All Episodes
Data Science at Home — 316 episodes
AI is punishing game developers (Ep. 312)
EU AI Act. What is this thing? (Part 2) (Ep. 311)
EU AI Act. What is this thing? (Part 1) (Ep. 310)
AI is the Concorde of our time (Ep. 309)
The propaganda algorithm (Ep. 308)
AI tips & tricks (Ep. 307)
AI and videogames: Conversational NPCs (Ep. 306)
AI and videogames (Ep. 305)
Europe, wake up! You Can't Be a Superpower on Someone Else's Servers (Ep. 304)
Social media is an ant mill (Internet is a disaster) (Ep. 303)
About Apple's Privacy (Ep. 302)
Productivity is the new data breach (Ep. 301)
Programmable Money: The Cage They'll Call Convenience (Ep. 300)
There Is No AI. There's a Stateless Function on 10,000 GPUs Pretending to Know You (Ep. 299)
Your Favorite AI Startup is Probably Bullshit (Ep. 298) [RB]
Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 297) [RB]
AGI: The Dream We Should Never Reach (Ep. 296)
When Data Stops Being Code and Starts Being Conversation (Ep. 297)
Your AI Strategy is Burning Money: Here's How to Fix It (Ep.295)
From Tokens to Vectors: The Efficiency Hack That Could Save AI (Ep. 294)
Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 293)
The Scientists Growing Living Computers in Swiss Labs (Ep. 292)
When AI Hears Thunder But Misses the Fear (Ep. 291)
Why VCs Are Funding $100M Remote Control Toys (Ep. 290)
How Hacker Culture Died (Ep. 289)
Robots Suck (But It’s Not Their Fault) (Ep. 288)
Your Favorite AI Startup is Probably Bullshit (Ep. 287)
Tech's Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything (Ep. 286) [RB]
Brains in the Machine: The Rise of Neuromorphic Computing (Ep. 285)
DSH/Warcoded - AI in the Invisible Battlespace (Ep. 284)
DSH/Warcoded Swarming the Battlefield (Ep. 283)
DSH/Warcoded Kill Chains and Algorithmic Warfare – Autonomy in Targeting and Engagement (Ep. 282)
DSH/Warcoded: Eyes and Ears of the Machine – AI Reconnaissance and Surveillance (Ep. 281)
AI Agents with Atomic Agents 🚀 with Kenny Vaneetvelde (Ep. 280)
Run massive models on crappy machines (Ep. 279)
WeightWatcher: The AI Detective for LLMs (DeepSeek & OpenAI included) (Ep. 278)
Tech's Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything (Ep. 277)
Scaling Smart: AI, Data, and Building Future-Ready Enterprises with Josh Miramant (Ep. 276)
Autonomous Weapons and AI Warfare (Ep. 275)
8 Proven Strategies to Scale Your AI Systems Like OpenAI! 🚀 (Ep. 274)
Humans vs. Bots: Are You Talking to a Machine Right Now? (Ep. 273)
AI bubble, Sam Altman’s Manifesto and other fairy tales for billionaires (Ep. 272)
AI vs. The Planet: The Energy Crisis Behind the Chatbot Boom (Ep. 271)
Love, Loss, and Algorithms: The Dangerous Realism of AI (Ep. 270)
VC Advice Exposed: When Investors Don’t Know What They Want (Ep. 269)
AI Says It Can Compress Better Than FLAC?! Hold My Entropy 🍿 (Ep. 268)
What Big Tech Isn’t Telling You About AI (Ep. 267)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 266)
Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 265) [RB]
AI and Video Game Development: Navigating the Future Frontier (Ep. 264) [RB]
LLMs: Totally Not Making Stuff Up (they promise) (Ep. 263)
AI: The Bubble That Might Pop—What’s Next? (Ep. 262)
Data Guardians: How Enterprises Can Master Privacy with MetaRouter (Ep. 261)
Low-Code Magic: Can It Transform Analytics? (Ep. 260)
Do you really know how GPUs work? (Ep. 259)
Harnessing AI for Cybersecurity: Expert Tips from QFunction (Ep. 258)
Rust in the Cosmos Part 4: What happens in space? (Ep. 257)
Rust in the Cosmos Part 3: Embedded programming for space (Ep. 256)
Rust in the Cosmos Part 2: testing software in space (Ep. 255)
Rust in the Cosmos Part 1: Decoding Communication (Ep. 254)
AI and Video Game Development: Navigating the Future Frontier (Ep. 253)
Kaggle Kommando's Data Disco: Laughing our Way Through AI Trends (Ep. 252)
Revolutionizing Robotics: Embracing Low-Code Solutions (Ep. 251)
Is SQream the fastest big data platform? (Ep. 250)
OpenAI CEO Shake-up: Decoding December 2023 (Ep. 249)
Careers, Skills, and the Evolution of AI (Ep. 248)
Open Source Revolution: AI’s Redemption in Data Science (Ep. 247)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246)
Debunking AGI Hype and Embracing Reality [RB] (Ep. 245)
Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244)
The AI Chip Chat 🤖💻 (Ep. 243)
Rolling the Dice: Engineering in an Uncertain World (Ep. 242)
How Language Models Are the Ultimate Database(Ep. 241)
Elon is right this time: Rust is the language of AI (Ep. 240)
Attacking LLMs for fun and profit (Ep. 239)
Unlocking Language Models: The Power of Prompt Engineering (Ep. 238)
Erosion of Software Architecture Quality in the Age of AI Code Generation (Ep. 237)
The new dimension of AI: Vector Databases (Ep. 236)
Building Self Serve Business Intelligence With AI and LLMs at Zenlytic (Ep. 235)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner (Ep. 234)
Debunking AGI Hype and Embracing Reality (Ep. 233)
Full steam ahead! Unraveling Forward-Forward Neural Networks (Ep. 232)
The LLM Battle Begins: Google Bard vs ChatGPT (Ep. 231)
Unleashing the Force: Blending Neural Networks and Physics for Epic Predictions (Ep. 230)
AI’s Impact on Software Engineering: Killing Old Principles? [RB] (Ep. 229)
Warning! Mathematical Mayhem Ahead: Demystifying Liquid Time-Constant Networks (Ep. 228)
Efficiently Retraining Language Models: How to Level Up Without Breaking the Bank (Ep. 227)
Revolutionize Your AI Game: How Running Large Language Models Locally Gives You an Unfair Advantage Over Big Tech Giants (Ep. 226)
Rust: A Journey to High-Performance and Confidence in Code at Amethix Technologies (Ep. 225)
The Power of Graph Neural Networks: Understanding the Future of AI - Part 2/2 (Ep.224)
The Power of Graph Neural Networks: Understanding the Future of AI - Part 1/2 (Ep.223)
Leveling Up AI: Reinforcement Learning with Human Feedback (Ep. 222)
The promise and pitfalls of GPT-4 (Ep. 221)
AI’s Impact on Software Engineering: Killing Old Principles? (Ep. 220)
Edge AI applications for military and space [RB] (Ep. 219)
Prove It Without Revealing It: Exploring the Power of Zero-Knowledge Proofs in Data Science (Ep. 218)
Deep learning vs tabular models (Ep. 217)
[RB] Online learning is better than batch, right? Wrong! (Ep. 216)
Chatting with ChatGPT: Pros and Cons of Advanced Language AI (Ep. 215)
Accelerating Perception Development with Synthetic Data (Ep. 214)
Edge AI applications for military and space [RB] (Ep. 213)
From image to 3D model (Ep. 212)
Machine learning is physics (Ep. 211)
Autonomous cars cannot drive. Here is why. (Ep. 210)
Evolution of data platforms (Ep. 209)
[RB] Is studying AI in academia a waste of time? (Ep. 208)
Private machine learning done right (Ep. 207)
Edge AI for applications in military and space (Ep. 206)
[RB] What are generalist agents and why they can change the AI game (Ep. 205)
LIDAR, cameras and autonomous vehicles (Ep. 204)
Predicting Out Of Memory Kill events with Machine Learning (Ep. 203)
Is studying AI in academia a waste of time? (Ep. 202)
Zero-Cost Proxies: How to find the best neural network without training (Ep. 201)
Online learning is better than batch, right? Wrong! (Ep. 200)
What are generalist agents and why they can change the AI game (Ep. 199)
Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198)
Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197)
State of Artificial Intelligence 2022 (Ep. 196)
Improving your AI by finding issues within data pockets (Ep. 195)
Fake data that looks, feels, and behaves like production.(Ep.194)
Batteries and AI in Automotive (Ep. 193)
Collect data at the edge [RB] (Ep. 192)
Bayesian Machine Learning with Ravin Kumar (Ep. 191)
What is spatial data science? With Matt Forest from Carto (Ep. 190)
Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189)
History of data science [RB] (Ep. 188)
Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187)
Embedded Machine Learning: Part 5 - Machine Learning Compiler Optimization (Ep. 186)
Embedded Machine Learning: Part 4 - Machine Learning Compilers (Ep. 185)
Embedded Machine Learning: Part 3 - Network Quantization (Ep. 184)
Embedded Machine Learning: Part 2 (Ep. 183)
Embedded Machine Learning: Part 1 (Ep.182)
History of Data Science (Ep. 181)
Capturing Data at the Edge (Ep. 180)
[RB] Composable Artificial Intelligence (Ep. 179)
What is a data mesh and why it is relevant (Ep. 178)
Environmentally friendly AI (Ep. 177)
Do you fear of AI? Why? (Ep. 176)
Composable models and artificial general intelligence (Ep. 175)
Ethics and explainability in AI with Erika Agostinelli from IBM (ep. 174)
Is neural hash by Apple violating our privacy? (Ep. 173)
Fighting Climate Change as a Technologist (Ep. 172)
AI in the Enterprise with IBM Global AI Strategist Mara Pometti (Ep. 171)
Speaking about data with Mikkel Settnes from Dreamdata.io (Ep. 170)
Send compute to data with POSH data-aware shell (Ep. 169)
How are organisations doing with data and AI? (Ep. 168)
Don't fight! Cooperate. Generative Teaching Networks (Ep. 167)
CSV sucks. Here is why. (Ep. 166)
Reinforcement Learning is all you need. Or is it? (Ep. 165)
What's happening with AI today? (Ep. 164)
2 effective ways to explain your predictions (Ep. 163)
The Netflix challenge. Fair or what? (Ep. 162)
Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161)
Apache Arrow, Ballista and Big Data in Rust with Andy Grove RB (Ep. 160)
GitHub Copilot: yay or nay? (Ep. 159)
Pandas vs Rust [RB] (Ep. 158)
A simple trick for very unbalanced data (Ep. 157)
Time to take your data back with Tapmydata (Ep. 156)
True Machine Intelligence just like the human brain (Ep. 155)
Delivering unstoppable data with Streamr (Ep. 154)
MLOps: the good, the bad and the ugly (Ep. 153)
MLOps: what is and why it is important Part 2 (Ep. 152)
MLOps: what is and why it is important (Ep. 151)
Can I get paid for my data? With Mike Andi from Mytiki (Ep. 150)
Building high-growth data businesses with Lillian Pierson (Ep. 149)
Learning and training in AI times (Ep. 148)
You are the product [RB] (Ep. 147)
Polars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146)
Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)
Pandas vs Rust (Ep. 144)
Concurrent is not parallel - Part 2 (Ep. 143)
Concurrent is not parallel - Part 1 (Ep. 142)
Backend technologies for machine learning in production (Ep. 141)
You are the product (Ep. 140)
How to reinvent banking and finance with data and technology (Ep. 139)
What's up with WhatsApp? (Ep. 138)
Is Rust flexible enough for a flexible data model? (Ep. 137)
Is Apple M1 good for machine learning? (Ep.136)
Rust and deep learning with Daniel McKenna (Ep. 135)
Scaling machine learning with clusters and GPUs (Ep. 134)
What is data ethics? (Ep. 133)
A Standard for the Python Array API (Ep. 132)
What happens to data transfer after Schrems II? (Ep. 131)
Test-First Machine Learning [RB] (Ep. 130)
Similarity in Machine Learning (Ep. 129)
Distill data and train faster, better, cheaper (Ep. 128)
Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (ep. 127)
Top-3 ways to put machine learning models into production (Ep. 126)
Remove noise from data with deep learning (Ep.125)
What is contrastive learning and why it is so powerful? (Ep. 124)
Neural search (Ep. 123)
Let's talk about federated learning (Ep. 122)
How to test machine learning in production (Ep. 121)
Why synthetic data cannot boost machine learning (Ep. 120)
Machine learning in production: best practices [LIVE from twitch.tv] (Ep. 119)
Testing in machine learning: checking deeplearning models (Ep. 118)
Testing in machine learning: generating tests and data (Ep. 117)
Why you care about homomorphic encryption (Ep. 116)
Test-First machine learning (Ep. 115)
GPT-3 cannot code (and never will) (Ep. 114)
Make Stochastic Gradient Descent Fast Again (Ep. 113)
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)
[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)
Rust and machine learning #4: practical tools (Ep. 110)
Rust and machine learning #3 with Alec Mocatta (Ep. 109)
Rust and machine learning #2 with Luca Palmieri (Ep. 108)
Rust and machine learning #1 (Ep. 107)
Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)
Compressing deep learning models: rewinding (Ep.105)
Compressing deep learning models: distillation (Ep.104)
Pandemics and the risks of collecting data (Ep. 103)
Why average can get your predictions very wrong (ep. 102)
Activate deep learning neurons faster with Dynamic RELU (ep. 101)
WARNING!! Neural networks can memorize secrets (ep. 100)
Attacks to machine learning model: inferring ownership of training data (Ep. 99)
Don't be naive with data anonymization (Ep. 98)
Why sharing real data is dangerous (Ep. 97)
Building reproducible machine learning in production (Ep. 96)
Bridging the gap between data science and data engineering: metrics (Ep. 95)
A big welcome to Pryml: faster machine learning applications to production (Ep. 94)
It's cold outside. Let's speak about AI winter (Ep. 93)
The dark side of AI: bias in the machine (Ep. 92)
The dark side of AI: metadata and the death of privacy (Ep. 91)
The dark side of AI: recommend and manipulate (Ep. 90)
The dark side of AI: social media and the optimization of addiction (Ep. 89)
More powerful deep learning with transformers (Ep. 84) (Rebroadcast)
How to improve the stability of training a GAN (Ep. 88)
What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)
Deeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86)
[RB] How to generate very large images with GANs (Ep. 85)
More powerful deep learning with transformers (Ep. 84)
[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 83)
What is wrong with reinforcement learning? (Ep. 82)
Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81)
Attacking machine learning for fun and profit (with the authors of SecML Ep. 80)
[RB] How to scale AI in your organisation (Ep. 79)
Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78)
Training neural networks faster without GPU [RB] (Ep. 77)
How to generate very large images with GANs (Ep. 76)
[RB] Complex video analysis made easy with Videoflow (Ep. 75)
[RB] Validate neural networks without data with Dr. Charles Martin (Ep. 74)
How to cluster tabular data with Markov Clustering (Ep. 73)
Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)
Training neural networks faster without GPU (Ep. 71)
Validate neural networks without data with Dr. Charles Martin (Ep. 70)
Complex video analysis made easy with Videoflow (Ep. 69)
Episode 68: AI and the future of banking with Chris Skinner [RB]
Episode 67: Classic Computer Science Problems in Python
Episode 66: More intelligent machines with self-supervised learning
Episode 65: AI knows biology. Or does it?
Episode 64: Get the best shot at NLP sentiment analysis
Episode 63: Financial time series and machine learning
Episode 62: AI and the future of banking with Chris Skinner
Episode 61: The 4 best use cases of entropy in machine learning
Episode 60: Predicting your mouse click (and a crash course in deeplearning)
Episode 59: How to fool a smart camera with deep learning
Episode 58: There is physics in deep learning!
Episode 57: Neural networks with infinite layers
Episode 56: The graph network
Episode 55: Beyond deep learning
Episode 54: Reproducible machine learning
Episode 53: Estimating uncertainty with neural networks
Episode 52: why do machine learning models fail? [RB]
Episode 51: Decentralized machine learning in the data marketplace (part 2)
Episode 50: Decentralized machine learning in the data marketplace
Episode 49: The promises of Artificial Intelligence
Episode 48: Coffee, Machine Learning and Blockchain
Episode 47: Are you ready for AI winter? [Rebroadcast]
Episode 46: why do machine learning models fail? (Part 2)
Episode 45: why do machine learning models fail?
Episode 44: The predictive power of metadata
Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)
Episode 42: Attacking deep learning models (rebroadcast)
Episode 41: How can deep neural networks reason
Episode 40: Deep learning and image compression
Episode 39: What is L1-norm and L2-norm?
Episode 38: Collective intelligence (Part 2)
Episode 38: Collective intelligence (Part 1)
Episode 37: Predicting the weather with deep learning
Episode 36: The dangers of machine learning and medicine
Episode 35: Attacking deep learning models
Episode 34: Get ready for AI winter
Episode 33: Decentralized Machine Learning and the proof-of-train
Episode 32: I am back. I have been building fitchain
Founder Interview – Francesco Gadaleta of Fitchain
Episode 31: The End of Privacy
Episode 30: Neural networks and genetic evolution: an unfeasible approach
Episode 29: Fail your AI company in 9 steps
Episode 28: Towards Artificial General Intelligence: preliminary talk
Episode 27: Techstars accelerator and the culture of fireflies
Episode 26: Deep Learning and Alzheimer
Episode 25: How to become data scientist [RB]
Episode 24: How to handle imbalanced datasets
Episode 23: Why do ensemble methods work?
Episode 22: Parallelising and distributing Deep Learning
Episode 21: Additional optimisation strategies for deep learning
Episode 20: How to master optimisation in deep learning
Episode 19: How to completely change your data analytics strategy with deep learning
Episode 18: Machines that learn like humans
Episode 17: Protecting privacy and confidentiality in data and communications
Episode 16: 2017 Predictions in Data Science
Episode 15: Statistical analysis of phenomena that smell like chaos
Episode 14: The minimum required by a data scientist
Episode 13: Data Science and Fraud Detection at iZettle
Episode 12: EU Regulations and the rise of Data Hijackers
Episode 11: Representative Subsets For Big Data Learning
Episode 10: History and applications of Deep Learning
Episode 9: Markov Chain Montecarlo with full conditionals
Episode 8: Frequentists and Bayesians
Episode 7: 30 min with data scientist Sebastian Raschka
Episode 6: How to be data scientist
Episode 5: Development and Testing Practices in Data Science
Episode 1: Predictions in Data Science for 2016
Episode 4: BigData on your desk
Episode 2: Networks and Graph Databases
Episode 3: Data Science and Bio-Inspired Algorithms