do through machines is help hiring managers understand that candidates past, be able to dig deeper with you, look at the peer group of the community to see what their peer group is doing today.(25:27) – Some of the success stories of the companies that we know today in the world come from combining experience with young talent. (27:26) – The talent market rate landscape is completely going to go through a massive shift in next 18 months. This is also a good time to hire great talent, because many people are looking up.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

#148- Robert Stevenson: Why The Future Hospitality Guest Experience is Mobile [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSRobert Stevenson is the Chief Executive Officer at INTELITY. He is a business and technology executive with 20 years’ of rich experience across a wide array of disciplines. Robert specializes in the productization, strategy and market delivery of new technologies. In addition to undergraduate studies in Design and Computer Science, Robert holds an MBA from the Schulich School of Business at York University and the Kellogg School of Management at Northwestern University, including work at the Hong Kong University of Science & Technology.Episode Links: Robert Stevenson’s LinkedIn: linkedin.com/in/robertstevensonRobert Stevenson’s Twitter: https://twitter.com/intelity?lang=en Robert Stevenson’s Website: https://intelity.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media: – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction.(01:45) – Hospitality Tech has been reluctant to embrace the latest and greatest technologies.(03:28) – INTELITY is a mobile platform being built to modernize the guest experience.(05:36) – INTELITY customer segment and customer ecosystem and market is that 80% who are not major hotel brands.(08:09) – INTELITY has been conceived as a B2B2C.(12:41) – How the pandemic stroke Hospitality industry but leveraged a long-expected change.(13:53) – Mobile experience and automation to improve the market.(14:53) – Using AI and data to drive revenue.(18:31) – Using AI and data to predict customers behavior and offer a better service.(19:59) –Automate the experience to elevate the guest and improve the travel P&L for the hospitality space.(21:17) – The voice space in hospitality has been slow to customize and adapt these tools.(23:54) – Mobile technology has led the way, but major changes will emerge in mobile computing devices.(27:56) – The power of the devices will continue to get stronger, better and more demanded.(28:38) – The trend will be to see new hotel apps rolling out to promote contactless experiences because of COVID.(29:55) – The hospitality industry needs AI and Machine Learning to adapt to customer needs.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How Platforms Leverage The Extended AI Community To Address Misinformation with Claire LeibowiczClaire Leibowicz currently leads the AI and Media Integrity program at the Partnership on AI. She holds a BA in Psychology and Computer Science from Harvard College, and a master’s degree in the Social Science of the Internet from Balliol College, University of Oxford, where she studied as a Clarendon Scholar.Not only tech companies should be involved in creating good, responsible, ethical AI, but also civil society organizations, academic venues, other parts of industry and especially media.AI and media integrity proposes a very simple way to have good, healthy, beneficial information online by using AI systems to do that. Not everyone agrees what type of content should be allowed online. Even humans don't agree about what misinformation is or what content should be shown to people through technology. Some tech companies feel empowered to take comments off platforms. So, not only just to declare a label or more context around people, but really to take a public figure off a platform, which is really an emboldening of platform agency in contributing to who is allowed to speak and who's not.In terms of tactics for misinformation, how people create misinformation, how they spread content, is generally applicable to social media. There's misinformation flowing in WhatsApp groups, in texts, in all these different venues. There is a real movement towards this kind of misinformation that's not just total misrepresentation of an event or a fact, but a slant or a leaning, or a caption that may make a post have a different connotation than it would if it was written by someone else.AI and Media integrity seeks to reach a public that can distinguish credible information from misleading information. Labeling is an interesting, almost in-between option, because it's not limiting speech or saying you can't share this post or saying someone's information shouldn't be seen. It's giving you more context. The idea is to find a middle ground for platforms to seem like they're giving the user control and autonomy, and being able to judge for themselves what's credible. Some people are really skeptical about platforms. Labels might encourage major division in user attitudes between those who think they're important for people to be healthy consumers of content and those who find them biased and partisan and error prone. Automating that label deployment is really complicated. And we don't really know what the best intervention is right now to help bolster credible content consumption. With the de-platforming of Donald Trump, we're living in a new society where we are giving the rights of freedoms to platforms to say, we can get content so that we're providing the best interest for our users without acknowledging whether the users really want that.The platforms have been emboldened, and that has a connotation that we're going to become the arbiters of truth. Those who value free speech and principles might frown upon, since the internet was founded as a venue for democratizing speech and allowing people to speak. There are other solutions that the platforms can take to change how content gets shown beyond just labeling. Platform labels alone are insufficient to address the question of what people trust and why there is this general distrust, in the principle of platforms to self-regulate and for fact-checkers and media companies to offer non-politicized ratings. We need to better design interventions that don't repress people, but really respect the intelligence and autonomy that has raised awareness of looking into a source and media literacy. So holistic, digital literacy, educational interventions to focus community-centric moderation,. And that people in the community rather than the platform itself, are the ones doing the moderation, which might increase trust in how the speech is being labeled and ultimately decided upon.A lot of the policies that platforms have about speech on the platforms have to do with the way in which they cause real world harm. You may have a policy that says we don't label speech, we don't do anything until there's a perception that post might prompt real-world harm. Manipulated media is basically any visual artifact that has been altered in some way by any means, and whereas there's no harm to the public square, there might be harm to other types of political speech or those that are misleading. So when we talk about manipulated media, it's really important to underscore what makes that misleading or problematic. So a lot of people have advocated for AI-based solutions to deal with manipulated media. It's not just how an artifact has been manipulated that matters. It's partially the intent, why it's been manipulated and what it conveys that really matters. Just because something has been manipulated doesn't mean it's inherently misleading or automatically misinformation.But rather, what's the effect of that manipulation. And that's a really hard task for machines to gauge, let alone people. Shownotes Linkshttps://www.linkedin.com/in/claire-leibowicz-17156a65/  https://twitter.com/CLeibowicz   https://www.partnershiponai.org/manipulated-media-detection-requires-more-than-tools-community-insights-on-whats-needed/    https://medium.com/partnership-on-ai/a-field-guide-to-making-ai-art-responsibly-f7f4a5066ee  https://arxiv.org/abs/2011.12758  https://medium.com/swlh/it-matters-how-platforms-label-manipulated-media-here-are-12-principles-designers-should-follow-438b76546078  About HumAIn PodcastThe HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Episode Show Notes: - Eric Daimler is the CEO & Co-Founder of Conexus.com. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage.- Daimler believes the Obama administration made big efforts to bring in more technologists into government for innovation and digital modernization, and is optimistic that sensibility around a digitally native environment will be expressed inside of the Federal Government, and continue to trickle down into states' governments for the benefit of all. - Human failure has come before machines got trained on human failures. Therefore, technologists can't use massive amounts of data on every human problem and expect to come out with mind blowing results. So there's limitations on technology. What can be done is to transform these whole domains of knowledge and map them onto others through a new type of math.-There's a discovery in this domain called category theory. Categorical mathematics, category theory, is really at a level above all those other mathematics that transforms a problem from geometry, into another problem called safe set theory, applying it to databases. The math of category theory changes how we relate to data. This is “the math of the future”.-It's at a higher level of math, a level of abstraction to model the world in which companies operate their business, and make bigger decisions better and faster, reasoning large amounts of data at a higher level to power a whole new change in our environment, as business people, as academics, as citizens. -Daimler suggests three ways to solve data issues: matching data in a unified database, create a silo and then they sell a subscription to data silos and data interoperability math analysis through category theory.-AI definition has been misinterpreted over the years as algorithms that collect data and have machines do stuff, when in reality, AI should be understood as a system that senses plans, acts and learns from the experience. And it senses plans and acts from inputs that are given to it. -Not everyone needs to be a programmer in a basement. People need to be playing a multitude of roles. There's not just a choice between computer science or an English degree. What the current world of tech needs is policy considerations, places to get involved, and a way to focus educational efforts. Automation doesn't mean no human intervention. Societies benefit by that exchange of ideas and communication of values.Shownotes Links: https://www.linkedin.com/in/ericdaimler  https://youtu.be/YP9kodLGvT8  https://youtu.be/jqn4wnSBKuE  https://youtu.be/c92rK_UZaXU  About HumAIn PodcastThe HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How We Can Design Autonomous Systems for Values with Steven UmbrelloShow notes:Steven Umbrello is the Managing Director at the Institute for Ethics and Emerging Technologies with a research focus on responsible innovation and the ethical design methods for emerging technologies. His work focuses on ethics and design thinking around building AI systems, and how policy can shape the future of these autonomous systems. Ethics clarification of what would normally be abstract, philosophical concepts like human values to engineers can be implemented into design requirements. Design has to be approached so that engineering can incorporate human values, which are often abstract, into technological design.The difficulty with AI and with many technologies in a globalized world is that technology can be developed in X, but unfortunately, that technology has cross-cultural, cross-domain, cross-border impacts. So, it's about trying to incorporate different understandings of values from across the globe into a single technology. These are some of the difficulties that designers are facing right now.Technology is not purely deterministic. Nor is society purely constructive and nor is technology purely instrumental. It's just a neutral tool. It doesn't embody any type of values whatsoever. And that really is important, because that means that the decisions that engineers make today, as designers, philosophers, do have a real substantive impact into the future.We can begin to really break down the debate on whether we should ban or not ban autonomous weapon systems. Technological innovations have always played a key role in military operations. And autonomous weapon systems, at least within the last few years are receiving asymmetric attention, both in public and, as well, academic discussions.  Scientists should not apologize for, but show the nuance in debate that level five autonomy in and of itself is not the problematic point of interest, but rather what type of system has this level five autonomy. There's all these assessments. This is the nuance in the debate. For those who are interested in the philosophical foundations of meaningful human control, or even value sensitive design more generally you can find my work on my website and my social media. If people are interested in following the debate on the prohibition of AWS they can watch many of the online multilateral meetings, both hosted by the UN and outside their auspices as they take place. People can check out Human Rights Watch and the Campaign to Stop Killer Robots for news on these events.Show notes Links: https://www.frontiersin.org/articles/10.3389/frobt.2018.00015/full https://www.hrw.org/https://www.stopkillerrobots.org/About HumAIn PodcastThe HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How to Power Enterprises with Intelligent Applications with Jordan Tigani of SingleStoreJordan Tigani is the Chief Product Officer at SingleStore. He was the co-founding engineer on Google BigQuery. He also led engineering teams then product teams at BQ. SingleStore powers Comcast with their streaming analytics to drive proactive care and real-time recommendations for their 300K events per second. Since switching to SingleStore, Nucleus Security converted its first beta account to a paying customer, increased the number of scans Nucleus can process in one hour by 60X, and saw speed improvement of 20X for the slowest queries.  To be more competitive in our new normal, organizations must make real-time data-driven decisions. And to create a better customer experience and better business outcomes, data needs to tell customers and users what is happening right now. With the pandemic accelerating digitization, and new database companies going public (Snowflake) and filing IPOs (Couchbase), the database industry will continue to grow exponentially, with new advanced computing technologies emerging over the next decade. Companies will begin looking for infrastructure that can give real-time analytics -- they can no longer afford to use technology that cannot handle the onslaught of data brought by the pandemic. True Digital in Thailand utilizes SingleStore’s in-the-moment analytics to develop heat maps around geographies with large COVID-19 infection rates to see where people are congregating, pointing out areas to be avoided, and ultimately, flattening the curve of COVID-19. In two weeks’ time, SingleStore built a solution that could perform event stream processing on 500K anonymized location events every second for 30M+ mobile phones. Businesses need to prioritize in-app analytics: This will allow you to influence customer's behaviors within your application or outside of it based on data. Additionally, businesses must utilize a unified database that supports transactions and analytics to deliver greater value to customers and business. Enterprises must access technology that can handle different types of workloads, datasets and modernize infrastructure, and use real-time analytics.Shownotes Links: - https://www.linkedin.com/in/jordantigani - https://twitter.com/jrdntgn  - www.SingleStore.com  - https://www.linkedin.com/company/singlestore/-https://www.singlestore.com/media-hub/releases/research-highlights-spike-in-data-demands-amid-pandemic/  -https://www.singlestore.com/media-hub/releases/businesses-reconsidering-existing-data-platforms/   About HumAIn PodcastThe HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How AI will impact the Future of Jobs and Work with Jeff WaldJeff Wald is an Entrepreneur, Speaker and author of the book “The End Of Jobs: The Rise Of On-demand Workers And Agile Corporations”. Wald has started three technology companies, the most recent, WorkMarket , sold to ADP, is enterprise software that enables companies to organize, manage and pay their freelance workforce. He is also a Board member to other companies with an expertise in audit, governance and cyber security. Robotics, AI and technology as a whole are the key factors in what’s being called the fourth Industrial Revolution. Wald mentions three phases: fear-mongering, where society believes all jobs will be automated, dislocation, when job losses occur, and finally, changes in the way of work and society’s standard of living.New technology doesn't replace existing jobs. Companies, workers and society adjust differently to changes in labor, but eventually, that transition is slow and social and economic dislocations do happen, but not immediately. Plus, from a technology standpoint, there is a need for customer service and a human factor which cannot be disregarded.The pandemic has definitely impacted the labor market, but is a complete guess what the outcome will be in a post pandemic world. Economic growth can be predicted, but only as the economy recovers, real estimations could be made related to unemployment rates.The hard tech jobs are growing even through the pandemic, and they will grow post pandemic. They were growing pre pandemic. The pandemic is not impacting that. But hard human jobs, those that involve human connection are also predicted to grow because computers and AI systems can’t do those jobs. Automation is easily applicable to those jobs that are repetitive, high-volume, task-driven jobs.Remote and flexible work have also been growing due to the pandemic. Companies had been reluctant to change their mindsets, infrastructures, policies and procedures for remote work. But now that they've been forced to do it, there is a great number of people who prefer working under the current work arrangements. Not meaning that workers will never again be at the office, just less often than prior to the pandemic, and more frequently than now, pursuing human interaction. But no prediction is accurate until vaccination can really incide in variants.Everyone needs to become a lifelong learner, constantly upskilling in industries that will continue to grow or rescaling because an industry is at very high risk of automation and displacement,https://www.amazon.com/End-Jobs-Demand-Workers-Corporations/dp/1642934356/https://www.jeffwald.com/Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Future of Augmented Reality and Apple Glasses with Robert ScobleEpisode Show Notes: Robert Scoble works with companies that are implementing Spatial Computing technologiesRobert is a futurist and technology strategist and the author of four books about technology trends, being the first to report on technologies from autonomous vehicles to Siri. Previous positions held by Robert include being a strategist at Microsoft, a futurist at Rackspace, Chief Strategy Officer at Infinite Retina, and the producer and host of a video show about technology at Fast Company.Spatial computing is the next step in the ongoing convergence of the physical and digital worlds. It does everything virtual-reality and augmented-reality apps do: digitize objects that connect via the cloud; allow sensors and motors to react to one another; and digitally represent the real world.Spatial computing will soon bring human-machine and machine-machine interactions to new levels of efficiency in many walks of life, among them industry, health care, transportation and the home. Major companies, including Microsoft and Amazon, are heavily invested in the technology.    Is computer vision about to change everything (and already is) and what should business people do to prepare for the changes that will come in 2022? How will the war between Facebook and Apple go and why we will soon give a LOT more data to these big companies?Call to Action: Learn about spatial computing and how it will roll up all the other AI advantages into a new way of computing.Links: - https://twitter.com/Scobleizer  - https://www.linkedin.com/in/scobleizer/  - https://varjo.com/  About HumAIn PodcastThe HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How Data Scientists Transform the Financial Industry with Geoffrey Horrell from London Stock Exchange GroupEpisode Show Notes: Geoffrey Horrell was the Head of Refinitiv Labs, London and currently the Global Head of Innovation and Labs at the London Stock Exchange Group. This episode unpacks key trends from Refinitiv’s new global research report ‘The Rise of the Data Scientist’. Data scientists are moving across teams and realizing (and scaling) new opportunities for AI across their firms - e.g. NLP. In addition, 75% of firms are now using Deep Learning while 17% of firms rely solely on unstructured data for AI/ML use-cases. Data strategy is set to overtake tech strategy in importance. There is a further evolution of teams and talent across finance - more citizen data scientists in trading, investment teams etc.Data scientists are going from supportive to strategic roles as investments in tech and talent put data strategy in the spotlight. COVID-19 is upsetting ML models where you need to recalibrate and calculate disruptive events. NLP use-cases are going into production and impacting accuracy with more advancements increasing the need for ‘explainability’  Models, data and people are prepared for tomorrow’s unexpected shocks - COVID proved that models need re-calibrating / new data to calculate disruptive events (e.g. data enrichment). Learn More about Refinitiv Labs:https://www.refinitiv.com/mlreport2020 https://www.refinitiv.com/en/labs https://www.refinitiv.com/en/artificial-intelligence-machine-learningAbout HumAIn PodcastThe HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSagi Eliyahu is the co-founder and CEO of Tonkean, a next generation business dashboard that connects the dots between the tools organizations use every day and the insight only teams can provide. With Tonkean, Sagi seeks to help companies of all sizes and types simplify and automate the process of staying updated on the most important details they need to more successfully manage their businesses.Episode Links:  Sagi Eliyahu ’s LinkedIn: https://www.linkedin.com/in/eliyahusagi/ Sagi Eliyahu ’s Twitter: @esbsagiSagi Eliyahu ’s Website: https://tonkean.com/  Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:39) – Tonkean enables more people to use software. And what impact would that have on enterprises and business in everyone's life? That's what we're all about. (02:43) –  We joined from acquisition and grew the team there from a handful of people to over 150 people. Even though we had all those great tools in place and the top CRMs and the top project management tools like most companies, it didn't feel like it helped to force people into those softwares. You look at the CRM, you don't have the information that you need. You look into the project manager system, it's not there. So I tried to hack those systems together, trying to connect them together with the likes of integration platforms.(04:24) –  The biggest moment for me was to realize that business processes are actually not about data, they're about people. But software in enterprise is almost a hundred percent about data. How do you actually go about using technology in a process that is very dynamic, very asynchronous and very human-centric? And the answer is that you didn't actually have anything to do that for you. That sounds like a big enough problem to pursue. So that's when we decided to start Tonkean. (06:01) – We call it the Operating System for business operations. It's really abstract into the complexity of business processes, which are human-centric, highly dynamic, highly complex, simplifying it to non-coders business professionals, operation teams like sales operations, marketing operations, legal operations, and general operations, and so on, to be able to build their own solutions that are across a process, not necessarily creating a new app where you can view and manage data, but actually streamline a process end to end across different systems and across different teams.(09:28) – When the pandemic hit and everyone moved in almost overnight, it was really quick to be fully remote. I always had a remote team or more accurately a team that is, like you said, distributed on both sides of the globe. Everything is more measured, and not because we want to, because we're forced into it. All that coordination and all that work that was not in the spotlight becomes more in the spotlight because we're remote, and because everyone is remote. That definitely pushed a lot of the automation world in a lot of our sort of human-centric processes world to the top of mind, because now you can see how much of the work you actually do every day. And all of us are not necessarily in systems. It's between us people and how much of it is manual.(12:29) – One of the big things we're pushing forward is a concept we came up with, which is people-first process design. It's not even about what technology you have. We also believe that most companies and most people misuse technology in the way that they even structured the processes. (15:11) – If we're not designing the process into their strength, then we're actually replacing one inefficiency with another. And that's kind of where we strive to help operation teams. They know the process, they understand it, provide them with a tool set, with a platform where they can actually create efficiency on top of existing systems and on top of existing behaviors. (16:26) – There's the personalization for the role. What is important for that role? What is important for that team? What are the things that work well and what are the things that are not working well as part of this end-to-end operation? (17:47) – Work is more global. And to get the best case scenario, outcome, you need to actually leverage everyone. And that is something that I feel our platform allows to do, but more of that, the movement of no-code and low- code release, all about enabling more people to create more solutions that are more customized for their own processes, their own team, their own company, versus buying packaged solutions off the shelf.(18:55) –  No Code and Low Code are both playing on the same, call it a wave of future improvement and future next steps off software. So for many reasons they are in the same global area, but at the same time, they're night and day, they're actually very different. Low-code, by definition, is the ability for developers to do more things with less code, but it's a low code because you still need to code. And even if you're not writing scripts, like Python or any other coding language, you still expect that to be a developer mindset and skillset. The low code movement allows you to move faster. So it's basically saying the same people that can code today can code faster.(20:39) –  No-code is about expanding the pie, making the pie bigger of people that can actually build things. So it's, instead of saying, you can do more things faster, it's saying more people can do more things. And why that is important is because if you think about the impact of technology and the growth of technology over decades and over many generations, any duration in software specific to the big leaps do not come from making things faster. Those are linear growths. The big things come from opening the door for different new people that can now code.(22:15) –  With Tonkean, we believe that operation ops people, again, sales ops, legal ops, finance ops, professionals that understand processes really well, they understand what needs to happen and why, and what's important, but they don't know how to code. So they don't even understand how API works or well enough to create mission critical solutions.(22:54) – If you give them low code, it's not very useful for them. They can do toys, they can do small things that create small impact, but they will never be able to build huge complex systems with low code because the gap is not in the speed. The gap is in the knowledge that they come with. With Tonkean, being fully no-code, we focus on those business processes segments and they've created them to be fully no code in the sense that you don't need to be a developer in your mindset.(24:50) – There's always going to be the need for implementers and the need for architects. To be an architect, you would need to still be the technical person in that case, that understands how networks work and how data flows. Tonkean is a bridge between tech and IT, it incorporates engineering with the operation teams. And empower the operation teams and business analysts to implement their own solutions.(27:15) – 95% of all IT and operation teams have already adopted or planning to adopt in the next 12 months a no-code or low-code solution. The need for efficiency in those departments was always there.  What we're seeing now is the movement from personal productivity to operational efficiency.(33:47) – We're focusing mostly on large enterprises these days. So  there's a lot that we're going to add on from that perspective as well. And being able to, like I said, allow people to build true missions, critical processes and things that run for a long time.(35:02) – Get educated on what's out there. There's a lot of great technology that is very complimentary.  There's a lot of noise marketing wise. A lot of things seem or sound the same, but that's because the opportunity is so big. And there's so many things that we took for granted over the years.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSKen Grohe is SVP & Chief Revenue Officer, Taos. Additionally, Ken Grohe has had 3 past jobs including SVP & GM at Barracuda Networks. He got a BS in Business Management from Boston College.Episode Links:  Ken Grohe’s LinkedIn: https://www.linkedin.com/in/leveragegtm/ Ken Grohe’s Twitter: @LeverageSignNow (suspended)Ken Grohe’s Website:https://www.taos.com  Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:27) – WEKA as you probably know, and some of the folks that might be data scientists listening in, they had to strip a wekabite. So it's 10 to the 30th power. That's a good way to future-proof it. It's all you can fit in a file system. A new way to do storage. It's all software, it's all service subscription through the people you're buying from every day. So if you run it through AWS in the cloud or on premises with Hewlett Packard, it's a great way to get things done and solve big problems. What WEKA is, is a modern and limitless parallel file system, that's easy to deploy any scale in the cloud or on premises for the people in the data center, solve big problems.(05:15) –  71% of corporate data goes unused, despite how much money was spent to create this information data. And it's going on use. So that's amazing. So the average sale for us is a petabyte and that's two thirds of the time. It's on premises. One third of the time, it's in the cloud every time to go between the two.(08:12) –  I can certainly think if you're in a university and at the end of the day, you want an AI project and I'll cut to the chase, not just for the greater good, but to recruit great talent. So when you're doing that and you're recruiting that type of talent, you're putting it into action. And that's probably going to be on premises. We allow you to put the right data at the right place at the right time to get, manage your information across the entire life cycle. So you make the money when you need it, and then you don't lose it when you really want to protect it for data protection.(13:09) – Where you live in your hat in a COVID world, doesn't matter. They kept going. When you think about it, traditional Hollywood shut down during the beginning of COVID. Because you couldn't break the unions, you couldn't get the talent, the labor, they, Brad Pitt's Reese Witherspoon's to go on site, you couldn't see, you couldn't create any of the content we watched. The tiger came and things like that. But what I've told students able to do is enable them to create content. The need to have a parallel modern file system with no limits, no compromise. It was so important because you're going to bring all these engineers and all these scientists, you want to make breakthrough discoveries.(16:22) –Some early in the career, 20 something, it says what am I going to do with the rest of my career? I heard AI is great. I'm telling you now the chief data officers are to learn. And as part of it, you may not earn that job right away, but think about, and put this individual's going to be, and typically they've come from the HPC high performance compute environment or the academic environment. So what's happened is a title has risen. It's called chief data opposite. Some of it is compliance and there's certainly a chief compliance officer in there, but more important, more exciting is building out new applications that grab market share and new revenue streams using that.(20:28) – Storage is going to have a Renaissance or is we're living in right now, part of AI. (24:53) – I see three different paradigms. GPU's being prevalent. NBME being everywhere in the network, but especially in the GPU and the server itself.(27:27) – All the intended AI practices and initiatives, it was going to be a fallout that over 50% of them were not going to have ROI. And that's unfortunate. Now that number has shrunk to less than 12% per the analysts we spoke with yesterday. You never want to have strengthened aptitude and intelligence, but you don't have the ability to use it at that time. So the pro file system lets everyone use it all the time. We take care of the locking and the overriding, all the other management is part of it. (29:52) –  You can start as small as you want and go as large as you want, but bring the ability and the imagination to solve big problems. Because storage and more importantly, AI centric accelerated storage from WEKA is certainly huge. And I love I'm going to use an ops shoot of your bottomless. I'm going to call it limitless. So it's kind of the solutions of limitless.(31:01) – You want the right data at the right place at the right time. No in all the cases. So you can capitalize, you can make, go faster and go actually press your advantage. And wherever it might be, whether it be retail or manufacturing. The reason I say extensibility is for naming conventions, whatever file you create, you want that same name and convention whether you're on premises, we on a cloud, we were an object store or whatever. And what's great about WEKA.(33:03) – The fast, eat the slow. If that's the case, the ability to move the correct data, the right naming conventions based on the right policies, the right security allows you to happen. So we're kind of known as the information life cycle management type company. (34:14) – We were involved with vaccine development, obviously with those, most of those vendors did all suppliers do that through the cloud and we have a solution for them in the cloud but ultimately a hybrid solution as well.(39:51) – We're not a very sales dominant culture. We're all about solving big problems and very technical by nature to get into your use cases. In fact, most of our people spend most of the time trying to move data scientists or people represent data scientists. So if you're in that category, we'd love to help you out.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDr. Vasco Pedro is the co-founder and CEO of Unbabel. He owns the vision, overall business strategy and sets the direction for Unbabel’s product development. Responsible for the company’s culture, Vasco is heavily involved in recruiting and spearheads Unbabel’s fundraising efforts, which total USD$91 million in venture capital to date. He is a leading presence in the burgeoning Lisbon startup scene, with Unbabel known for being the first Portuguese company to be accepted into the Y Combinator accelerator program.Vasco received his Ph.D. in Computer Science in May 2009 from Carnegie Mellon University (CMU), working with Jaime Carbonell and Eric Nyberg. His thesis, titled “Federated Ontology Search,” focused on developing new methods using ontologies (a set of concepts which compartmentalizes variables for computations and establishes the relationships between them) in large scale data-processing scenarios. From 2001-2009 he was a Research Assistant at the Language Technologies Institute, contributing in the field of Question Answering (a computer system capable of answering questions posed in natural language), alongside the team that eventually went on to create IBM’s Watson. Vasco was a Fulbright Scholar, 2001-2005, and was awarded a scholarship from Fundação para a Ciência e a Tecnologia, Portuguese Foundation for Science and Technology (FCT), Ph.D. Scholarship, 2006-2010.Episode Links:  Vasco Pedro’s LinkedIn: https://www.linkedin.com/in/vascopedro/ Vasco Pedro’s Twitter: @justvascoVasco Pedro’s Website: https://unbabel.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:30) – We need to create a new version of the translation service that blends artificial intelligence and humans in a number of different varieties to provide just this very simple, straightforward API for translation. That was the original idea. (04:21) –  Companies are pressured earlier to be able to serve multiple markets. And as you expand to multiple markets, you face the fact that people in that market will speak a different language and I need to be able to serve them.(06:49) –  Our goal is to build the language operations platform that enables every enterprise to seamlessly scale across languages. And a big part of that is the full stack that we've built on translation and different components of AI, quality estimation or anonymization, or the actual interfaces for humans to translate and all the different components.(08:43) – AI will have the biggest impact in areas that are highly commoditized and require a lot of human effort. A lot of humans can acquire the knowledge and the skillset to do translation and to do transcription. Overall, AI is not replacing humans, it is augmenting humans. And it's enabling humans to be more productive as a tool, so far.(10:43) –You will need a smaller amount of human effort per unit, but that human effort overall would be more valuable, because it translates into a higher value. I don't see, unless you're talking about very basic repetitive tasks, I see the real value is in this interaction of being able to give the boring task to AI and to let the human do the higher cognitive load function type of tasks. (15:10) – We started by focusing on customer service and the drive behind that was a number of things. One, conversational interaction is particularly suited for enabling AI to have a large impact. There's this sense of almost the inequality of customer service, depending on language.(16:59) – We're still focused on text, chat and email, but in a way that I, as a customer service agent, don't have to really care about the language you're talking. You, as an agent, focus on being an amazing customer service agent and really understanding your product and providing that level of customer service. And we act, we sit in between to make sure that that communication happens at a high quality human level on both ways, both from the customer to the customer service agent and vice versa.(20:07) – Unbabel is a platform and solution for language operations that relies on multiple things. So the portal is really the product that the LangOps use to implement, manage and scale the translation layer. This is powered by the underlying platform, which is the actual bit that does a translation and would set up pipelines. And that's where a lot of the AI and human work combined to provide fast, scalable, robust and high quality translations.(24:13) –  The digital-first world that we're accelerating into, and despite all the very, really bad things that the pandemic brought, that's probably the silver lining in terms of accelerating into the future, highlighting the need for that, for the ability to overcome language challenges. It's very clear that even in Unbabel, which is a company that's focused on eliminating language barriers, everyone that we hire needs to speak English, because otherwise we can't really communicate yet at the level that we do, we need to do. You're now really being able to overcome physical barriers, but still have some sort of pseudo physical presence. And so the glaring barrier becomes language. If your appearance and location are not an issue for communication, then, really, the language that you use becomes the number one barrier for it.(29:34) – Conversational is still going to be, you mentioned the interface, but it's going to expand more beyond text into voice, which was pioneered in the media is going to migrate into a lot of business use cases because we were forced to do it.(31:49) – If you're a consumer, don't settle for bad customer service, just because they don't speak English.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJacqueline Nolis is a Data Science consultant, who helps companies like T-Mobile, Expedia, with their data science problems.She’s got an undergrad in math. Masters in math. She got a doctorate in industrial engineering and then started working as a consultant. For the last ten years she’s been doing data science consulting for all sorts of companies and leading data science teams.Emily Robinson studied very related fields of statistics. And that's where she started programming in R, went on from there to get a Master's in organizational behavior and then did Metis, which is another data science bootcamp.Went on to Etsy DataCamp. And now she is a senior data scientist at Warby Parker. She got interested in data science because quantitative social sciences are a very good background to lead into data science.Episode Links:  Jacqueline Nolis' LinkedIn: https://www.linkedin.com/in/jnolis/ Emily Robinson’s LinkedIn: https://www.linkedin.com/in/robinsones/ Emily Robinson’s Twitter: @robinson_esJacqueline Nolis' Twitter: @skyetetraEmily Robinson’s Website: https://hookedondata.org/ Jacqueline Nolis' Website: https://jnolis.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(04:08) – There's just, clearly, some desire in the world that people are data scientists, or if you're a junior data scientist, a desire in the world to be one of these senior data scientists, giving talks at conferences and joining the community. And so we just noticed organically that this is happening more than us making some grand observation about the state of the world.  You bring up  the current moment also recognizing, how May I become even more valuable to employers? I may end up having to do a job search. What can I do to prepare so that I can be an attractive candidate to different companies? (06:23) – The book was put up into four parts, and the first part is, basically, what is data science? What does it look like at different companies? How do you find jobs? What does the interview process look like all the way up to negotiating an offer? So that's the first half. The second half of the book, and the third part is around settling into your job. Putting a machine learning model into production. And dealing with stakeholders. And then, finally, the last half is about when you start settling in it's about continuing to grow by joining the community, handling failure, which is pretty much inevitable when you're a data scientist going on to a new job. And then the final chapter is what are the things you can do even after you become a senior data scientist. So Management, independent consulting or being a principal data scientist. Finally, actually we have an interview appendix with over 30 interview questions, example answers.(08:51) – No one really knows what's happening. No one, or for the last two months, no one really knows what happened. No one knows what's going to happen for a while. That we're just in a really uncertain time. We don't know if your company is going to be around in six months, everything's more uncertain.(09:57) –A lot of companies are putting on hiring freezes in general, except for very critical roles. (12:18) – Each one of those stakeholders has a different goal, whether it's to make their engineering stronger, to make better decisions, to make their company go to a better place in the long term. And how you work with each one of these groups of people really will differ based on who they are and what their goals are. So we break down that a lot. (15:40) –  Some of the key communication strategies include messing up a lot until you remember how you messed up the last time, and then get a little bit better. And you do that for 10 or 20 years. And eventually you're okay. Being consistent. Creating a consistent framework for how you share things. You have to adapt your strategies.(18:01) – The idea of how you prioritize this work thinking through a lot of the prioritization and deciding what work to do when that's really important to good stakeholder management.(19:43) – Failure can come in all shapes and sizes. For me, I find one of the most difficult types of failure is that when you're a data scientist, you generally have to get people excited about a project before it starts. You have funding from people, and then you start working with the data. And it turns out that data doesn't have a signal in it. If you can't find it with a simple model, you're never going to find it. And that's a really big source of failure in the data science field. (20:54) – So it's also worth thinking about, as a team, maybe not taking on only pie in the sky, very high risks, new cutting edge projects and balancing that with things that you're more confident you can deliver because that can help show people the value of the team. And then, hopefully occasionally, one of those riskier projects does pay off and it will probably pay off in a bigger way.(22:38) – A lot of the work you need to do to handle a failure really starts long before the failure actually occurred. Companies do have different cultures around failure, and at some places it's not seen as valuable, you might be punished for it.Try to understand if that company has a culture of learning and ongoing feedback, because you do want to be at a place where it can be safe and understood that sometimes things do fail. Startups are more comfortable with failing fast and frequently because startups are lean and exciting.(27:40) – These softwares to monitor their employee's computers, which will take screenshots every 10 minutes, it hugely invades privacy. You should know what outcomes you're striving for. What success looks like there, trust your team to do the work well, to give them the flexibility. We're not just working remotely, we're working remotely in a pandemic. And having that human understanding that people are going through different stuff.(35:57) – I am a big component, a proponent of doing public work. In my free time, I've picked up art. So I've been doing a lot of watercolor and oil pastel, and it's been nice to just have something that is totally not tech to put a little bit of my heart into.(43:05) – At the current moment, it's certainly riskier to leave without another job lined up. You could just ditch the system entirely and become a consultant and work as a freelancer, which is what I've been doing, which can have a huge payout and huge opportunity, but also is incredibly stressful, very risky, and just almost impossible to do right now, given the virus. I really do not care for giant tech companies to come out with giant technology and we're supposed to be excited about it. I find that inaccessible. I really love seeing new projects, new things people are doing. But what I get very excited about, too, is when folks start sharing their side projects or blogs, or sharing some of their work, it's cool. There's more to be done with other groups including people of color, but I've also seen some meetup groups and other efforts for that. So that's what's exciting to me. (53:59) – My call to action is to try to find a way to help people. That's why we wrote the book. It was certainly not so we could get fabulously wealthy and retire early. Don't take conventional wisdom and assume because someone told you it has to be true, including us. Challenge conventional wisdom a little bit.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBabak Hodjat is Vice President of Evolutionary AI at Cognizant, and former co-founder and CEO of Sentient. He is responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world's first AI-driven hedge fund, Sentient Investment Management. He is a serial entrepreneur, having started a number of Silicon Valley companies as main inventor and technologist.Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. He was also co-founder, CTO and board member of Dejima Inc. Babak is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri.He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas. Babak holds a Ph.D. in machine intelligence from Kyushu University, in Fukuoka, Japan.Episode Links:  Babak Hodjat's LinkedIn: https://www.linkedin.com/in/babakhodjat/ Babak Hodjat's Twitter: @babakatwork Babak Hodjat’s Website: https://digitally.cognizant.com/author/babak-hodjat Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:34) – Machine learning and AI based algorithms are being used to get a sense of what is happening in an organization, abstracting out patterns, and then to be able to actually forecast and make predictions into the future. We need to have our AI systems help us with the decision-making itself.(03:59) – Humans are really good at general intelligence. We know a lot of things about a lot of things. So often that state-of-the-art in AI can not capture things like common sense. The frequency of making decisions is slow enough that we can have a human in the loop.Today, it does still make sense to have a human in the loop. There are cases where we have to rely on our AI systems to make autonomous decisions for us. (06:54) – We can build models that are specialized in assessing certainty in our AI systems and the way they do that is based on familiarity on the input side, the context side and familiarity on the output side.(09:57) – Our systems have to be able to tell us how much we can rely on them. You need confidence in what the AI system is telling you to do, but then there is risk sort of projecting that confidence out. Past performance is no indication of future returns.(11:36) – Companies and enterprises have definitely reprioritized things. They have maintained, or even in some cases increased their investments in AI enablement, which says a lot about the value that people ascribe to AI based systems. It is a natural next step to digitizing your business.(14:45) –Evolutionary AI is a set of tools that we use to build AI systems and AI enabled companies. The reason why it's called evolutionary AI beyond the fact that it's an evolution in the way people should think about AI, is that a very strong core component of it is evolutionary computation. We do pull from other AI disciplines as well, such as deep learning and neural networks and so forth, but the essence, the main differentiation here is the fact that we have an element of what I call creativity that is missing in a lot of AI systems. 15:29) – We're able to search for solutions much more efficiently than we are with your typical machine learning based systems. And that speed and efficiency allows us to be much more creative and find solutions that are either very difficult to arrive at using other methods or impossible to arrive at. So it also gives us a number of very interesting capabilities. (20:09) –  What evolutionary AI allows us to do is to actually use machine learning to create what we call a surrogate for the real world. That surrogate is learned off of data that we've seen up until now.(20:47) – This is the principle of what we call evolutionary surrogate assisted prescriptions, where you have a predictor, which is the surrogate for the real world. You have a prescriptive that you evolve, which gives you a decision strategy. And often you pair that with a certainty model. So when the three of these come together, you have all the elements of a good decision augmentation system, where a human decision maker, let's say a policy maker would ask the AI, how can I achieve this balance of cost and containment.(24:46) –  Optimization is where we need to be. And that is what decision-making is about. We are constantly optimizing and trying to improve on goals and outcomes. (29:25) –  There's a lot of work around new architecture, search and evolving, basically the design and hyper parameters of any kind of deep learning based system.(36:57) – More and more companies are going to adopt this technology for decision-making and it will start with areas where the decision-making has been captured. So the data around the decision-making is already there, but it will not stay there. It will get to areas where we think decision-making is the soul.(38:20) – If you are in an organization or enterprise where there's critical decision-making happening, work back from there. You have to have a vision of AI enablement in order to even get the data and digital part of what you do. Build your Data infrastructure, modernize it, report on top of that, build your machine learning and forecasting and predictions on top of that.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAlberto Todeschini is a Faculty director, consultant and lecturer in artificial intelligence. He has supervised over 150 projects covering a wide variety of industries and techniques, with a special focus on sustainability in energy and water. He also works with the University of California, Berkeley, GetSmarter, and aivancity. Episode Links: Alberto Todeschini's LinkedIn: https://www.linkedin.com/in/atodeschini/ Alberto Todeschini's Twitter:Alberto Todeschini's Website: https://www.ischool.berkeley.edu/people/alberto-todeschini Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos  Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch  – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:49) – It has been interesting because in the last few years, a lot of this is about the environment, about energy and about agriculture having been penetrated by data science. I'm pretty optimistic actually, coming out of this big dark cloud. First half of 2022 will be some good news. (03:56) – Newer energy technologies have been around for a while, but they really have become mainstream recently, such as wind and solar. They are intrinsically data-driven. So you need to squeeze every last percent of energy out of this massively capital intensive works.(06:22) – With COVID, we've been forced essentially to experiment. We will see more experimentation around the livable cities for instance. There's a lot of appetite for resilience, for community resilience, maybe at the city level, but also at the regional level and national level.(09:00) – We've seen the investment moving elsewhere to renewable, which is certainly more future proof. if you talk to the epidemiologists, they'll say, well, there will be another pandemic. As a matter of fact, it could be a lot deadlier. So it will be nice to have this distributed way of storing large amounts of essential items.(12:40) – 5G enables this distributed system and the ability to communicate incredibly quickly and also to do, technically speaking, inference on the edge.(17:08) - The market in Europe is pretty fragmented. Partially that has to do with language. So, pretty much most European countries would speak reasonable English, but that's not absolutely not true for the entire population. One of the things that maybe has changed with COVID is the sense of locality.(20:25) – There's a huge amount of work that needs to be done postmortem, in the real meaning of the term, to understand what went wrong with the data collection. So that next time, collect it better. What went wrong with communication between health authorities and political authorities and the general population.(24:49) –  Cultivated areas are very interesting because agriculture consumes the majority of fresh workers and about half of agriculture. Currently it is not sustainable. Purely from the point of view of water. And we're not talking about deforestation, we're not talking about runoff of chemicals into the ocean, purely just the water.7:05) – Some of the main carbon capture technology is very water-intensive. As we increase both the data collection, as well as the predictions, which are two of the main things that we can do with machine learning, we can just use water better.(28:45) –  These companies that are, from day one, data-driven companies, are all thriving and they're becoming ever more unmatchable.(38:45) – Let’s use technology to figure out how to improve life in the city or make places where we enjoy walking. We like walking, and we enjoy local restaurants. We enjoy going out. We like biking around the same city, livable cities. So maybe that is something we can think about and work towards.(41:31) –  It's been awful. It still is awful, but I'm optimistic. Look around your neighborhood and think of things that you want to stay with us. We've been given a great opportunity to reset a lot of our habits.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSStan Vlasimsky helps companies envision and navigate complex transformations leveraging technology to achieve business outcomes. He is currently a Senior Vice President at Pariveda Solutions focused on digital transformation and helping clients navigate change with a particular focus on innovation, operating with a product mindset, organizational health and leveraging emerging technologies.Formerly, Stan was a senior executive at Accenture, where he spent 25 years working across the Americas, Europe, and Asia focused on large scale global change initiatives, operational excellence, and technology modernization. He has had the privilege to serve some of the leading companies in the world, including Toyota, Walmart, ExxonMobil, ChevronTexaco, and AmerisourceBergen amongst others.Episode Links:  Stan Vlasimsky’s LinkedIn: https://www.linkedin.com/in/stanvlasimsky/ Stan Vlasimsky’s Twitter: @Pariveda_IncStan Vlasimsky’s Website: https://www.parivedasolutions.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:30) – We were moving to a more virtual world and we have been for a while, but then all of a sudden over a span of a few weeks  everything was accelerated. How do you make teams effective and motivated where we're used to walking around having team lunches, mentoring, and recognition and all those things? How do we make human relationships?(03:13) – We've been experimenting with how you scan things, all the collaboration tools we use with our clients we're now having to use with our employees from a career growth perspective.(06:19) – The most complex algorithm that exists is the human brain and how humans interact with each other, and that's the most challenging thing.(09:22) – Leveraging both what we do internally and expanding that out into the broader ecosystem condition to other third parties as well. We're doing five or 10 years in five or 10 months. (10:57) – We have been changed forever to some extent and we'll have to deal with that new normal and much of that is positive and some it's going to require some more work.(18:19) – Productivity measured in output of the consulting work we do or to clients has actually gone up.  As we've reduced some of the friction cost of commuting and all those things that happen and then there's an element of, even though we're a very employee friendly company, everybody has seen people in their ecosystem be impacted, furloughed, laid off, whatever. So,  there's an element of the Hawthorne effect, which is ultimately when people believe they're being measured, their productivity changes or generally improves.(22:22) –  There's going to be  a lot about people and a rethinking of what the models are for things such as restaurants or retail and malls and all the things are going to be similarly impacted as people try to figure out they need a certain density of customers. (25:58) – This is going to test every organization, every leader, agility and product and all those are digital, all of the words that we like to use right now, but it's real at this point in time either you figure it out or you don't survive.(28:32) – Contactless payments helps perhaps with restaurants. The core of this is being able to simplify payment transactions.(32:14) – It can all be underpinned by ultimately automation, those processes that have traditionally been more manual, but pushed in more traditional ways through different organizations and such again, going back to as things get more digital, that's going to happen. It'd be accelerated because there's so much more data to deal with and every day there's so much more data.  Again, it's never going to add to now the worry is what do you do with the data and what do you do with Intelligent data, because there's no longer a lack of data. It's because I got too much data. (35:37) – We're going to be in some sort of hybrid world and  your comments about European flare brands, recognizing what the consumer wants is going to be even more important than it ever was and you're going to have to morph to a hybrid so rather than saying the strong sales experience, people value product expertise, so rather than saying, then having somebody, this is the sales person, this is a person that can help you pick the product.(38:48) – How do you continue to build those communication skills in a world that is remote? For others of us, it's going to be about empathy.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Listen in to this episode as David Yakobovitch shares his thoughts on why open source vulnerabilities are increasing.Available for reading on Medium.🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.About HumAIn Podcast:The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDr. Kari L. Jordan is the Executive Director of The Carpentries. Kari has been on the Core Team of The Carpentries since 2016. Before becoming Executive Director, Kari served as the Acting Executive Director. She has expertise in engineering education, diversity & inclusion, and leadership. In addition to her work as the Acting Executive Director, Kari held the role of Senior Director of Equity and Assessment where she guided The Carpentries through development of an Equity, Inclusion, and Accessibility Roadmap and was the liaison to the Code of Conduct Committee. Before this, Kari was the Director of Assessment and Community Equity where she streamlined The Carpentries assessment strategy and expanded their mentoring program.Episode Links:  Kari Jordan’s LinkedIn: https://www.linkedin.com/in/kariljordan/ Kari Jordan’s Twitter: @DrKariLJordanKari Jordan’s Website: https://www.carpentries.org/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:38) –  I hadn't heard of open source until I started working with The Carpentries and more specifically data carpentry.(04:11) –  We're all over the place and we work remotely full-time, so the shift that we've seen over the past couple of months from a teamwork perspective has not changed, but in the way we deliver our workshops has totally changed. We’ve moving our workshops online, making sure that the quality in our brand stays the same.(08:39) – We received quite a substantial amount of support from both the Moore Foundation and the Chan-Zuckerberg Initiative and this funding will help us scale our instructor training program.(12:02) – I had no idea what open source was, but now I can advocate for it and we can offer opportunities for workshops you may not get in a university, but what does that mean for a degree program? or how can I justify paying or having someone pay for a four-year degree to learn open source or learn to reproduce or all of these things when they can come to a The Carpentries shop? It's a very interesting conversation about the curriculum and who owns it and how it’s shared. (14:50) – The growth in open source has to do with problem solving and it comes from the desire to want to solve problems in your own community or want to solve problems that you see things that have been problems for such a very long time that they have not been solved. This is why I talk so much about not only diversity, but inclusion. Bringing people together of all backgrounds and giving them the space to contribute what they have, because every contribution truly does matter.(19:48) –  There is no wrong way to get involved. There are many ways we can get involved with open source.(21:39) – There are hundreds of organizations dedicated to allocating resources, to providing opportunities for people to get involved with data and coding and it's not the responsibility of one organization to do all the work, The Carpentries I feel like our zone of genius really is that training teaching data skills training that type of pedagogy. It's really important for this opportunity for access and just sharing what we do is so important.(24:32) – What do you want your participants to walk away with? That's extremely important to the carbon truth. We don't want anyone leaving our workshop feeling worse than when they came in or feeling they’re never going to learn this. It's more so about that self confidence piece that belonging to a community that's what it's all about, and eventually you're going to learn some code, you're going to learn how to code. (28:19) – There's no wrong way, and I very much appreciate the industry acknowledging a four year degree may not be the answer for everything. There are things that I've definitely learned in college, but the industry is noticing that you can pick up skills along the way, you can take a two day course, you can take a month long seminar and be just as effective in your role and learn just as much. So it's all about pathways. (30:22) – You don't have to be proficient in any of the programs to be a maintainer, you have to be patient and know how to be organized and how to facilitate conversation around the lesson.(34:46) – If you ever thought that you could never code, you thought wrong. I have been in your shoes, I shied away from programming for a very long time and now I'm the executive director of a nonprofit that teaches foundational coding and data science skills. There is nothing to be afraid of because there is a community in The Carpentries that values you, that appreciates your contribution and that appreciates your perspective. I want you to visit Carpentries.org, check out the opportunities that we have for mentoring see if there's a workshop, all of our workshops are online right now, actually so this is actually a great opportunity and great time for you to get involvedAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSBen Byford has been a freelance web designer since 2009, and he is now mostly a freelance AI / ML teacher, speaker and ethicist and tinkerer – in his spare time he makes computer games. Ben has worked on large scale projects as a web designer with companies such as Virgin.com, medium scale projects with clients including BFI, CEH, Virgin and Virgin unite, as well as having created a myriad of sites for smaller businesses, startups and creatives' portfolios.He’s mostly been a design and front-end guy, with extensive knowledge of other tech and development languages and has previously worked as a mediator between dev teams and clients. His public speaking and lecturing blends his insights within AI and ethics, web technologies, and entrepreneurship; focusing on the usage of technology as a tool for innovation and creativity. He hosts the Machine Ethics Podcast, which consists of interviews with academics, writers, technologists and business people on the theme of AI and autonomy.He also talks about Machine Ethics.Episode Links:  Ben Byford’s LinkedIn: https://www.linkedin.com/in/ben-byford/ Ben Byford’s Twitter: @benbyfordBen Byford’s Website: https://www.benbyford.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:24) – The big question and a moral quandary which we're battling with is how much information do we give to organizations, governments, about our movements - and that's always been the case - but we're now having to think differently in the face of a pandemic, about  how much we can give away, and what kinds of things can be done with that data.(03:31) –  You're really concerned with whom you're giving that data to. And can they be transparent about how they're using that data, and have that data secure, and be able to delete that data when appropriate. And it's very hard to actually believe or have trust in organizations when they say these things. it's a good thing to be doing, but the trust issue is a big one. (06:51) –  Whether Americans have a similar legislation put in place is, in my opinion, irrelevant. Because the internet is cross boundary, cross continental. So, if you deal with anyone outside of your own jurisdiction, your own country, then you will fall into someone else's legislation. And it just so happens that GDPR is one of the most robust that we have at the moment, to do with data.(09:19) – We should be teaching people to reflect on the situation within our educational institutions, so that we are priming people who are going to be making this stuff in the future, to be making design decisions and technical decisions that they can implement it in full respect of other people, and for the respect of the environment. (12:20) – We should all be worried about security, as citizens and our data privacy as citizens, because we don't necessarily want to tell everyone what we're talking about, and that comes into our discrimination issue. So, you can be discriminated against in different countries, for all sorts of different things. And you might not want to tell your neighbor or your government certain things about your person, because those things aren't deemed in that country normal or acceptable or legal. (13:22) – There are many reasons why you would want to keep your privacy and your security intact.You're using a utility, and the utility doesn't respect the user. We're saying water and electricity is a general need, a civil need, I think the internet is certainly up there as a civil need.(17:40) – As you're building technology, you have to require consent under GDPR. You have to stipulate usage under GDPR, and you have to give terms of access under GDPR. So, if you are to be amended or deleted for your delayed data or have your data shared to the user, what specific data they have on them. All that has to be implemented. And if you don't implement that, then you could be taken to court and sued for a lot of money. Now it's illegal to be doing some of that stuff, but within the ethics of AI and the ethics of technology and kind of the ethics of mass automation, we have to really go beyond what is under GDPR, beyond what is legal, illegal and think about again, what is it the world we're we're making?  What is equitable to most people? What is useful to people and what isn't just useful to shareholders. (22:34) – Face tracking stuff is great. It's a microcosm of what is essentially a really big ethical quandary, which has positive and negative effects. So it is really interesting and really frightening in the same way. You have to create trust. And if it is known that these machines are very good and work very well, and the information maybe doesn't really leave the robot in any meaningful way, or is anonymized in all aspects and isn't actually restricting the citizens mobility we've built something that actually really does work and works enough, and knowing when it works enough is an ethical question. And then also, allowing humans to be in the loop somewhere.(26:53) – The obvious contradiction here is that the Chinese system seems to be very heavy handed in its use of technology to implement those social norms. We don't really have a similar approach, I don't think, in the West.(34:02) – You have all these really good applications, all these really interesting applications. And then you have applications which then restrict people's rights or human rights. And again, it might be that we have to look at what human rights actually mean in the digital world.(38:04) – We want to live in a world where George Floyd or anyone who is discriminated against traditionally in a society can walk up to a police officer, can walk up to a person of power in that society and know that they are going to be trustful, trustworthy, wherever your trust in any situation, you don't want to be in a situation where you are in grave danger and you can't trust your own environment.(39:11) –  There has been a wealth of interest in ethics and technology and, in Data Science and Machine Learning, and AI has just been an explosion. I'm seeing that with the emergence of quite a few workshops and talks and conversations around AI, responsibility, transparency, and diversity and equity and all those sorts of terms. Into the future, I am most interested in how the interaction of moral agencies appears in technologies that we actually use and within society's reaction to it. (44:19) – Be mindful. We all have our autonomy and we all should be thinking about the things that we are doing, and you should be empowered to think about what you are doing. It's easy for me to say it on this podcast, but please be mindful of how you affect the world. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy