Agents Of Tech

AI was supposed to make us better: faster searches, more productivity, wider knowledge, but it hasn't, and public sentiment is starting to shift. People are inundated with fake images and voices, AI-generated slop, unreliable answers, phony influencers, and a growing wave of content that looks real, but it may not be. At the same time, young people just entering the job market are asking whether AI will not only just take their jobs, but alter their ability to have a career altogether. Is the conversation around AI contributing to this ever-growing crisis of distrust and misinformation? As the tide turns on AI, could public backlash against Big Tech be reaching a breaking point?In this episode of Agents of Tech, our hosts Stephen Horn, Autria Godfrey, and Laila Rizvi explore whether AI is still a productivity tool ready to revolutionize the world for the better, or if it’s creating a crisis of distrust and leading to a wave of anti-AI backlash?In this lively discussion episode, our hosts grapple with the important debate about whether the public is turning against AI or maybe just against the companies deploying it. Or, to put it another way, “Are we heading toward Big Tech's big tobacco moment?”We start off with Stephen describing the CEO’s perspective into the agentic AI revolution, comparing the benefits of AI adoption with the negatives and the narrative around it. Autria and Laila debate Hollywood studio’s response to using AI. Is it a tool to be used for augmentation. Will it replace workers? Or is it reflective of a flawed communication strategy by the studios? Stephen compares the AI revolution to the Industrial Revolution and the lag between the actual change and the perception of that change - even bringing up the original Luddites. You’ll hear from previous guests on our show, like Emily Bender, who talks about AI’s impact on early jobs in a clip from our episode, “What AI Is — and Isn't: A Conversation with Emily Bender.” According to Laila, this is another example of Silicon Valley’s messaging problem, and she cites statistics that show that the AI trust gap is different in the U.S. than it is in China. Stephen pushes back on Emily’s concern that replacing entry level jobs with AI will create problems up the corporate ladder in the future. But when Autria asks him if he’s predicting the end of the C-suite, he says that AI will lead to a fundamental restructuring of the workplace.We also explore the impact of AI on politics, citing our previous guest Gary Marcus. Will anti-AI sentiment be an important element in our upcoming political discourse? Next up, we share a clip with Wikipedia founder Jimmy Wales from our episode, “Wikipedia, Media Bias and AI with Jimmy Wales.” We discuss the relationship between AI and human knowledge, and whether LLMs would be completely lost without humans. In our final thoughts, Autria, Laila and Stephen debate the concept of “AI for good” and the role of public perception of AI. Stephen concludes that because the fundamental change created by AI will affect all aspects of society, it’s society, however you define it, that will have to grapple with it. And Laila brings in the importance of capital markets and their strategy of selling fear as opposed to highlighting the value-add. What do you think? Tell us in the comments below.

Hundreds of billions of dollars are flowing into AI investment, yet our guest, Hala Hanna, Executive Director of MIT Solve, points out that less than 1% of AI venture funding is going to social impact. If AI is so powerful, why is so little of that power being directed toward the people and the problems that need it the most? Can AI really be used for the public good, or is the promise of AI for good just Silicon Valley lip service? We're talking AI, social impact, and the economics of doing good this week on Agents of Tech.According to Hala, “technology can and should serve humanity's most pressing needs, not just its most profitable markets. We think technology can close gaps in health, in wealth, in learning, and of course respond to climate change.”Solve was created a decade ago by the President of MIT, with the idea that “the challenges of our time require us to stretch a hand to all problem solvers around the world.” But because, according to Hala, “tech doesn't change the world, people do,” Solve finds incredible early-stage ventures that are using technology to close those equity gaps and helps them scale. Their global network of over 460 solutions reaches 430 million lives, with almost $90 million in direct funding and over $1.4 billion raised. Within their portfolio, AI-enabled ventures have been shown to reach 5 times more people than non-AI ventures. Hala tells us about one of their Solvers, a startup called Speetar that used AI to connect anyone with a smartphone to a doctor at a time where there was no digital healthcare infrastructure, and has since become the backbone of most of Libya's healthcare system.Hala and our hosts Autria Godfrey, Stephen Horn, and Laila Rizvi discuss why such a small percentage of the trillions of dollars being spent to build out AI goes to social causes. They also ask Hala what can be done to change that imbalance given the profit goals of the large hyperscalers and their responsibilities to their shareholders, as well as how to include the parts of the world that are being left out, like developing countries in the Global South.Among other things, Hala talks about how the “broligarchy” is employing a calculus that is completely disassociated from the real life of actual people, and that, “It’s also the regulatory wild west. Today, a deli sandwich is more regulated than AI.” Rather than wait for their conscience to kick in, Hala says, we need to structure incentives that get them to change, or to build alternative models. “We're back to prioritizing now cheap energy over clean energy, basically burning barrels of oil over micro doses of dopamine.”After our break, we cover how AI is being used with increasing frequency to not only assess grant applications, but also to write them. Hala explains how Solve is using AI, and why in their process, humans have the final say.We also explore how the “agentic revolution” is happening at a much faster rate than the industrial revolution, and how society can participate in imagining – and bringing about – a better future for everyone.As always, we end our interview with our Rapid Fire segment, where our hosts ask our guests a series of three questions. Autria asks Hala where people will draw the line when it comes to allowing AI in their personal lives; Laila asks her for something that's universally accepted that she strongly disagrees with; and Stephen asks her what she’s optimistic about.What about you? Do you think that AI should be pushed toward prioritizing more social good? Or are the commercial incentives simply too strong to sway current priorities? Can big tech help solve global problems? Or should we stop waiting for companies to do what only governments, funders and public institutions can mandate? Tell us in the comments below, and please be sure to like and subscribe.

The current AI boom is really built on the huge scaling of LLMs, but are they're getting it wrong? Alex LeBrun, Co-founder and CEO of Advanced Machine Intelligence (AMI Labs), argues that if AI is going to understand the world, model cause and effect, and reason across time, it may need something different: world models. His new company has raised more than $1 billion to try to prove it. He isn't making a technical argument – it's a challenge to the whole logic of the current AI boom. So today, our hosts Autria Godfrey, Stephen Horn and Laila Rizvi are asking Alex LeBrun whether the industry is over-invested in scaling just one dominant paradigm, and whether world models are a real alternative or just a better theory in a market that may have already chosen its winner. Autria kicks things off by asking Alex where LLMs fall short. According to Alex, “If you want to understand or manipulate the real world, then LLMs are not good…across the board.” He explains that LLMs are good in language-first tasks: everything that is discreet and recognized, like mathematics, coding, and information retrieval. But that’s “only one class of problems. It's not everything in the world.”Alex defines what world models are, how they are trained, and what they are most useful for. You’ll hear about how AMI chairman Yann LeCun and his team developed a concept called JEPA, Joint Embedding Predictive Architecture, which is a way to train world models through self-supervised learning. We explore how AMI is using data-rich video to train their world models – and why the vast majority of the videos on YouTube just won’t cut it as source material. Laila asks about the possibility of scaling LLMs to the point where they can lead to implicit world models emerging. Alex disagrees. He points to diminishing returns on the core progress of LLMs in spite of spending billions of dollars. Instead, he sees world models as complimentary to LLMs the way physicians need real world training in addition to only reading books. He even compares how LLMs and world models can mirror the way the human brain works, with different areas of the brain responsible for different functions while working together in tandem.We dive into the economics of AI, from whether further investment in scaling LLMs makes sense to whether world models will catch on with investors. Alex shares some of the challenges he’s faced competing for compute and raising capital in an investment climate where Anthropic has a $30 billion run rate.When Stephen wonders how world models will fit in with agentic AI in areas like healthcare, Alex points out that agentic models are very brittle and usually break when it comes to long term planning. He says 99% accuracy isn’t enough if it means killing 1 out of every 100 patients. World models build an internal representation of the world, which is more deterministic, and will allow for longer term planning with more accuracy.Finally, it’s time for our Rapid Fire segment, where our hosts ask our guests a series of three questions. Autria asks Alex where people will draw the line with AI in their personal lives; Laila asks him for something that's universally accepted in his field that he disagrees with; and Stephen asks Alex what will happen in the future that people aren't talking about now.In our post interview discussion, Autria. Stephen and Laila discuss whether Alex made the case for including world models in the AI economy, including diverting some of the capital being invested in LLMs into world models.We also want to know what you think. Are world models the new frontier in the world of AI, or is scaling LLMs still the best bet forward for seeing all the potential that AI has to offer? Tell us in the comments.

AI at work has been sold as a productivity revolution. But Microsoft's latest Work Trend Index points to a more complicated story. Workers are using AI, companies are buying the tools, and agents are beginning to take on more of the execution – but many organizations are not yet built to capture the value. The question is no longer just whether AI can help people work faster. It's whether companies know how to redesign work around it. Is enterprise AI finally moving from experimentation to measurable impact? Or are companies still buying the promise before they know how to change the work?In this episode of Agents of Tech, we’re talking the current state of AI, workplace productivity, and the rise of agents with Matt Firestone, General Manager of Frontier Function at Microsoft, who leads their work on Microsoft 365 Copilot and agents.Matt, who has been closely involved in the company's research on “The Frontier Firm,” starts off by explaining how Microsoft looked at native AI companies to figure out how existing companies can reorganize themselves to be more like them and reach the frontier. He and hosts Autria Godfrey, Stephen Horn and Laila Rizvi discuss frontier professionals, who use AI agents for multi-step workflows and multi-agent systems. According to the Microsoft Work Trend Index, 16% of people self-identify that way. Matt describes the “Transformation Paradox,” where frontier professionals do great things using AI but get resistance from their organizations. The group talks about how the rate of change is much faster than the Industrial Revolution, and how leaders need to listen to and rely on their employees who are ahead of them on the AI adoption curve. They explore barriers to adoption, including technology hurdles, organizational culture, and the relationship of individuals to AI and their career development.When Autria brings up the topic of AI replacing entry level workers, Matt points to data from the MWTI that shows people are using AI for different things than rote, repetitive task work. “49% of the usage of M365 Copilot was higher-order cognitive tasks. So things like deep data analysis, asking multi-shot questions, more sophisticated types of long-running research.” Which means, Matt says, that AI is actually raising the level, scope, breadth, and depth of what an entry level worker can do in their first few years. Laila asks Matt about how to make AI less of a product, where employees use it within limits, and more of a substrate, where frontier professionals can use AI to extract the most value out of it. Matt says that AI is “raising individual ambition” and shifting from task-based knowledge work to outcomes-based thinking.Stephen and Matt talk about how enterprise AI can unlock value in employees as well as organizations. Matt describes the 15x year-on-year growth in agents across their ecosystem, and explains Microsoft’s Customer Zero Program. In our Rapid Fire segment, Autria asks Matt about where people will draw the line with AI in their personal lives; Laila asks Matt for something that's universally accepted in your specialist field that he disagrees with; and Stephen asks Matt what he’s optimistic about?Be sure to stick around for the conversation between Autria, Stephen and Laila about how not every company is as engaged with AI adoption as Silicon Valley may think, and what the cost of falling behind may be.What are you seeing in your offices? Is AI at work genuinely changing how your organization operates, or is it still mostly experimentation at employees' leisure? If you are using AI tools, where are you seeing the real value? Has your company successfully implemented AI strategies that are supported by the proper framework, or are they woefully under-prepared on how to execute AI in your industry? Share some successes and some shortcomings in the comments below.

Disclaimer: The conversation in this video is for information purposes only and does not constitute medical advice. Always consult a licensed healthcare professional before making any health decisions.Dr. Eric Topol believes AI can predict disease decades before symptoms appear, and he's argued that AI without doctors may outperform doctors with AI. But someone has to set the rules. And right now, the people most likely to write them aren't doctors or patients, but rather insurers, tech companies, and hospital administrators. This week on Agents of Tech we’re exploring prevention, power, and who really controls medical AI, with Dr. Eric Topol, cardiologist, bestselling author, and Founder and Director of the Scripps Research Translational Institute.We start with Autria asking Dr. Topol about accountability when it comes to medical AI. “It has to be accountable,” he says, but “The problem most people don't realize is there's lots of errors by physicians. In the US, you know, 800,000 serious diagnostic errors a year that result in disability or death. So we're trying to improve accuracy.” He describes studies that show that AI without doctors outperforms doctors using AI, and offers some possible reasons that the studies came to those conclusions. Stephen brings up a Swedish breast cancer study that showed a 29% improvement when AI was brought into detecting, and wonders why that kind of result hasn’t led to widescale adoption. Eric breaks down some of the issues with adopting AI, and he and Laila discuss the implementation problem, including the impact of income disparities on access.Dr. Topol and Autria consider the differences between using AI to look for a cure, and what Eric feels is more promising, using AI for disease prevention. He explains to Stephen how AI has already been used not only to predict disease, but also when it will show up! With Alzheimer’s, for instance, there are layers of data we’re not yet using. “There are biomarkers, like the breakthrough one for Alzheimer's disease, p-tau217, that tells us in advance 15 or 20 years about people who are destined to have a high risk.” Later, he goes into more detail about how AI can make a difference in preventing Alzheimer’s and slowing down the “brain clock.”The conversation shifts to who controls medical AI and what their goals are. Dr. Topol describes hospital administrators who only want to use AI to “increase revenue and to use AI to maximize productivity…My biggest concern about the AI era in medicine is we have this great chance to restore a remarkable patient-doctor relationship. We may never see it again for a long, long time, if ever. And we could blow it because of business-centric issues.”Eric tells Laila how AI can give doctors back time they spend on writing notes, and how China is using opportunistic AI – finding things that were not the reason why abdominal and chest CTs were done that doctors miss – to pick up pancreatic cancer before it’s too late. Another question the team addresses is whether medical AI will benefit everyone or just the rich. Dr. Topol says it will be hard work to ensure the democratization of healthcare, and that it’s one of his primary worries.Finally, it’s our lightning round. Autria asks where people will draw the line with AI, and Eric talks about the current public backlash to AI that he feels will fade over time as we resolve their issues.Laila asks Eric what’s widely accepted in his field that he disagrees with, and he says it’s ludicrous that people in genomics say we shouldn't be using polygenic risk scores. And Stephen asks Eric what people aren't talking about now, and he says “no one's really talking about this prevention opportunity. I'm kind of the lone wolf out there.”What about you? If AI could predict your disease decades early, but the price was that that data is sitting in the hands of tech companies and insurers, not necessarily your doctor, would you still want it? Tell us in the comments.

CuspAI was co-founded by Max Welling, a pioneer of modern AI. But instead of building chatbots, he's creating and harnessing AI to discover new materials for everything from carbon capture to water purification, plastic alternatives, and more efficient batteries. If this works, it could change everything. But can it? And if so, when? This week, we’re talking science, scale, and when CuspAI will be able to deliver with company co-founder Max Welling.Before talking to Max, hosts Autria Godfrey, Stephen Horn, and Laila Rizvi discuss why AI-powered material creation is so exciting… and why it needs to be addressed with little skepticism. Autria kicks off the interview by asking Max Welling about CuspAI’s plans for 2026. He explains that they’re currently building their platform, and that 2026 will be a time when CuspAI will work with customers to actually design new materials, synthesize them, and put them into the real world. In 2026, they also plan to connect to high throughput self-driving lab experimental facilities to increase the speed of experimentation.Max unpacks how the process works, starting with a customer describing the real world material that they need, to AI agent assessment to see whether anything already exists that can fit the task, to generating entirely new materials that never existed before. They typically generate “hundreds of thousands to millions of those,” which are first tested by a digital twin consisting of very cheap property predictors that quickly assess whether the material could exist in this world, followed by sophisticated molecular dynamic simulations to assess their actual properties in high accuracy. Laila and Max discuss the process of verification in materials science, and Max lays out some of the difficulties that lay between moving from simulation into the real world.Autria asks where their first successes will come. Max predicts that it will be in the semiconductor space, partly because of partners like Hyundai that have the need and the capability to manufacture the new materials at scale. Max explains how semiconductor lithography has gotten to a point where they’re creating the smallest structures possible that are the size of a few atoms, and in order to make that happen they really need new materials. “We are now creating chips that grow in the third dimension, so they become sort of taller.”Stephen brings the conversation around to scalability, the importance of finding partners that can power that growth, and whether that scale can even happen in Europe. Max makes a distinction between making materials at scale and scaling up a company, but says that Europe has the right companies for both.Laila raises the issue of commercial demand versus public good, and Autria asks about the pressure around using AI for the betterment of humanity. Max’s answer: “For me, the only reason I do this is because I do want to make a positive net impact… And I think the same goes for all our employees.”As always, we end with our lightning round questions. Autria asks where people will draw the line with AI, and Max says “We don't want AI to invade our privacy. We don't want AI to be used for mass surveillance. We don't want AI to get us addicted. We don't want AI to manipulate our opinions.”Laila asks Max for something that's widely accepted in his field that he disagrees with. He says that AI superintelligence replacing humans might be a little overhyped at this point.And Stephen asks Max what will happen in 18 months that people aren't talking about now? Max’s answer: “That we can design materials that feel quite exotic right now, with properties that you could not imagine, and it could completely change the world, and hopefully for the better. That's what we are shooting for.”What do you think? Will CuspAI be able to deliver on their promises in 2026? Will AI help us create new materials that benefit humankind in less than a year? Or will it take them longer? Tell us in the comments.

If you survive the next five years, says immunologist Derya Unutmaz, MD, you will live for the next 50 thanks to what AI can accomplish in medical research. But is AI really a silver bullet that solves humanity's most difficult problems? Or does that kind of thinking get us into trouble?Professor Unutmaz is an NIH funded immunologist, with 35 years of published research and more than 100 papers, and a professor at the Jackson Laboratory.But when it comes to what he calls the bio singularity – the moment when the convergence of AI and biotechnology radically transforms human biology – he’s ahead of most of his fellow scientists and researchers. He’s also ahead of most predictions about AGI and SGI, or super general intelligence, by at least a couple of years. But what if he’s right?“In the US alone,” Dr. Unutmaz tells hosts Autria Godfrey, Stephen Horn, and Laila Rizvi, “there are 12 million misdiagnoses every year. Only about 70% of the diseases are diagnosed correctly by medical professionals. And about 700,000 people die or…become sick because of misdiagnoses, okay?... with AI, if you could improve that by 10%, you are saving hundreds of thousands of lives.”For Derya, AI means the totality of artificial intelligence, including LLMs, agentic systems, world models, and more. He likens the different areas of AI to how the human brain works, with different areas managing different tasks. He even likens his own scientific training to the way AI models are trained.He explains that there are multiple levels of Artificial General Intelligence, where general means that you can generalize knowledge. “So for example, if you learn something on one topic, we can somehow generalize that information to learn something completely different, or understand something completely different.”According to Derya, we’re already at AGI Level One, where a system is as good as the top 1% of humanity. He thinks there will eventually be three or four levels of AGI. Dr. Unutmaz says that when Demis Hassabis is describing AGI, he really means ASI, or Artificial Super Intelligence, which is better than human. Derya says that “the Einstein test” – where we train an AI models on pre-1911 knowledge and ask it to recreate the General Theory of Relativity – is unfair, because only a few people in the history of humanity have been capable of such incredible insight.He also thinks we’ll reach a point where most jobs that depend on using a “computer, or your intelligence, or your experiences or expertise could be eventually replaced by AI.” In fact, he says, “I’m a scientist…and I can tell you that current AI models like GPT-5.2 PRO [are] simply better than me.”Derya says we are going to have to rethink the whole fabric of society and the impact of AI will be extremely disruptive. Whether he is right or not about the timeframe for AGI or the bio singularity, Stephen, Laila and Autria agree that the disruption from AI is here, now, and not enough people are addressing it. Do you think AI will cure disease within a decade, or that it's just a dangerous thing for a scientist to claim? Tell us in the comments.CHAPTERS:00:00 - Will AI Cure All Disease Within 10 Years?00:55 - AI, Disease and Bio Singularity with Immunologist Derya Unutmaz01:21 - Why So Much Hype and Uncertainty Around AI and Science?03:06 - We Can’t Leave This to the Scientists… or the Tech Bros03:55 - If You Survive the Next 5 Years, Will AI Help You Live 50 More?04:20 - Tech and AI Capabilities Doubling Every Few Months07:31 - What is AGI?08:44 - Are We Prepared for the Biggest Transformation in History?10:48 - Is AI Hitting a Wall? Is the Einstein Test Fair?13:58 - I’m a Scientist, and GPT-5.2 PRO Is Better Than Me15:29 - Will There Always Be a Place for Humans In Science?18:17 - Is It Unethical for Doctors and Scientists Not To Use AI?24:55 - Aging Can Be Reversed Says Dr. Derya Unutmaz25:26 - Is There Any Point in Publishing Scientific Papers Now?

As AI gets more capable, will it make public information more trustworthy, or less? Does news media have to be biased to be financially successful? Is AI a threat to Wikipedia or will we always be reliant to the human component when it comes to seeking trustworthy information? These are timely questions about AI, information, technology and trust that affect us all – which is why Stephen Horn, Autria Godfrey and Laila Rizvi are interviewing the founder of Wikipedia, Jimmy Wales.We start with a discussion of trust about where we get our information, and how to build trust amidst the changing economics of news media and AI.With Wikipedia celebrating its 25th Anniversary, Autria asks Jimmy how they overcame the public’s initial distrust and what he thinks about the current cynicism towards AI. He admits that “There is, you know, a cycle that happens…when the quality is low and something's very new, then people obviously are skeptical and quite reasonably so.”Laila asks if we’re close to AI superintelligence, and Jimmy explains that he’s a tech geek but not an expert in AI. The people he listens to, his friends Gary Marcus and Demis Hassabis, think we need some fundamental breakthroughs before that. Of course, he says, they may be wrong and things are moving pretty quickly. “It’s a classic sort of thing in tech, it’s an old saying: People tend to overestimate the short run and underestimate the long run.”The conversation turns to the value of neutrality and unbiased information. Laila suggests that people are happy with the ease of the answers they get from AI or social media and don’t have the luxury of researching every issue. Jimmy offers an “imperfect” analogy to junk food, saying “Junk food’s easy. Tastes really good right now… So I don't buy [crisps]. I don't like to have them around because… I actually do have a higher order sort of brain.”Stephen points out that the media world seems to be moving beyond providing multiple perspectives on an issue, and that there is no business model for neutrality. Jimmy disagrees, citing Wikipedia’s popularity, which is higher than the top 10 newspapers combined, and suggests that, when it comes to neutrality and fighting bias, “We have to fight for it.” In our rapid fire segment, Autria asks where people will finally draw the line when it comes to AI. Jimmy cites OpenClaw and his feeling that people will draw the line between using AI to get things done and the improper use of personal information by that AI.Laila asks Jimmy what's something that's universally accepted in his field that he disagrees with? His answer: “That news media has to be biased to be financially successful,” although he admits, “I'm a minority viewpoint there.”Finally, Stephen asks what Jimmy sees in the future that we’re not talking about today? Jimmy says we’re focused a lot about AI in LLMs, but there are other things going on like advances in biology, drug discovery, driverless cars and other positive, transformative developments that deserve more attention. “I think there's a lot more that's going to come that's going to be really pretty amazing.”CHAPTERS:00:00 - Introduction01:00 - Is Trust in Ai, Tech and Media in Short Supply?04:10 - Early Skepticism about Wikipedia and AI05:34 - When and Where To Use LLMs and AI06:40 - Jimmy Wales on AI: Pretty Terrible at Facts but Kind of Creative07:17 - Can AI Work With the Right Framework?10:04 - Will AI Replace Wikipedia?13:22 - The Seven Rules of Trust - Neutrality and Bias15:18 - People Tend to Trust Individuals Over Abstract Entities16:22 - Echo Chambers, Convenience and Trust20:43 - Media Literacy and the Economics Of Trust22:23 - Is There a Media Business Model for Neutrality?24:19 - Drawing the Line Between Personal Info and Getting Things Done25:14 - News Media Doesn’t Have to Be Biased to Be Financially Successful25:38 - Bright Future for AI in Biology, Drug Discovery, Driverless Cars, More27:11 - Can AI and Wikipedia Coexist?

NOTE: This episode was recorded before the recent conflict involving Iran began.The US and China control over 90 percent of the world's AI computing power. In practice, that means most countries rely on American or Chinese firms, chips, and rules to access the most advanced systems ever built.Some call it partnership. Others call it dependency.Our guest today, Ryan Wain, the Senior Director of the Tony Blair Institute for Global Change, advises governments on how to navigate this. His answer? Stop trying to compete. In fact, he calls self-sufficiency a "vanity project."But here's the question: if you're not one of the two countries holding the keys, what leverage do you actually have? And if you are America or China, should you share this power at all?Hosts Autria Godfrey and Laila Rizvi start off with the TBI report which argues that AI self-sovereignty is unrealistic for most countries. Autria asks if AI power is already so entrenched that we’ll just see a widening divide between the haves and the have nots. Ryan says that the US and China have spent so much money building frontier models, that other countries building their own frontier AI is now an unrealistic strategy. Instead, they need to figure out how to take part in the AI revolution by leveraging their strengths and opportunities, like Kazakhstan’s pan to train a million people to become AI engineers, or Kenya, which has geothermal energy they’ve used as leverage build partnerships with tech companies to bring AI to their country.Ryan says, "Control what you can, steer where you have leverage, and then depend on those partners for the rest." Could geopolitical tensions bleed over into AI access, so even allies like the UK could end up locked out of US-based AI? Ryan argues that long before this happens, countries need to not get locked into one model. He points out that “Sovereignty is a choice and we have levers that we can pull” and that the UK and Europe are looking at multiple models, including open source models. What about concerns that AI can be used to create more authoritarian states as we’re seeing in China and the US? Political leadership needs to understand the importance of harnessing technology and make the case that it can provide greater privacy protection, safety from crime, and even security during wartime. He points out how Estonia has digital ID and yet ranks as the second freest online environment, after Iceland.Should the US be letting China get its chips? Is AI more like the development of 5G or more like the nuclear arms race? Neither, says Ryan. Sovereign frontier models don’t guarantee national prosperity or security. Advantage comes from a robust and diverse set of tech companies like America has. The path involves proper industrial strategy, communicating with the public, addressing energy needs and data centers, training, and supporting founders and leaders to build next gen AI companies that transform everything from healthcare and public services to boosting national security.CHAPTERS:00:00 - Power, Partnership and Dependency01:22 - Is this a Catch 22?02:30 - What Does AI Sovereignty Really Mean?03:07 - Is It Better To Build Your Own Frontier Model?04:02 - What If the US Pulls the Plug?O4:58 - Frontier AI Models Are Impossible for Many Countries05:34 - What Are The 3 Dimensions of AI Sovereignty?07:14 - You Can’t Be Dependent on One AI Model07:59 - Sovereignty Is a Choice and We Have Levers that We Can Pull08:12 - Europe Embraces Open Source More Readily than US or China08:56 - Smaller Nations Should Leverage Their Strengths with AI10:23 - Digital ID, Facial Recognition, Surveillance15:36 - Should US Give AI Chips to China?17:36 - Europe Needs More Global Tech Startups18:40 - The World Is Interconnected19:54 - Is AI Sovereignty a Fantasy?20:39 - What Advantages Do Countries Other Than China and the US Have?22:25 - Energy Costs, Talent and Industrial Strategy26:30 - Is True AI Sovereignty Even Possible?

Will AI destroy humanity? Most people think that's science fiction. The people actually building it aren't so sure.Geoffrey Hinton - the Godfather of AI - says there's a 10 to 20 percent chance AI wipes us out. OpenAI’s Sam Altman told Congress his own technology could 'cause significant harm to the world.' Our guest, Malo Bourgon, CEO of the Machine Intelligence Research Institute (MIRI) has been warning about this for two decades. He says a machine doesn't need to be sentient to become a global risk. And today's safety measures? Nowhere near enough.Can we build an off switch for a machine that's smarter than us?Malo tells hosts Autria Godfrey and Laila Rivzi that he thinks we’re on the path to building systems that are radically smarter than us, speeding faster in a race to build systems that we don’t understand and that we don’t know how to control that “could end up with none of us around to see the future we could have built instead.”While he’s not worried that current AI models are existentially dangerous, Malo agrees that some emergent behaviors, like the deception we’re seeing in smarter general systems, suggest that if we continue to scale towards superintelligence, they’ll only have a bigger impact. The trio discuss whether it would even be possible to build in a safeguard, like an off switch. Malo considers this a losing battle if we wait too long and even then, it would be better if we built a broader system that allows us to not get into that position in the first place. Some harms and disruptions from AI are already happening. Malo suggests that we need to find some way to have coordinated action globally, even among adversaries. When it comes to existential risk, he draws a comparison to the nuclear arms race and the cold war, and how, thanks to treaties and agreements and some luck along the way, we’re still here.Autria asks about specific ways AI can lead to a catastrophic, apocalyptic ending, including disruptions to the food chain, the creation of bioweapons, mass unemployment and faltering economies. Malo says even if we solve those, there remains the core danger which comes from a misalignment of the values and goals of the superintelligence we build and our own. It doesn’t have to be evil, it just has to” care about weird, other things that aren’t the things we care about and it wants to pursue those things” with an indifference to our existence.So what would convince Malo that AI is safe? Changing how we create AI systems, he says, from growing them “in a very brute force way” the way we do now to crafting them with a more principled sense of what we’re trying to do to make them safe.Finally, Malo suggests that the claim that “the people running these companies are, you know, bad, immoral people" isn’t quite right. He says that most of “these people aren't actually villains, they're normal people who are kind of trying to do the right thing but they're in a really bad situation. But I also think they're also doing a bunch of bad stuff.”What about you? We want to know what you think. How concerned should we be about a doomsday scenario? How concerned are you? Tell us in the comments below,CHAPTERS:00:00 - Profits, Power, and AI Risk02:36 - Are We Racing Toward Extinction?03:33 - Are Big Tech’s Predictions of AGI Accurate?04:50 - Are AI Models Exhibiting Signs of Understanding?05:01 - Deception in Today’s Models07:51 - Can’t We Just Build an AI Off-Switch?10:10 - What Can We Actually Do?11:06 - Harms Are Already Here – More Over The Horizon12:00 - Global Race to AI Superintelligence13:00 - Parallels to Nuclear Arms Race15:14 - Pathways to Catastrophic Risk16:14 - The Boss Fight – Misalignment As The Core Danger18:51 - What Would “Safe” Look Like?19:40 - Rapid Fire Questions for Malo Bourgon19:44 - What Does Your Field Get Wrong?20:53 - Where Do People Draw The Line On AI In Their Lives?22:24 - Post-Interview Reflections24:44 - What Do You Think: Doomsday Scenario or Not?

ChatGPT has 800 million users. OpenAI is valued at $500 billion. But our guest today says the whole thing is a scam. Professor Emily Bender, author of “The AI Con” and Director of the Computational Linguistics Laboratory at University of Washington, argues Artificial Intelligence is just a broad marketing term and “AI" is just a label for unrelated tech - creating a false sense of an inevitable, God-like entity. Is she a prophet... or is she just wrong? We’ll ask our questions for Professor Bender in the episode, but if you’ve got questions for us, throw them into the comments below!Hosts Autria Godfrey and Laila Rizvi start by asking Emily whether AI is intelligent enough to replace humans. Emily says studies indicating that AI models are cheating, blackmailing, and playing dumb when they know they’re being tested don’t stand up. She says it’s elaborate interactive fiction, and that Anthropic’s “research” isn’t peer reviewed – basically, no more than blog posts. LLM training includes language that looks like introspection, so systems can output language that looks like introspection even though they have no capacity to actually engage in introspection.Emily suggests that replacing interns and entry level workers with AI short-circuits the process of training future leaders. She describes how AI systems exploit the Global South, with difficult psychological conditions and compensation so low it creates, as Autria suggests, the next generation of sweat shops.When it comes to AI 2027 and whether AI poses an existential threat, Emily says it’s just a case of “Big Tech Fan Fiction” from the same shared world as the thinking of Nick Bostrom and the Effective Altruist movement.What about claims by Anthropic that Claude Code wrote the code for Claude Cowork? Emily doubts those claims, explaining that those systems have no agency and require input to do something. Although Emily doesn’t buy into claims of near-term existential risk, AI is creating labor and environmental harm on local levels if not global ones, often with a lack of transparency.What about arguments like those by Nobel Prize winner Geoffrey Hinton that suggest LLMs understand meaning and can mirror how humans operate? Emily says that given his background and specific knowledge of how these systems are built, he “really ought to know better.” She explains that unless we have access to the training data actually used on these systems, we can’t know that they are actually understanding concepts without explicit training.After Professor Bender leaves, Autria and Laila discuss whether Professor Bender’s dismissal of some of the data Laila presented is appropriate or incorrect.CHAPTERS: 00:00 - Is AI Hype a Scam?01:33 - AI: Existential Risk or Theater?02:02 - Dario Amodei and Demis Hassabis At Davos: 1-2 years Until AI Is a Risk02:50 - Revolution or Con?03:07 - How intelligent is AI, really? We ask Emily Bender03:30 - Is AI Intelligent Enough to Replace Humans? Emily Bender Says No!04:24 - “Cheating” Models and False Agency06:32 - Will AI Take Our Jobs or Just Make Them Crappier?06:43 - AI and the Career Ladder Problem07:54 - Are AI Systems Exploiting Data Workers in the Global South?08:18 - The Hidden Human Labor of AI10:47 - AI 2027 and Big Tech Fan Fiction?12:29 - Are LLMs like Claude Really Writing Their Own Code?13:45 - Does AI Code Itself?14:41 - Does AI Need to Be All-Powerful to Pose an Existential Risk?15:44 - Environmental and Labor Harms16:35 - Is AI Power and Water Consumption As Bad As Some People Claim? 17:41 - If AI’s Importance to Humanity Is Overhyped, Why Do So Many Believe It? 17:52 - Why the Hype Worked18:48 - Can Neural Networks Mirror Human Neurology?21:02 - Geoffrey Hinton and “Understanding”22:07 - What Is AI Actually Good For?23:23 - Questions for Professor Bender23:36 - Is AGI Inevitable?24:08 - Where Do Humans Draw the Line?25:28 - After the Interview: Who’s Right?27:34 - What Do You Think: Doomsday or Hype?

Diplomacy used to be about treaties and territory – now it seems it's more about data, algorithms, and the companies that control them. At Donald Trump’s inauguration, Silicon Valley’s most powerful figures stood steps away, a sign that Big Tech now sits at the centre of global power. Tech companies pervade everyday life and wield power once reserved for nation states. Are the people in charge of global power those elected to office or those appointed to positions within those companies? To explore how AI is reshaping diplomacy, from negotiation and representation to influence operations and disinformation, hosts Autria Godfrey, Stephen Horn, and Laila Rizvi interview Dr. Jennifer Cassidy, AI & Diplomacy, University of Oxford, about: How AI is transforming diplomacy’s core functionsWhy Big Tech now rivals governments in geopolitical influenceThe rise of “digital sovereigns” and private powerWhen former political leaders move into tech, where accountability goesDemocratic versus authoritarian uses of AIWhy global AI governance is still largely non-bindingFor Dr. Cassidy, diplomacy rests on three, timeless pillars: communication, representation, and negotiation. AI “is not demolishing these pillars, but quietly rewiring the architecture that holds them together… Predictive analysis now allows ministries to read the global mood” almost in real-time. The United Nations and the World Bank use AI models that monitor food prices, rainfall patterns, and social media data to anticipate instability “up to 6 weeks before that instability might actually break out.” NATO employs machine learning to map Russian disinformation. “What we’re seeing here is the move from reactive diplomacy… to anticipatory diplomacy.”One of the most pressing questions is whose AI is being used to create “sovereign diplomatic AI systems.” France and the EU train their AI on Mistral, a French company. US AI models are OpenAI's and Anthropic's. Microsoft's Azure Cloud hosts data for NATO and national governments.These companies have become “digital sovereigns” – private actors who control the three levers of power that were once defined by the state: information, infrastructure and interpretation. Former politicians like Nick Clegg (Meta) and Rishi Sunak (Microsoft) represent a “circuit of influence” where “experience, access, and authority are just flowing continuously between capitals and campuses in Silicon Valley.” While “democracies do need experienced voices helping to steer the tech transition,” we must ensure that “when the expertise moves, accountability moves with it.” What about bad actors using AI? Jennifer says we’ve seen this in elections in the US and the world. In China, “predictive policing algorithms are tracking not just where crime might occur, but who might commit it… Authoritarian regimes are combining facial recognition, travel data, and digital behaviour into vast surveillance scores.” It is “digital authoritarianism in its most refined form… controlled by prediction, rather than force.”Dr. Cassidy concludes, “We have a very, very, very long way to go regarding the governance and structure of, and frameworks for AI… a difficult task… that has to be done.”What’s your take? Share your thoughts in the comments and subscribe for more on AI, geopolitics and global power. CHAPTERS00:00 Tech, Trump and the New Global Power Game01:26 Do Tech Giants Now Run Foreign Policy?04:00 How AI Is Reshaping Diplomacy? 06:37 Why Nations Are Building Their Own AI Models 09:18 Have Big Tech Companies Become Sovereigns?12:33 From Prime Minister to Big Tech: The Revolving Door16:46 AI Power Politics Beyond the West19:43 AI for Good or Digital Authoritarianism?22:09 Who Sets the Rules for AI?24:48 Closing Thoughts with Dr. Jennifer Cassidy25:05 Debrief: Authoritarian Drift and Regulation Fights27:13 AI Ministers, Echo Chambers and What Comes Next

Quantum computing may finally be ready for the real world - and it could power the next wave of AI. In this episode of Agents of Tech we sit down with Oxford Quantum Circuits (OQC) CEO Gerald Mullally to explore how OQC is integrating quantum computers into data-centres in London, Tokyo and New York.

Larry Ellison built Oracle into a cornerstone of the modern tech economy. Now he is making a $2.5 billion bet on Oxford, backing the Ellison Institute of Technology at Oxford to fuse AI, medicine and sustainability in one global hub.In this episode of Agents of Tech, Autria Godfrey, Stephen Horn and Laila Rizvi sit down in Oxford with Professor Santa Ono, Global President of the Ellison Institute of Technology (EIT), to ask a simple question: Can Oxford really become Europe’s Silicon Valley?We explore:- Why Ellison chose Oxford and the UK over Chicago or California- How EIT plans to recruit 7,000 world class scientists and double Oxford’s research base- The model of science-led capitalism and why commercialization is central to Ellison’s vision- The UK’s unique advantage in health data and biobanks (NHS data, UK Biobank, Protein Data Bank)- How AI, machine learning and robotics will change drug discovery, pandemics and healthcare- The relationship between EIT and Oracle, and how independent the institute really is- Parallels and contrasts with the Bill & Melinda Gates Foundation model of philanthropy- What this means for the UK’s role between the US and China in the global innovation raceProfessor Ono explains why he believes the UK is now one of the best places in the world to build AI-driven science: from single-payer health data to a fast-growing ecosystem of serial entrepreneurs. He also addresses questions about data privacy, ethics, bioterrorism risks and public concerns about American tech money in historic British institutions.If you care about:- How AI and health data will reshape medicine- Whether Oxford and Cambridge can anchor Europe’s answer to Silicon Valley- What it really takes to build a global science and technology campus at scale…this conversation is for you.Tell us in the comments: Do you think Ellison’s Oxford gamble is a bold new model for global science, or another moonshot that will be hard to scale?CHAPTERS00:00 Larry Ellison’s $2.5B bet on Oxford00:35 Agents of Tech intro01:22 Why Oxford?02:45 Interview begins: Santa Ono03:01 Ellison’s vision for EIT05:11 Scaling talent and entrepreneurship05:53 Science capitalism vs traditional philanthropy07:52 Why base EIT in the UK10:38 NHS data, privacy and AI concerns12:55 AI’s impact on jobs and drug discovery15:12 Commercialisation and scientific breakthroughs17:38 Building a new global research hub20:26 AI geopolitics and the UK’s role21:03 EIT as a global model22:50 Interview ends23:01 Post-interview reflections24:41 Closing and invitation to Larry Ellison

Is the U.S. LOSING the AI race to China?China and the U.S. are neck and neck in the AI race for global dominance. Former OpenAI board member Helen Toner (now at Georgetown’s CSET) joins us in Washington, D.C. to break down China vs U.S. strategies—open-source diffusion vs big tech global dominance— and what “winning” actually means.Helen has recently spent time in China and works at the center of U.S. AI policy—offering a rare inside view of both ecosystems and who’s truly ahead.Helen explains: - Who’s ahead right now and how to measure it (frontier AI vs adoption/diffusion)- Open-source vs closed: DeepSeek, Qwen, Kimi, Gemma, Llama vs OpenAI, Anthropic, Google- Compute & chips: NVIDIA dependence, export controls, and why compute concentration matters- AGI timelines: whether “AI 2027” holds up and why short timelines cooled after GPT-5- “AI+” strategy: applying AI to manufacturing, healthcare, and finance vs pure frontier bragging rights- What governments should do now: transparency, auditing, AI literacy, and measurement scienceWho do you think is winning and WHY – China or the U.S.? Drop one evidence-backed reason (links welcome). We’ll pin the best reply. Don’t forget to like and subscribe for more unfiltered conversations on AI, tech, and society.Chapters00:00 – Two strategies, one AI race01:00 – Open-source China vs Big-Tech USA03:37 – Not one race: choose your finish line04:04 – Who’s actually open? DeepSeek, Qwen/Kimi, Llama, Gemma, GPT-OSS06:26 – Frontier bragging rights vs real-world adoption07:46 – China’s “AI Plus” play (AI + industry)10:06 – Is the US still ahead at the frontier?12:04 – GPT-5 reality check & AGI timelines20:58 – Compute decides: chips, export controls, auto-ML engineers23:04 – What we need now: transparency, audits, AI literacy28:02 – Standards in practice: de-facto beats de-jure30:56 – Next 5 years: closed peaks, open bow wave37:55 – Final take: which path wins?#OpenAI #HelenToner #ai #GPT5 #OpenSource #podcast #China #DeepSeek

Is the U.S. LOSING the AI race to China?China and the U.S. are neck and neck in the AI race for global dominance. Former OpenAI board member Helen Toner (now at Georgetown’s CSET) joins us in Washington, D.C. to break down China vs U.S. strategies—open-source diffusion vs big tech global dominance— and what “winning” actually means.Helen has recently spent time in China and works at the center of U.S. AI policy—offering a rare inside view of both ecosystems and who’s truly ahead.Helen explains: - Who’s ahead right now and how to measure it (frontier AI vs adoption/diffusion)- Open-source vs closed: DeepSeek, Qwen, Kimi, Gemma, Llama vs OpenAI, Anthropic, Google- Compute & chips: NVIDIA dependence, export controls, and why compute concentration matters- AGI timelines: whether “AI 2027” holds up and why short timelines cooled after GPT-5- “AI+” strategy: applying AI to manufacturing, healthcare, and finance vs pure frontier bragging rights- What governments should do now: transparency, auditing, AI literacy, and measurement scienceWho do you think is winning and WHY – China or the U.S.? Drop one evidence-backed reason (links welcome). We’ll pin the best reply. Don’t forget to like and subscribe for more unfiltered conversations on AI, tech, and society.Chapters00:00 – Two strategies, one AI race01:00 – Open-source China vs Big-Tech USA03:37 – Not one race: choose your finish line04:04 – Who’s actually open? DeepSeek, Qwen/Kimi, Llama, Gemma, GPT-OSS06:26 – Frontier bragging rights vs real-world adoption07:46 – China’s “AI Plus” play (AI + industry)10:06 – Is the US still ahead at the frontier?12:04 – GPT-5 reality check & AGI timelines20:58 – Compute decides: chips, export controls, auto-ML engineers23:04 – What we need now: transparency, audits, AI literacy28:02 – Standards in practice: de-facto beats de-jure30:56 – Next 5 years: closed peaks, open bow wave37:55 – Final take: which path wins?#OpenAI #HelenToner #ai #GPT5 #OpenSource #podcast #China #DeepSeek

Has OpenAI made the WRONG bet? Gary Marcus argues that OpenAI is taking the wrong approach - and the AI bubble is real. We walk through why GPT-5 underwhelmed, where the scaling paradigm breaks, and what might really get us to AGI.Gary explains:– Why the economics don’t add up for current AI– Why GPT-5 isn’t as good as expected– The core LLM limitations and why the scaling paradigm fails– Why AI won’t take your job in the near term– A practical path to AGI (hybrid / neuro-symbolic, world models)We also debate whether investors are over- or under-valuing AI, what productivity gains are real, and how long it will take before AI truly replaces jobs.References discussed in this episode:– Gary Marcus, The Algebraic Mind: Integrating Connectionism and Cognitive Science (2001)– Gary Marcus, “Deep Learning Is Hitting a Wall” (Nautilus, 2022)– Gary Marcus, The Next Decade in AI (arXiv, 2020)– Mike Dash, TulipomaniaDo you think Gary is right—or are we just getting started? Drop one strong piece of evidence either way (links welcome). We’ll pin the best reply. Don’t forget to like and subscribe for more unfiltered conversations on AI, tech, and society.Chapters00:00 – Is AI in a bubble?00:30 – AI hype vs. reality01:10 – GPT-5 launch: disappointment or progress?03:50 – Can AI be both a revolution and a bubble?05:40 – Productivity gains and investment hype08:00 – Gary Marcus joins the conversation09:30 – Why Gary calls himself a skeptic11:15 – GPT-5 and the limits of scaling14:00 – Financial reality of large language models17:20 – “Deep Learning Is Hitting a Wall”19:00 – Why hallucinations won’t go away21:00 – Neuro-symbolic AI explained24:00 – Building world models for AI27:00 – Are AI valuations sustainable?29:30 – Lessons from Tulipomania31:30 – Will AI take all our jobs?36:00 – What comes next for AI research38:30 – Final thoughts#OpenAI #GaryMarcus #ai #GPT5 #scaling #podcast

AI agents can’t easily talk to each other. To solve this, Cisco has joined forces with Google, Dell, Oracle, and Red Hat to launch AGNTCY, the open-source Internet of Agents. It’s designed so agents from different organizations can discover, trust, and work together - and it’s now donated to the Linux Foundation for neutral governance.Vijoy Pandey, Senior VP and GM of Outshift by Cisco, explains how AGNTCY tackles interoperability, why secure messaging (via SLIM) matters, and how enterprises are already using multi-agent systems in the real world. We also look ahead to quantum networking - the next frontier Cisco and its partners are preparing for, connecting processors and data centers across vast distances.👉 Tell us in the comments how you see open-source agents changing your industry.Visit AGNTCY.orgVisit Outshift.com Chapters00:00 Cold open00:34 Welcome and hosts01:45 Why interoperability is the bottleneck03:10 What AGNTCY enables05:55 Trust, identity, and evaluation12:40 SLIM and secure agent messaging16:45 Real-world deployments20:10 From pilots to production21:55 Open source and Linux Foundation25:20 Quantum networking outlook28:50 Final thoughts and CTAGuest:Vijoy Pandey, SVP and GM, Outshift by Cisco#AI #Agents #Cisco #AGNTCY #Interoperability #OpenSource #LinuxFoundation #Quantum #AgenticAI #Outshift

AI is changing the way we do biology. But can it crack one of the toughest challenges in science — drug discovery?In this episode of Agents of Tech, we speak with Professor Charlotte Deane, MBE, University of Oxford, one of the UK’s leading computational biologists. As a co-lead of OpenBind, Charlotte is helping build the global data infrastructure needed to accelerate drug discovery with AI.From the role of open science to the cultural gap in innovation between the US and UK, Charlotte explains why the UK, despite its size, continues to punch well above its weight in AI and computational biology.📍 Recorded live at ISMB/ECCB in Liverpool, the world’s leading computational biology conference.🔔 Subscribe to Agents of Tech for more conversations with the people shaping the future of AI, science, and society: https://www.youtube.com/@AgentsOfTech #AI #DrugDiscovery #CharlotteDeane #UKAI #Biology #AgentsOfTech00:00 Intro: AI’s toughest challenge – drug discovery 00:27 Welcome to Agents of Tech at ISMB/ECCB, Liverpool 01:00 Why is drug discovery so hard for AI? 02:00 The UK and AI: overlooked but world-class 05:57 Innovation culture: risk-taking in the UK vs US 08:00 Introducing Professor Charlotte Deane 09:12 What is the OpenBind consortium? 13:00 Pharma data vs open science data 15:59 How the UK punches above its weight in AI 19:20 Training the next generation of AI-literate scientists 22:00 How AI will change the way science is done 25:00 Can AI ask better scientific questions? 27:40 How AI is changing drug discovery workflows 32:00 Final thoughts from Charlotte Deane 34:00 Host debrief: UK AI, OpenBind, and the future of biology

Can artificial intelligence replace scientists? At Stanford University, Professor James Zou is leading research on AI scientists, virtual labs, and digital researchers that are already transforming biology and medicine.In this episode of Agents of Tech, recorded live at ISMB/ECCB in Liverpool, James explains how his lab is building virtual research teams powered by AI. These “digital scientists” act like a real lab, with specialized roles in immunology, chemistry, and computational biology. They collaborate, design experiments, and even help discover potential Covid vaccine candidates.We discuss:How AI schools train agents to become domain experts in daysWhy virtual conferences run by AI could change scientific publishingWhat James’s team learned from designing nanobody therapies with AIThe future of human and AI collaboration in science Read James Zou’s recent Nature paper on AI scientist agents: https://www.nature.com/articles/s41586-025-09442-9 Subscribe for more conversations with global AI and science leaders: https://www.youtube.com/@AgentsOfTech#AI #Science #JamesZou #Stanford #VirtualLabs #ComputationalBiology #ArtificialIntelligence #MachineLearning Chapters (time-coded)00:00 – AI isn’t just a tool, it’s becoming the scientist00:17 – Meet Professor James Zou of Stanford00:41 – What are AI “virtual labs”?01:07 – Can AI replace human researchers?02:00 – The promise and fear of agentic AI03:15 – Building AI teams with different expertise04:45 – Human creativity vs virtual collaboration05:36 – James Zou explains the concept of virtual labs06:40 – Early success: AI scientists design Covid nanobody candidates08:20 – Why virtual labs are more than large language models09:40 – Specialized AI agents with domain expertise11:05 – Human collaboration with AI scientists12:20 – Filling critical expertise gaps with AI13:45 – How virtual labs “teach themselves” through AI schools15:20 – The problem of agreeable AIs and why critics are needed17:00 – Bias in literature and how AI agents learn18:45 – Trust and experimental validation in AI science20:20 – Why human scientists still matter in the lab21:10 – Next steps for Stanford’s virtual lab research22:20 – Potential applications in biology, medicine, and beyond23:20 – The future of AI-run conferences and publishing25:10 – Explosion of research papers and the role of AI reviewers26:00 – Reactions: Are AI scientists partners or competitors?29:20 – What does AI mean for the future of human discovery?30:00 – Closing thoughts and thanks to James Zou

In this exclusive Agents of Tech interview from ISMB/ECCB in Liverpool, we sit down with Dr. John Jumper, Nobel Laureate and leader of the groundbreaking AlphaFold project at Google DeepMind.Discover how AlphaFold reshaped molecular biology by accurately predicting protein structures, and hear Jumper's vision for using AI to not just predict biology, but design it. Join us as we explore the future of computational biology, scientific reasoning with AI, and what’s next for this Nobel Prize-winning scientist. 00:00 - Introduction to John Jumper and AlphaFold00:29 - ISMB/ECCB Liverpool Overview01:00 - AlphaFold's Impact on Structural Biology02:10 - Understanding Protein Structures with AlphaFold04:14 - Google's Role in Scientific Innovation05:20 - AlphaFold’s Influence Beyond Biology05:55 - Welcoming John Jumper06:23 - Why ISMB/ECCB Conference?06:55 - How Does AlphaFold Work?08:46 - Surprising Applications of AlphaFold09:17 - Importance of Protein Databank (PDB)12:00 - Industrial Science and the Nobel Prize14:14 - Ingredients of AI: Data, Compute, Research17:38 - Collaborative Teams in Modern Science18:50 - Responsibility as a Public Intellectual22:02 - Jumper’s Next Big Research Question25:56 - Public Trust in AI31:56 - Scientific Method and AI Predictions34:01 - Concluding Thoughts#AlphaFold #JohnJumper #GoogleDeepMind #AgentsOfTech #AI #ArtificialIntelligence #MachineLearning #ComputationalBiology #StructuralBiology #ScienceInnovation #ISMB #ISCB2025 #ECCB2025 #LiverpoolScience #NobelPrize #NobelPrize2024 #NobelLaureate #Podcast

In this episode of Agents of Tech, we dive into agentic AI, a new kind of artificial intelligence designed to enhance human agency rather than automate people out of the loop.We’re joined by Dr. Niloufar Salehi, Assistant Professor at UC Berkeley and Chief Product Officer at Across AI. Her work spans healthcare, education, criminal justice, and the creative industries. She explains why most AI systems misunderstand how people actually work, and what it will take to build systems that empower rather than override human judgment.Topics include:- The rise of agentic AI and what it means for work- Case studies in medicine, law, and content creation- Why most automation fails in the real world- The hidden risks of synthetic data and algorithmic bias- What we can learn from creatives trying to outsmart YouTube’s algorithmThis is a must-watch if you’re thinking about AI and ethics, human-computer interaction, or the future of decision-making in high-stakes settings.00:00 Intro to Agentic AI 00:16 Meet Dr. Niloufar Salehi 00:24 What is Agentic AI? 00:48 Hosts Discuss Human-AI Interaction 04:00 Guest Interview Begins 04:40 HCI and Interdisciplinary Design 06:34 Algorithmic Misconceptions 07:11 Xerox PARC & HCI Origins 10:48 AI in Healthcare: What Works 13:26 Translation Risk in Medicine 16:08 AI in the Courtroom 19:02 Synthetic Data: Power & Pitfalls 21:00 YouTube Creators & Algorithm Personas 26:00 Designing Interfaces Around Human Strengths 29:00 AI in Hiring, Policing & Due Process 31:00 AI That Offers Options, Not Orders 32:30 Future of AI & Human Collaboration 📘 Plans and Situated Actions – Lucy A. Suchman• Chapter “Situated Actions” in Human Machine ReconfigurationsDOI: 10.1017/CBO9780511808418.008 (asmepublications.onlinelibrary.wiley.com, Cambridge University Press & Assessment)📘 AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference – Arvind Narayanan & Sayash Kapoor• DOI for an excerpt in Stanford Social Innovation Review: 10.48558/0Z9Z DR86 (Stanford Social Innovation Review)• DOI for a full-length review article: 10.1215/2834703X 11700273 (Cambridge University Press & Assessment, Duke University Press)📕 Study: Effect of Prior Diagnoses on Dermatopathologists' Interpretations…• DOI: 10.1001/jamadermatol.2022.0000 📕 Study: Disparities in Dermatology AI: Assessments Using Diverse Clinical Images• DOI: 10.1126/sciadv.abq6147 (Science)

Artificial intelligence is revolutionizing scientific research—but can it really replace human scientists? In this episode of Agents of Tech, we speak with Professor José Penadés and Dr. Tiago Costa from Imperial College London about their groundbreaking collaboration with Google's Gemini 2.0 AI co-scientist.Within just two days, AI unraveled a scientific mystery they'd spent years exploring. But what does this rapid success mean for human researchers and the future of science itself?🔑 Key highlights:How Google's AI co-scientist generates novel scientific hypothesesWhy AI may accelerate—but not replace—human discoveryThe surprising ways AI avoids human biases in scientific researchSubscribe for more cutting-edge insights from Agents of Tech!#AI #ArtificialIntelligence #ScienceInnovation #GoogleAI #AIResearch #AgentsOfTech. Research Paper: https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf 0:00 – Intro: The AI Era in Science0:20 – The Problem AI Solved in 2 Days0:52 – What Are AI Co-Scientists?1:16 – Can AI Generate Entirely New Hypotheses?1:43 – Hosts Discuss the Implications of AI Collaboration2:34 – Difference Between AI Bots and AI Co-Scientists3:33 – Skepticism About AI’s Scientific Creativity5:27 – Could AI Break Free from Outdated Knowledge?7:45 – Interview Start: José Penadés & Tiago Costa9:00 – How AI Avoids Human Bias in Science10:06 – AI’s Ability to Generate Original Hypotheses12:41 – Overcoming Human Scientific Biases14:03 – Will AI Accelerate Discovery or Complicate Science?15:25 – Obstacles to AI-generated Breakthroughs16:12 – Can AI Innovate Across Disciplines?18:23 – Human Intuition vs. AI-generated Hypotheses21:01 – Could AI Ever Formulate Revolutionary Ideas?23:36 – The Human Role: Asking the Right Questions25:52 – AI and the Future of Peer Review27:52 – Advice for Young Scientists Using AI29:00 – Final Thoughts: AI's Role in Future Science30:39 – Closing & Credits

Is generative AI replacing workers—or helping them thrive? In this episode of Agents of Tech, we explore how AI is transforming the global workforce. Economist Lindsey Raymond (Microsoft Research), co-author of a groundbreaking study with Erik Brynjolfsson and Danielle Li, joins us to unpack how AI tools are boosting productivity, equalizing skills, and reshaping the modern job.We cover:AI’s impact on job creation and automation (World Economic Forum’s 2025 forecast)Why AI helps junior workers most—but might demotivate top performersHow “editor over producer” is becoming the new workplace modelWhether AI narrows or widens global inequalityWhat skills will remain uniquely humanHow AI tools can power faster business scaling—and redesign the nature of jobs🎧 Featuring: Lindsey Raymond, Microsoft ResearchAutria Godfrey, Laila Rizvi & Stephen Horn, Agents of Tech hostsSubscribe for more deep dives into AI, tech, and the future of work. 🔔 Don’t forget to like, comment, and share.Papers and Reports: https://academic.oup.com/qje/article/140/2/889/7990658?login=falsehttps://www.weforum.org/publications/the-future-of-jobs-report-2025/?gad_source=1&gclid=Cj0KCQjw4cS-BhDGARIsABg4_J3j8i_TwZLC_yzLxuOzrfHphxgBT02X0IWMRTUUYM27PICz30xiJRsaAvEUEALw_wcB#GenerativeAI #FutureOfWork #ArtificialIntelligence #AIandJobs #AIAutonomy #WorkplaceTransformation #ErikBrynjolfsson #LindseyRaymond #AIProductivity #AgentsOfTech

Can AI help us survive extreme weather and climate change—or is it part of the problem?In this episode of Agents of Tech, we speak to Professor Auroop Ganguly, Director of AI for Climate and Sustainability at Northeastern University, about how artificial intelligence is being used to predict disasters, improve infrastructure resilience, and shape sustainable climate policy.🌀 We discuss:• How AI predicts floods, wildfires, and extreme weather• The rising energy cost of AI and the push for green computing• Challenges in low-data regions and climate modelling• Why AI needs governance to support sustainability goals• Using AI to build infrastructure that can withstand climate shocks🚨 AI is powerful, but not a magic bullet. Tune in for a grounded look at the promises, limits, and risks of AI in the climate fight.📍 Guest: Professor Auroop Ganguly, Northeastern University🎙️ Hosts: Autria Godfrey & Laila Rizvi📺 Produced by WebsEdge🔗 Subscribe for more on the future of AI, climate tech, and innovation.#ClimateChange #AI #Sustainability #ClimateTech #AuroopGanguly #GreenComputing #DisasterResilience #AgentsOfTech #ExtremeWeather #MachineLearning #AIClimateModels #TVA #FloodPrediction #ClimateAdaptation #NetZero #AIethics #aigovernance CHAPTERS00:33 – The climate crisis and the AI dilemma01:30 – Can AI help or harm the planet?02:45 – Guest intro: Professor Auroop Ganguly04:10 – How AI supports climate resilience06:00 – Case study: NASA-funded flood prediction with AI08:30 – Improving infrastructure with predictive models10:00 – AI’s limits in reducing emissions11:45 – Can DeepSeek lead to greener AI?13:00 – Making climate models more accurate with AI15:30 – The Global South's data gap problem17:20 – Using transfer learning in low-data regions18:40 – Combining AI with physics for better predictions20:00 – The energy demands of AI explained22:00 – Why governance matters for sustainable AI23:30 – AI's role in shaping future climate policy25:00 – Risks of bias in disaster AI systems26:30 – Innovation, equity, and evolution over revolution28:00 – Hosts’ reflections: What we learnedhttps://www.nature.com/articles/s41467-023-35968-5

We’re entering an era where AI systems don’t just follow prompts—they act independently. But what happens when we hand over too much control?In this episode of Agents of Tech, our hosts Stephen Horn, Autria Godfrey and Laila Rizvi sit down with Margaret Mitchell, Chief Ethics Scientist at Hugging Face and one of the world’s leading voices on AI ethics. Together, they explore what autonomy really means in artificial intelligence—and why we can’t afford to be passive observers.#AIEthics #ResponsibleAI #TechForGood #EthicalAI #HumanInTheLoop #AIRegulationTopics include:🔹 Why AI autonomy is fundamentally different from traditional automation🔹 The real risks of removing human oversight🔹 How trust and anthropomorphism distort our judgment🔹 What “human in the loop” must mean going forwardThis is a must-watch conversation for anyone working with AI—or impacted by it (which means all of us).https://huggingface.co/papers/2502.02649🎧 Listen now on your favorite podcast platform and subscribe for more deep tech insight. 0:00:00 - Intro: Are we giving AI too much control?0:00:17 - Meet DeepSeek and Monica – the next-gen AI agents0:00:58 - The ethics of autonomous AI0:02:13 - Guest intro: Margaret Mitchell on AI autonomy0:04:12 - Human control vs machine independence0:06:25 - AI, society, and the illusion of moral reasoning0:07:49 - Security risks from autonomous coding agents0:11:27 - The BBC analogy & user-generated chaos0:13:42 - Deepfakes, consent & harmful content0:15:45 - Why AI doesn’t think like us0:17:22 - Sensitive data, agents & social media nightmares0:19:30 - Ease vs privacy: Why people give up control0:21:00 - Good uses of agents: Accessibility & productivity0:22:30 - AI and the future of creative jobs0:24:20 - Capitalism, AGI & the wealth imbalance0:26:10 - Margaret's message to governments0:27:50 - Rights-based regulation vs restriction0:29:35 - Looking 10 years ahead: Margaret’s fears & hopes0:31:30 - Final reflections: Are we too late?0:35:30 - Outro: Like, share & subscribe!

In this episode of Agents of Tech, hosts Autria Godfrey and Stephen Horn dive deep into one of AI’s most pressing challenges: energy consumption. With the release of DeepSeek and rising concerns over compute power and costs, the race to build efficient AI is heating up.We speak to two pioneering researchers:Dr. Shreyas Sen (Purdue University), who’s developing nervous-system-inspired chips that connect wearables with ultra-low energy use.Dr. Hongyin Luo (MIT CSAIL / BitEnergy AI), whose work on Linear-Complexity Multiplication (L-Mul) may drastically cut compute cost and energy usage.Is the future of AI massive centralized data centers — or decentralized personal clouds and localized compute? And what happens when we run out of training data?👉 Don’t forget to like, comment, and subscribe to support meaningful tech discussions!⏱️ Timestamps / Chapters:00:00 - Introduction: Welcome to Agents of Tech00:20 - Why AI energy usage is an urgent issue01:00 - DeepSeek’s $6M run and the energy debate02:00 - The promise of mixture-of-experts and energy savings02:45 - Interview intro: Dr. Hongyin Luo (BitEnergy AI)03:25 - What is L-Mul and why it matters06:00 - Floating-point vs integer math in AI08:30 - Shifting compute from datacenters to the edge10:00 - Barriers to L-Mul adoption and FPGA innovation12:00 - The case for local family clouds13:30 - Moonshot idea: Stop pretraining to save energy15:00 - Interview intro: Dr. Shreyas Sen (Purdue University)15:45 - Wearable brains and nervous-system-inspired design17:30 - Conductive human body as an AI network19:00 - Brain-to-device communication breakthroughs21:30 - Data layers: cloud, edge, and leaf devices23:00 - Real-world use and commercialization of Wi-R tech24:00 - Future implications and distributed AI potential26:00 - Panel discussion: What does efficient AI really mean?28:30 - The end of training and rise of true intelligence?30:00 - From megawatt datacenters to household AI hubs32:00 - Wrap-up and reflections33:55 - Credits and thanks#ArtificialIntelligence #AI #EnergyEfficiency #GreenAI #EdgeComputing #BrainInspiredTech #WearableTech#NeuralNetworks #BodyPoweredAI #futureofai #SustainableTech #AIChips #AIInnovation #SmartWearables

What if a virtual version of you could help cure disease?In this episode of Agents of Tech, hosts Autria Godfrey and Stephen Horn explore the fascinating world of digital twins—virtual replicas of real-world entities that are revolutionizing industries, especially healthcare. From their origins in NASA’s Apollo missions to accelerating clinical trials today, digital twins are changing how we predict, prevent, and personalize treatment.Featuring Aaron Smith, founder and machine learning scientist at UnLearn, we discuss how AI-driven digital twin models are transforming clinical trials—cutting costs, speeding up timelines, and reducing the need for placebo groups.Don’t forget to like, comment, and subscribe for more deep dives into the future of tech and science. 00:00 - Welcome to Agents of Tech00:15 - What are Digital Twins?01:00 - Origins in NASA and Apollo 1301:30 - From Static to Intelligent Twins02:30 - AI-powered evolution: Science fiction becomes science fact03:45 - Applications in Medicine & Clinical Trials04:30 - Guest intro: Aaron Smith from UnLearn05:10 - How Digital Twins Accelerate Clinical Trials07:00 - The AI models behind the magic08:20 - Why focus on clinical trials, not patient care?10:15 - Reducing risk and increasing trial power12:00 - Future of Phase 3 trials13:30 - The long-term vision for digital twins in medicine15:00 - Placebo groups and statistical innovation17:00 - Machine learning in rare diseases18:20 - Regulatory frameworks: EMA & FDA alignment21:00 - What makes a disease suitable for digital twin modeling?22:30 - Pharma partnerships and proprietary data24:00 - Recap: Why this tech changes everything25:15 - Outro: Like & subscribe!

Welcome to Agents of Tech, where we explore the innovations shaping tomorrow. In this episode, we dive into how digital twins are transforming personalized medicine, featuring Terry Poon, CTO of Twin Health.From reversing disease to real-time metabolic modeling, learn how AI, wearables and data-driven empathy are creating a new era of preventative healthcare.🔗 Subscribe for more conversations with the visionaries behind tomorrow’s tech.📣 Like, comment, and share to support the show!https://diabetesjournals.org/diabetes/article/73/Supplement_1/851-P/156350/851-P-Whole-Body-Digital-Twin-WBDT-Enabled. #DigitalTwins #HealthcareAI #TwinHealth #PersonalizedMedicine #AgentsOfTech. 00:00 - Intro: What Are Digital Twins in Healthcare?00:27 - From Sci-Fi to Reality: Digital Twins Today01:14 - Meet Terry Poon, CTO at Twin Health01:54 - Understanding Unique Metabolic Responses02:31 - Why NASA’s Tech is Now Revolutionizing Medicine03:20 - The Holistic Power of Digital Twins04:22 - Real-World Use: Predicting Heart Attacks & Optimizing Hospitals05:00 - Disclaimer: Medical Info vs Professional Advice05:14 - Terry Poon on What Digital Twins Actually Do06:36 - Reversing Disease, Not Just Managing It07:33 - The Data That Powers Twin Health’s Success08:55 - Tackling the Complexity of Human Metabolism10:07 - How AI Makes Sense of 10 Billion+ Data Points11:33 - Data Quality + Quantity = Better Outcomes12:20 - Combining AI + Human Empathy in Care13:33 - Could This Approach Go Beyond Metabolic Disease?14:25 - Can AI Discover What Humans Can’t See?15:57 - Surprising Patterns: Morning vs Afternoon Exercise16:33 - What True Personalization Takes to Work17:32 - The Tight Feedback Loop That Fuels Change18:33 - Advice for AI Innovators in Healthcare19:39 - A Future Without One-Size-Fits-All Medicine20:41 - Empathy: The Underrated Key to Patient Adherence21:17 - Final Thoughts & Takeaways

Welcome to Agents of Tech! In this episode, we dive into the future of data storage with the visionary Dr. Stuart Parkin. A pioneer in spintronics and the inventor of racetrack memory, Dr. Parkin’s groundbreaking work has shaped the way we store and process data in the digital age. With AI’s insatiable demand for data, how do we keep up? Dr. Parkin shares insights into the challenges of traditional storage, the revolutionary potential of spintronics, and how racetrack memory could transform AI and computing as we know it.🔹 How does AI’s growing demand impact data storage?🔹 What is spintronics, and how does it work?🔹 Can racetrack memory revolutionize computing?🔹 How close are we to commercializing this next-gen storage technology?Tune in to find out! If you enjoy the conversation, don’t forget to like, subscribe, and share to help us bring more groundbreaking discussions to you. 00:00 - Introduction to Agents of Tech & AI’s Data Challenge00:06 - Meet Dr. Stuart Parkin & His Impact on Data Storage00:28 - How the Spin Valve Revolutionized Storage Technology01:26 - The Growing Bottleneck: AI’s Increasing Data Needs03:00 - Spintronics & The Promise of Racetrack Memory05:29 - Dr. Stuart Parkin’s Journey & Groundbreaking Innovations07:32 - Overcoming Storage Barriers & The Future of Spintronics10:06 - The Push for Energy-Efficient Computing & AI13:31 - Why Racetrack Memory is Critical for AI & Big Data16:56 - The Investment Needed to Make Racetrack Memory Mainstream18:56 - Neuromorphic Computing & The Future of AI Hardware23:39 - Final Thoughts & Closing Remarks

AI is advancing at a breakneck pace, but how will we store the massive amounts of data it requires? In this episode of Agents of Tech, hosts Autria Godfrey, Stephen Horn, and Laila Rizvi explore the growing demand for AI data storage and the innovations shaping the future.💡 We break down:- The Stargate Project, a $500 billion AI infrastructure investment- DeepSeek AI, a cost-efficient rival to ChatGPT?- The next frontier of data storage – synthetic DNA- How biology meets computing to create a more sustainable tech futureWe’re joined by Dr. Jeff Nivala, Co-Director of the Molecular Information Systems Lab (University of Washington & Microsoft), to discuss the groundbreaking potential of DNA-based storage and its role in the future of AI.🚀 If you enjoy the episode, don’t forget to like, subscribe, and share to stay up to date with cutting-edge tech discussions!📌 Follow us for more: Bluesky: @agentsoftech.bsky.socialTikTok: @Agents_of_TechInstagram: @agents_of_tech_ X/Twitter: @Agents_of_Tech🔗 AgentsofTech.ai#AI #DataStorage #DeepSeek #DNAStorage #techpodcast Chapters:00:00 - Welcome to Agents of Tech00:11 - The AI data storage crisis00:42 - The $500B Stargate Project – The U.S. bets big on AI01:22 - The rise of DeepSeek AI – Can it rival ChatGPT?02:44 - Mark Zuckerberg’s AI ‘war room’03:37 - Meet our guest: Laila from WebsEdge04:52 - The storage bottleneck – why AI outpaces traditional infrastructure06:47 - The innovation lag: Can engineers solve AI’s storage problem?08:33 - Dr. Jeff Nivala joins the discussion09:46 - How synthetic DNA can store digital data12:00 - Can DNA replace traditional data centers?14:33 - The intersection of biology and computing16:21 - DNA vs. traditional computing: A paradigm shift18:45 - Scaling DNA-based storage – When will it be practical?21:00 - The future of AI storage: Data forests instead of data centers? 23:00 - Final thoughts & wrap-upProgram Notes: https://openai.com/index/announcing-the-stargate-project/ https://fortune.com/2025/01/27/mark-zuckerberg-meta-llama-assembling-war-rooms-engineers-deepseek-ai-china/

In this episode of Agents of Tech, we explore the rapidly evolving landscape of artificial intelligence and its intersection with cybersecurity. Our host, Autria Godfrey, is joined by Dr. Siwei Lyu, Director of the University at Buffalo Media Forensics Lab, and Dr. Damon McCoy, Co-Director of the NYU Center for Cybersecurity, to discuss the challenges and opportunities presented by AI.From the dangers of deepfakes and their role in misinformation to the vulnerabilities of critical infrastructures, our guests weigh in on the pressing need for innovative detection tools, ethical AI development, and global cybersecurity collaboration. Dr. Lyu introduces his groundbreaking DeepFake-o-Meter, while Dr. McCoy highlights real-world examples of AI-enhanced cyberattacks and shares insights on safeguarding data in an era of decentralization.If you’re interested in the cutting-edge applications of AI in cybersecurity and the measures being taken to protect against its misuse, this episode is a must-watch.Don’t forget to like, subscribe, and share to stay updated on the latest in AI innovation.🔗 https://www2.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-predictions/2024/deepfake-banking-fraud-risk-on-the-rise.html🔗 https://sensity.ai/reports/#AI #Cybersecurity #Deepfakes #ArtificialIntelligence #Technology #AIForGood #CyberThreats #DataSecurity #AIResearch #TechInnovation #Misinformation #DigitalSafety #AIandEthics #FutureOfAI #DeepfakeDetection #CyberAwareness #TechPodcast #AgentsOfTech #AIInnovation #AIinCybersecurity #MediaForensics #AIForSecurityChapters:00:00 - Welcome to Agents of Tech00:08 - Introduction to AI and Cybersecurity00:44 - Exploring Deepfakes: A Modern Challenge01:50 - Guest Introduction: Dr. Siwei Lyu03:30 - How the DeepFake-o-Meter Detects Manipulated Media07:00 - Challenges in Deepfake Detection and Bias in AI Models12:40 - The Growing Threat of AI-Driven Fraud14:00 - Public Awareness and Education on Deepfakes16:50 - Ethical Use of AI: Benefits and Risks19:00 - Guest Introduction: Dr. Damon McCoy20:40 - AI in Cybersecurity: Vulnerabilities in Critical Infrastructure23:00 - The Threat of Data Poisoning in AI Systems27:10 - Real-World Examples of AI-Driven Cyber Attacks30:00 - Insider Threats and Protecting Sensitive Data34:10 - Addressing Global Cybersecurity Norms and Collaboration38:00 - Building a Culture of Cybersecurity Awareness42:00 - Final Thoughts: Aligning Incentives in Cybersecurity45:00 - Closing Remarks and Acknowledgments

Join us on this episode of Agents of Tech as we explore the transformative potential of quantum science with the brilliant Professor Erica Carlson from Purdue University. 🎙️ Discover how quantum mechanics is shaping the future of technology, tackling global challenges like climate change, energy efficiency, and more. With engaging insights into superconductors, quantum materials, and neuromorphic computing, this conversation will leave you in awe of the possibilities the Quantum Age holds. ✨ Don’t miss her inspiring journey, educational initiatives, and her unique approach to making complex quantum concepts accessible to everyone. 🗓️ Special focus on the upcoming United Nations International Year of Quantum Science and Technology 2025, celebrating 100 years of quantum mechanics. 🎧 Subscribe for more insights into the cutting-edge innovations shaping our future! Dr Erica Carlson's YouTube Channel: https://www.youtube.com/channel/UCN6ygeEojVhJqocfAxP2wqw. Richard Feynman Lectures: https://www.feynmanlectures.caltech.edu/. #QuantumScience #QuantumMechanics #Technology #Innovation #FutureTech #Superconductors #QuantumMaterials #NeuromorphicComputing #UN2025 #SciencePodcast #AgentsOfTech Chapters:00:00 - Welcome to Agents of Tech00:14 - Exploring the Quantum Age with Professor Erica Carlson00:45 - UN's International Year of Quantum Science 202502:20 - What is Quantum Mechanics?04:00 - Professor Carlson’s Journey into Quantum Physics06:50 - Superconductors and Their Quantum Effects09:30 - The Fascinating World of Liquid Crystals11:40 - Making Quantum Science Accessible: Podcasts, YouTube, and More14:05 - Particle-Wave Duality: The Beauty of Quantum Physics16:30 - Advances in Quantum Materials18:45 - The Role of Quantum Science in Solving Global Challenges21:30 - Neuromorphic Computing and the AI Revolution24:15 - How Quantum Discoveries Shape the Future27:00 - Why Quantum Technology Matters to Everyone28:45 - Closing Thoughts: Celebrating the Quantum Age

In this episode of Agents of Tech, Stephen Horn and Autria Godfrey explore the rapidly evolving world of Artificial Intelligence and ask the pressing questions: Can we trust AI? Is it safe? AI is becoming deeply embedded in every aspect of our lives, from healthcare to transportation, but how do we ensure it aligns with ethical principles and remains trustworthy?Featuring insights from:Dr. Shyam Sundar, Director of the Center for Socially Responsible AI at Penn State, who discusses the role of ethics and trust in AI systems.Dr. Duncan Eddy, Executive Director of the Stanford Center for AI Safety, who shares lessons from aerospace safety and how they apply to AI.Join us as we examine the balance between technological advancement and safety, explore the role of regulation, and dive into the psychology of trust in AI. With perspectives on global AI trends, cultural differences in trust, and what the future holds, this is a must-watch for anyone curious about AI's impact on our society. #ArtificialIntelligence #AISafety #AITrust #EthicalAI #FutureOfAI #MachineLearning #AIFuture #AIInnovation #TechEthics #AgentsOfTech00:00 - Welcome to Agents of TechStephen Horn and Autria Godfrey introduce the episode, broadcasting from London and Washington, D.C., and pose today’s critical question: Can we trust AI?02:15 - AI in Our Lives: Benefits and RisksA discussion on how AI is rapidly transforming industries like healthcare, finance, education, and transportation. But with this integration come concerns about ethics, bias, and safety.05:30 - Ethical Implications of AIExploring the challenges of making AI systems socially accountable and the ethical dilemmas arising from unchecked AI development.10:00 - Conversation with Dr. Shyam SundarDr. Sundar, Director of the Center for Socially Responsible AI at Penn State, explains how AI’s conversational nature impacts trust and how personalization can lead to both engagement and misplaced trust.15:45 - Cultural Differences in AI TrustA fascinating look at how different cultures approach and trust AI systems, highlighting the global nature of AI challenges.20:00 - Dr. Duncan Eddy on AI Safety FrameworksDr. Eddy, Executive Director of the Stanford Center for AI Safety, draws parallels between aerospace safety systems and AI, offering insights into incremental safety improvements and regulation.25:30 - Can Regulation Keep Up with AI?A discussion on global efforts like the EU AI Safety Act and challenges in regulating both AI development and deployment, especially in high-risk applications.30:15 - How to Verify AI OutputsExamining methods like adaptive stress testing and formal verification to improve AI reliability and avoid catastrophic errors in fields such as medicine and finance.35:00 - The Future of AI Safety and TrustClosing thoughts on how AI safety research is racing to keep up with innovation, the importance of fostering a culture of safety, and ensuring trustworthiness as AI becomes ubiquitous.38:00 - What’s Next on Agents of Tech?A sneak peek at the next episode, where the focus will shift to deepfakes and cybersecurity with experts from NYU and the University of Buffalo. Trust in Artificial Intelligence

In this episode of Agents of Tech, we dive into the groundbreaking advancements in AI and biotechnology with a focus on AlphaFold 3, the revolutionary AI model developed by Google DeepMind. Before AlphaFold developers Demis Hassabis and John Jumper were awarded the 2024 Nobel Prize in Chemistry for their work, this AI system had already been making waves by predicting 3D protein structures with unprecedented accuracy.Join hosts Stephen Horn and Autria Godfrey as they explore the profound impact AlphaFold is having on science, from understanding the building blocks of life to accelerating drug discovery. We speak with Dr. Luke Yates, Assistant Professor at the University of Sussex, who explains how AlphaFold 3 is changing the landscape of biological research. We also hear from Dr. Kyle Rohde of the University of Central Florida, whose lab is using AI to speed up the development of new antibiotics, particularly for drug-resistant tuberculosis.Recorded prior to the Nobel Prize announcement, this episode offers an insightful look into the future of AI-driven discoveries in biology, medicine, and beyond. Don’t miss out on this fascinating discussion!Subscribe to Agents of Tech for more on the latest breakthroughs in science and technology. #AlphaFold #AIinBiology #NobelPrize2024 #DrugDiscovery #Biotechnology #AgentsOfTech #DeepMind #ScienceBreakthroughs #Antibiotics #ProteinFolding #AIinMedicine #DeepLearning #AlphaFold3 #FutureOfBiotech #AIInnovation #SciencePodcast #AIRevolution #BiomedicalResearch #Genomics #AIForGood #DrugDevelopment #TechForHealth #ArtificialIntelligence #CuttingEdgeTech #NobelPrizeScience #MedicalBreakthroughs #TechInBiology. 00:00 - Welcome to Agents of Tech00:13 - Hosts Introduction00:19 - Exploring Life's Building Blocks & Antibiotic Development00:54 - Upcoming Guests & Topics01:00 - Interview with Dr. Luke Yates on AlphaFold 303:20 - The Significance of Protein Folding06:12 - Impact of AlphaFold on Disease Research10:30 - Challenges in Drug Discovery13:05 - Open-Source Debate on AlphaFold16:10 - Ethical Concerns of AI in Biology18:00 - Interview with Dr. Kyle Rohde on TB Drug Discovery20:30 - How Multidrug-Resistant TB Complicates Treatment23:40 - AI and Target-Based Screening for New Antibiotics27:40 - The Future of AI-Driven Drug Discovery31:00 - Closing Remarks & Next Episode Preview32:00 - Subscribe to Agents of Tech

In this episode of Agents of Tech, hosts Stephen Horn and Autria Godfrey explore the intricate relationship between mental health and technology. As social media, gaming, and digital devices become more pervasive, they discuss how these technologies are impacting youth and adults, often leading to addiction and mental health challenges. We hear insights from leading experts: • Dr. Marc Potenza (Yale University) and Dr. Sanya Virani (Indiana University) discuss the intersection of social media, gaming, and gambling addiction. • Dr. Wendi Waits (Talkiatry) explains how digital technology addiction is leading to ADHD-like symptoms in adults. • Darlene King (UT Southwestern) shares how AI chatbots are being used to address mental health needs, and the importance of psychiatrists in their development. • We also discuss the alarming warning from US Surgeon General Vivek Murthy, who highlights the growing youth mental health crisis caused by social media, and what parents and companies can do to help mitigate the risks. Key topics include: Join us as we explore the solutions and challenges at the intersection of mental health and technology. Don’t forget to like and subscribe for more insightful episodes on technology, innovation, and society! Program Notes https://www.pewresearch.org/internet/2024/03/11/how-teens-and-parents-approach-screen-time/ https://www.nytimes.com/2024/06/17/opinion/social-media-health-warning.html https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170001/ Striatal dopamine synthesis capacity reflects smartphone social activity https://www.today.com/video/how-screens-and-phones-can-impact-a-child-s-mental-health-196047429741 https://www.aap.org/en/patient-care/media-and-children/center-of-excellence-on-social-media-and-youth-mental-health/qa-portal/qa-portal-for-parents/ https://www.psychiatry.org/psychiatrists/practice/mental-health-apps/the-app-evaluation-model https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10242473/

Hosts Stephen Horn and Autria Godfrey explore the growing energy demands driven by machine learning and AI. As we navigate through the challenges of powering the future, we discuss the rise of data centers, the promise of photovoltaic solar energy, and the potential of nuclear fusion.Join us as we interview leading experts, including Henry Snaith from Oxford University on revolutionary solar cell materials, and Mark Hersam from Northwestern University on neuromorphic computing. We also dive into the world of quantum computing with insights from D-Wave's Murray Thom.Are these technologies the key to a sustainable future? Can we meet the surging energy demands while keeping the planet green? Tune in to find out!🔔 Don't forget to like, subscribe, and hit the notification bell to stay updated with the latest in tech innovations! Program Notes: Goldman Sacs AI Energy report: https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand. EPRI AI Energy Report: https://www.epri.com/research/products/000000003002028905

Welcome to Agents of Tech! Join hosts Autria Godfrey and Stephen Horn as they dive into the TechBio revolution. Discover how AI and Machine Learning are transforming drug discovery and healthcare. Hear insights from Jesse Ehrenfeld, President of the American Medical Association, on digital health, data privacy, and the future of AI in medicine. Plus, an exclusive interview with Chris Gibson, CEO of Recursion, on the cutting-edge advances in TechBio.

Join us for an enthralling episode of Agents of Tech as hosts Stephen Horn and Autria Godfrey sit down with eminent figures such as 2023 Physics Nobel Laureate Pierre Agostini and leading experts from Yale and the US National Institute on Aging (NIA). Delve into Chiara Mingarelli’s insights on pulsar timing arrays and hear from Richard Spencer about pioneering quantitative MRI technologies to understand the aging brain. Explore the frontiers of a quantum internet with Stephanie Wehner of Delft University of Technology and discover the potential of these technologies to transform medicine and communications.Whether you're passionate about gravitational waves, curious about galaxy formations, or interested in the potential of quantum technologies to revolutionize our world, this episode provides a deep dive into the minds shaping the future of science and technology.🔗 Don't forget to subscribe for more updates from the cutting edge of scientific research. Available on YouTube, Apple Podcasts, Spotify, or your favorite podcast platform!

Welcome to Agents of Tech, where hosts Autria Godfrey and Stephen Horn explore cutting-edge technologies. In this episode, fresh from the American Physical Society's annual meeting, they share conversations with pioneers in quantum physics. Don't miss Stephen's interview with Stuart Parkin, MPI Halle, who revolutionized memory technology, and a captivating chat with Nobel laureate Moungi Bawendi, MIT, on quantum dots. Tune in for an enlightening discussion!

Welcome to the Visual Frontier of Science and TechnologyAgents of Tech: Deep Dive into InnovationThe podcast you've trusted to connect you with the leading minds in science and technology is now streaming with video on YouTube and Spotify!Join us as we uncover the revolutionary ways technology is reshaping our world, from the microcosm of medical breakthroughs to the macrocosm of global data networks.Featuring Special Guests:- 2023 Physics Nobel Prize Winner Anne L'Huillier, unraveling the secrets of attoseconds and changing how we perceive time and light itself.- Leaders of the American Physical Society, Jonathan Bagger and Young-Kee Kim, who are steering scientific endeavors to tackle our world's most pressing challenges.- Allison Patteson of Syracuse University, freshly honored with the prestigious Maria Goeppert Mayer Award, discusses her pioneering research in biomechanics and her impactful journey in physics.**👉 Subscribe Now!**- Don’t miss an episode. Subscribe to **Agents of Tech** on YouTube and Spotify. Tap the notification bell on YouTube to get alerts for new episodes.📆 **New Episodes Monthly** – Stay updated and inspired as we bring you the stories that are changing our understanding of the world we live in.

Ajay Gupta CEO of HSR Health and Este Geraghty, Chief Medical Office at Esri discuss how tech can be used to fight new and ongoing health threats. Dr. Alán Aspuru-Guzik, a professor of chemistry and computer science at the University of Toronto talks about creating labs of the future to accelerate scientific discovery. He also discusses the pressing question of whether AI can make a good cup of tea.

We all have extraordinary genetic similarity, but this allows the investigation of the small but impactful variations between populations that influence disease risk and resilience. We spoke to Brendan Lee, President of the Americn Society of Human Genetics about delivering on the promise of genetic and genomic medicine. Dr. Rosario Isasi, from the University of Miami Miller School of Medicine talks about societal attitudes towards scientific innovation and the factors shaping technological uptake, public engagement, and support.

What is AI's role in the fight against infectious diseases? Chair of Harvard Medical School’s Department of Biomedical Informatics, Isaac Kohane gives his thoughts. Dr Athena Starlard-Davenport, from the University Tennessee and Dr. Charles Rotimi, from the National Human Genome Research Institute discuss why population data needs to represent people of different genders and ethnicities in an equitable way and how we can all benefit if they do.

Can intensive care harness the potential of AI, can heart imaging merge with therapy and how can quantum science bout expand the potential of nuclear medicine. Professor Jean-Louis Vincent from Erasme University Hospital in Brussels, Dr James Thackeray from Hannover Medical School in Germany and Professor Taiga Yamaya, from the Institute of Quantum Medical Science in Japan, take a deep dive into technology and medicine.

Can the automotive industry decarbonise, can diversity drive innovation in vehicle design and how will quantum technology change our transport systems? Agents of Tech explores these issues with Dr. Kelly Senecal of Convergent Science, Dipti Vachani of Arm and Dr. Richard Wagner from Oak Ridge National Laboratory.

NASA Astronaut Megan McArthur gives advice on a career in space. AI pioneer, Rama Chellappa from Johns Hopkins University tells us why we can trust artificial intelligence and George Malliaras from the University of Cambridge discusses the development of bioelectronic devices for neurological disorders.

In this episode of Agents of Tech, Stephen Horn and Autria Godfrey speak to Gabriela Gonzalez from Louisiana State University about what she describes as “physics magic” – gravitational waves and we also hear from 2022 Physics Nobel Laureate Anton Zeilinger, about his work in quantum entanglement.

In Agents of Tech, Stephen Horn and Autria Godfrey look at exciting new technologies shaping our world today. In this episode, they speak to Pablo Jarillo-Herrero from MIT Physics. He is studying magic-angle graphene and its properties and he’s made an important breakthrough. Sidney Nagel from The University of Chicago talks about finding beauty in disorder.

In Agents of Tech our brand new science and technology podcast, hosts Stephen Horn and Autria Godfrey hear from Albert-László Barabási, a network scientist whose work includes revealing the structure of the brain, to treating diseases using network medicine, to how networks affect the economy and even climate change. And we’ll hear from Brad Marston about how theoretical and computational physics can help us better understand quantum matter.