Ilya Sutskever, a cofounder and chief scientist of OpenAI and one of the primary minds behind the large language model GPT-4 and it’s public progeny, ChatGPT, talks about AI hallucinations and his vision of AI democracy.

In this episode, Ben Sorscher, a PhD student at Stanford, talks about reducing the size of data sets used to train models, particularly large language models, which are pushing the limits of scaling because of the enormous cost of training and the environmental impact of generating the electricity they consume. 

Yann LeCun talks about what's missing in large language models and about his new joint embedding predictive architecture may be a step toward filling that gap.

Terry Sejnowski, an AI pioneer, chairman of the NeurIPS Foundation, and co-creator of Boltzmann Machines - whose sleep-wake cycle has been repurposed in Geoff Hinton's new Forward-Forward algorithm, talks in this episode about the NeurIPS conference, and how advances in deep learning may help us understand our own brains.

Geoffrey Hinton gives a deep dive into his new learning algorithm, which he calls the forward-forward algorithm, a more plausible model for how the cerebral cortex might learn.

To set the stage for some terrific conversations I have coming to you in the new year, in this episode we go back to some earlier conversations that talk about how we got to where we are in deep learning and how those early threads continue to lead innovation.

This week I talk to Bob Rogers, a Harvard trained astrophysicist who once built digital twins of black holes to better understand them, and now builds digital twins of supply chains to help make them more efficient and resilient.

Jonathon Reilly, co-founder of Akkio, a no-code AI platform, talks about how users with a web browser and an idea have the power to bring AI to life themselves without having to write code.

Moses Guttmann, founded ClearML, talks about the evolution of the MLOps industry over the past few years and ClearML's contribution to it.

Bratin Saha, head of Amazon's machine learning services, talks about Amazon's growing dominance in model building and deploying AI, about the company's SageMaker platform, and whether anyone can compete with the behemoth. 

Peter Schrammel, one of the founders of Diffblue, an automated unit-test writing software company, speaks about the increasing automatic generation of code and how he sees such automation increasing the productivity of developers.

Michael Kearns, a computer scientist professor at the University of Pennsylvania and an Amazon scholar talks about differential privacy, how Amazon's research approach differs from its peers, and how AI will eventually permeate all aspects of our lives. 

Jacki Morie, a senior XPRIZE advisor, talks about the ANA Avatar XPRIZE, a competition focused on creating a physical avatar system that will seamlessly transport human skills and experience to distant locations. The four-year competition is in its final stretch.

Aaron Patzer, founder of the personal finance app MINT and more recently founder of the AI-based healthcare company Vital, talks about keeping customer data private and the promise, giving emergency room patients information with AI and finding friendly solutions to anxiety-producing problems.

Rohit Prasad, Amazon's Senior Vice President and Head Scientist for Alexa, speaks about the development of conversational AI and virtual assistants and the merging of IoT sensor data into ambient intelligence - AI that is always present and immediately accessible.

Ken Washington, who leads Amazon's consumer robotics team, talks about the company's compact wheeled robot called Astro. Ken talked about Astro's evolution, it's popular and possible use cases, and what might be in store in the future. 

Almammon Rasool Abdali, a machine-learning engineer and PhD student in Baghdad, is one of the more prominent members of Iraq's small but growing deep learning community. We talked about his work, which involves vision systems to detect violence, and about the state of artificial intelligence research and teaching in the country generally. 

Oriol Vinyals, who leads DeepMind’s deep learning team, talks about AlphaCode, his group’s code-writing language model, and DeepMind’s winding road toward artificial general intelligence.

Andrew Ng, founder of Google Brain, Coursera and Landing AI, talks about his vision of data-centric AI, MLOps and the future of supervised vs unsupervised learning. The Eye on AI podcast is sponsored by ClearML.

Tom Siebel, founder and CEO of C3.ai talks about AI projects including military target acquisition and precision healthcare while musing about the dark side of our technological future. The Eye on AI podcast is sponsored by Clear.ML

Max Bileschi, a software engineer at Google Research, talks about his team’s application of convolutional neural networks to predict the function of amino acid sequences in a protein. Eye on AI is sponsored by Clear.ML. 

Vladlen Koltun discusses intelligent locomotion in robots and drones and reveals the Megaverse. The Eye on AI podcast is sponsored by Clear.ML, the open-source MLOps solution.

Connor Leahy, one of the minds behind Eleuther AI and its open-source large language model, GPT-J, talks about the building such models and their implications for the future.

Robert O. Work, former Deputy Secretary of Defense and recently co-chairman of the National Security Commission on AI, talks about competition between the US and China to integrate AI into their military capabilities.

Stephen DeAngelis, head of Enterra Solutions, reminds us that so-called Old-Fashioned AI continues to be a powerful tool. He talked about leveraging knowledge bases, inference engines and symbolic logic to make decisions about large dynamic systems. 

Andrew Feldman, one of the founders and CEO of Cerebras Systems, talks about the company's wafer-scale computer chip optimized for machine learning and about the network of chips that company has built that has as much computing power as a human brain.

Adobe’s head of research, Gavin Miller, talks about AI-enhanced creativity, guarding against manipulation of visual media and his own AI-enabled robot snakes.

Seth Dobrin, chief AI officer at IBM, talks about the company's tools to increase the trustworthiness, fairness and explainability of AI models.

This week, I talk to Ron Schmelzer and Kathleen Walch, founders of Cognilytica, a research, advisory, and education firm focused on artificial Intelligence. We talked about the rise of MLOps, data labeling, unsupervised learning and what it will take to get us to human-level artificial general intelligence.

This week I talk to the inimitable Ben Goertzel, about his non-profit foundation, SingularityNET, his work with the robotic head, Sophia, his talking Philip K. Dick avatar, his work in medical AI, his frustration over Big Tech's dominance of AI research and his own quest for AGI.

AI commentator and investor Kaifu Lee talks about his book AI 2041 in which he considers possible futures for humanity if deep learning applications develop as planned.

NSCAI staff Justin Lynch, Ryan Carpenter and Lance Lantier talk about how the 1986 Goldwater Nichols Act that reorganized the US military inspired the commission’s recommendations to reorganize the Department of Defense to drive adoption of emerging and disruptive technology capabilities.

NSCAI staff Christie Lawrence and Rama Elleru talk about how intellectual property protections play an unexpected role in guiding US innovation and contribute to the global competition with China for dominance in AI.

NSCAI staff Tess Deblanc-Knowles and Mike Garris talk about the steps the US government needs to take to foster AI innovation in the years ahead.

NSCAI staff Paul Leka and Christie Lawrence talk about forming an international emerging technology coalition to strengthen and coordinate the use of emerging technology for democratic ends.

NSCAI staff Mike Garris and Mike Jackson spoke about what needs to be done to lay the foundations for an AI-infused national security going forward.

NSCAI staff Aristotle Vainikos and Parker Wild spoke about the commission’s recommendations for establishing public-private innovation hubs around the country that can encourage and tap the entrepreneurial potential between the coasts and expand the participation of private enterprise in national security.

NSCAI staff Mike Jackson and Darren Wright talk about the commission’s recommendations for bringing the intelligence community up to speed with AI. They spoke about the organizational changes needed and the culture shift required to ensure that the intelligence community is integrating AI in its work.

NSCAI staff David Kumashiro and Courtney Barno talked about the commission’s recommendations on democratizing AI for the warfighter and enabling bottom-up AI innovation.

NSCAI staff Kevin McGuinness and Raina Davis talked about the commission’s recommendations for increasing public visibility about the government’s AI needs and lowering the barrier to entry for companies that want to help meet those needs.

NSCAI staff Chris Rice and Courtney Barno talk about what the US has to do to get up to speed on its AI military capabilities by 2025 in order to outpace our competitors, particularly China.

NSCAI staff Justin Lynch, Lance Lantier and Shaantam Chawla, talked about educating the warfighter for the AI era. They spoke about what kind of training warfighters need to effectively integrate AI, from commanders to infantry men and women and how that integration will change the nature of warfare and the military’s perception of itself in the years ahead.

Ylli Bajraktari, executive director of the National Security Commission on AI, talked about what he considers the most important recommendations in the commission’s final report and about the need for Congress and the White House to act swiftly to counter China’s concerted efforts to beat the US in deploying this critical technology around the world.

This week, I talk to Cade Metz, the New York Times' lead reporter on artificial intelligence, about his new book, “Genius Makers, the Mavericks Who Brought AI to Google Facebook  and the World."  The book focuses on some of the personalities and companies responsible for the current wave of AI innovation and touches on some of the controversies swirling around the new technology today.

Kent Walker, who oversees the Google's AI ethics and responsible AI innovation, talks about how Google reviews AI systems for trust and safety and the outlook for safe AI going forward. 

Director of the Pentagon’s Joint AI Center, Lieutenant General Michael S. Groen talks about improving war fighting and the challenge of China.

David Yi and Yohan Lee from Riiid Labs, the leading startup applying machine-learning to education, talk about making personalized instruction available to anyone in the world with an internet-connected device. They spoke about the family of algorithms behind their system and the recently concluded Riiid AIEd Challenge, a Kaggle competition that engaged more than 3,400 teams across the world in developing knowledge tracing algorithms.

Turing award winner Yoshua Bengio and his colleague Julian Serban, co-founder of Korbit, an AI ed-tech startup with the aim of democratizing education, talk about enhancing education through the application of deep learning systems that can track student behavior predict their performance and deliver strategies to both improve performance and prevent students from losing interest.

Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to me about his work In 2020 and what he sees on the horizon for AI.

Eric Horvitz, chief research scientist at Microsoft and a commissioner on the National Security Commission on AI, talks about ensuring interoperability of AI systems with US allies  and working to make all AI systems reliable.

An open-source compiler called TVM helps data scientists optimize their model’s performance on specific hardware.

NSCAI commissioner Steve Chien, technical supervisor of the artificial intelligence group at NASA's Jet Propulsion Laboratory, talks about the growing demand for AI solutions in space, from coordinating an increasing number of earth-orbiting objects to protecting critical communication satellites from attack.    

Jason Matheny, a commissioner at the National Security Commission on AI and head of Georgetown University’s Center for Security and Emerging Technology, talks about coordinating AI developments with NATO and making India the focus of the US's Indo-Pacific AI strategy to counter China.

Alex Ratner, an assistant professor at the University of Washington and a cofounder and CEO of Snorkel AI, talks about programmatically labeling training data for supervised learning models.

In the second episode of a series on the machine-learning pipeline, Joe Hellerstein, a professor at UC Berkeley, talks about data wrangling. 

Turing Award winner David Patterson talks about the end of Moore's law and the evolution of computer chips from general purpose CPUs to GPUs to the recent Cambrian explosion of specialized AI chips, including Google's TPU, in the first episode of five about the machine-learning model-development pipeline with Ameet Talwalkar, co-founder of Determined AI, an open-source platform for ML model development.

NSCAI commissioner Gilman Louie talks about great power competition in the digital age and the US need for a more technologically literate diplomatic core to counter China's increasing diplomatic efforts on the technology front.

Ken Ford, an NSCAI commissioner, talks about turning AI from a tool to a teammate and unshackling research and implementation of AI from government bureaucracy in order to compete with nimbler countries such as China.

Eric Horvitz, Chief Scientific Officer at Microsoft and a commissioner with the National Security Commission on AI, talks about the commission’s recommendations that responsible and ethical uses be a primary consideration in any AI system for national security.

Jason Matheny, an NSCAI commissioner who now leads a think tank at Georgetown University focused on the intersections of technology, policy and national security,
spoke in this week’s episode about the Commission’s recommendations on export controls for sensitive technology and the difficult problem of protecting digital assets like algorithms and data.

Mignon Clyburn, an NSCAI commissioner and longtime regulator at the FCC, talks about the Commission’s recommendations to increase competency in artificial intelligence at the Department of Defense and other government agencies. Ms. Clyburn talked about establishing a National Reserve Digital Corps with a scholarship program modeled after the ROTC and the creation of a United States Digital Service Academy similar to mint a new class of civil servants with deep technical knowledge.

The Alexa Prize Socialbot Grand Challenge is a competition for university students to create a social bot that can converse coherently and engagingly with humans.  This year's prize goes to the team from Emory University in Atlanta, Georgia. I speak with Prem Natarajan, vice president of natural understanding in the Alexa AI organization, about the prize, the evolution of conversational AI, its current challenges and its future promise

Qualcomm Technologies is powering the shift to fifth generation wireless communications, known as 5G. In this sponsored episode, I speak to two of the company's product managers leading the company's efforts to use AI to enhance 5G, reduce on-device power consumption, and give developers the tools necessary to make devices smarter and faster.

This week I speak to Lieutenant General Jack Shanahan, recently retired Director of the Pentagon’s Joint Artificial Intelligence Center,or JAIC. He was instrumental in starting Project Maven to integrate state-of-the-art computer vision into drone technology. He then started the JAIC, the central hub for the military’s AI efforts. Gen. Shanahan spoke about the challenges of nurturing innovation within a rigid and multilayered organization like the DOD and the threats the US faces ahead.

This week I speak to Stanford professor Fei-Fei Li, one of the people responsible for the current AI revolution. Fei-Fei talked about her early days running a New Jersey dry cleaner to finance her Princeton education, her creation of ImageNet, the world's first large labeled image data set, which allowed the validation of neural networks, and her latest work on ambient intelligence, which promises to transform elder care. 

Pietro Perona, a professor at the California Institute of Technology, is one of the brains behind a pair of smartphone apps that help you do just that: eBird and iNaturalist. But simple as the apps are to use in identifying tens of thousands of species, the science behind them is complex. Gathering enough examples for each species to train a neural network is impossible, so much of Prof. Perona’s work has been focused on making machines more efficient learners, requiring less training data.  

In this week's episode, the last in a series of six, we talked to Eric Horvitz. Commissioner at the National Security Commission on AI and Chief Research Scientist at Microsoft, about the commission's recommendations to Congress about the need for training, standards and documentation to govern the application of AI in the national security space.

In this week's episode, we talk with NSCAI Commissioner Gilman Louie about line of effort’s recommendations to Congress on how to maintain US leadership in AI hardware and 5G. He spoke about the need for a national microelectronic strategy and policies that would advance spectrum sharing for 5G to create a global alternative to the Chinese telecommunications giant Huawei.

NSCAI Commissioner Jason Matheny talks about his line of effort regarding cooperation on AI with key allies and partners. He spoke about his group's recommendations that the government establish a senior national security point of contact for AI and convene a multilateral working group for AI collaboration and interoperability - as well as the need for AI war games.    

In the third episode of six episodes looking at the National Security Commission on AI 's first quarter recommendations to Congress, we speak with Katharina McFarland, a former Assistant Secretary of Defense for Acquisition, about the commission's recommendation that the Department of Defense and the Office of the Director of National Intelligence establish a steering committee on emerging technology to ensure that AI for national security gets top priority in the years ahead.

In this week's episode, we speak to Jose-Marie Griffiths, a commissioner on the National Security Commission on Artificial Intelligence, about the commission’s recommendations on how to strengthen the federal government's AI workforce. The recommendations focused on raising understanding of AI within the government and the need to streamline government hiring practices in order to attract and retain talent.

In April 2020, the National Security Commission on Artificial Intelligence issued its first-quarter recommendations to Congress, covering seven lines of effort, six of which are public and one of which is classified. In the first of a series of podcast episodes about those recommendations, we spoke with Andrew Moore, a professor at Carnegie Mellon University and NSCAI commissioner about the commission’s recommendations on increased AI R&D funding.

COVID-19 continues to sweep through the human population, killing some and damaging the health of others. While this podcast is normally focused on machine-learning, this week I talk to Vittorio Sebastiano, an assistant professor of stem cell biology at Stanford University, about groundbreaking tech that could someday help restore scarred tissue to pre-COVID health. Vittorio talked about his hunt for machine-learning collaborators to understand the process further. 

COVID-19 has swept across the world was startling speed, but with equally startling speed, the machine learning community has responded. This week I speak with Irina Rish, a professor at the University of Montreal and a Mila academic member, who is helping head a task force to understand the virus. She talked about where the efforts currently stand and where they expect to go in the weeks and months ahead.

Justin Gottschlich, who founded the machine programming research group at Intel Labs, explains his group's efforts to automate software development. The ambition is to make it possible for anybody to create software simply by describing what they intend the software to do. 

This week I talk to Casimir Wierzynski, a senior director in Intel’s AI Products Group, Cas talked about his work in privacy, taking me on a tour of the latest strategies that promise to unlock the data necessary to liberate AI. He talked about hardening encryption against the code-cracking power of quantum computers and about his work in connectomics with salami slicers for the brain that are making it possible to map the neural networks of our minds.

Terry Sejnowski, author of the book Deep Learning Revolution, who together with Geoff Hinton created Boltzmann machines, a deep learning network that has remarkable similarities to learning in the brain, talks about whether machines dream and the algorithms of the brain, whether Marvin Minsky was the devil and how deep learning is shaping the future of education. 

We begin 2020 by looking back at some of the highlights from 2019 including conversations with Turing award winners, Yoshua, Bengio and Yann Lecun, as well as with the father of reinforcement learning, Rich Sutton. Our guests consider applying machine learning to the climate crisis; competition between the U S and China for dominance in AI; and the future of machine learning through various kinds of unsupervised learning.

Daphne Koller, formerly at Stanford University and cofounder of the online education company, Coursera, talks this week about using machine-learning to develop new drugs. Her approach is to use machine learning to accuratley identify cellular or genetic targets for treatment. The field is just getting started but promises to speed the development of new and better therapies to treat disease.

My guest this week, Aude Billard from Switzerland’s Learning Algorithms and Systems Laboratory, blends control theory with machine learning to build robotic systems that are both swift and precise but can handle some of the unpredictability of the real world. Her lab famously taught a robot arm to catch a tennis racket looping through the air and is working on ever more precise robots that can even do the work of Switzerland’s famous watchmakers.

Former Google chief executive Eric Schmidt and former Deputy Defense Secretary Bob Work, co-chairs of the U.S. National Security Commission on AI, talk about the challenges the government faces in winning support from a skeptical private sector and in maintaining engagement with China while ensuring that that engagement doesn’t work to America’s detriment.

The secret in much of artificial intelligence today is that it depends on hordes of unskilled workers to label the data used to train supervised learning models. But, in order for data science teams to work with labelers around the world, they need a platform. This week, in the second of a periodic series of sponsored episodes, I talk to Manu Sharma and Brian Rieger, who saw the opportunity to provide that platform and founded Labelbox, the leading labelling software in the space.

This week, I talk to Dawn Song, one of the world’s foremost experts in computer security, about her vision of a new paradigm in which people control their data and are compensated for its use by corporations. Dawn, a professor at the University of California, Berkeley, has recently launched a company, Oasis Labs, which is building a platform that brings together the immutability of blockchain and the privacy of secure enclaves to give data owners the ability to control their data.

A few months ago at the recent international conference on machine learning, a workshop and research paper launched a movement to use machine learning in addressing climate change. The response was huge and has given birth to the bones of an organization climate change.ai. This week I talked to David Rolnick, a postdoc at U Penn and Priya, Donti, a Phd student at Carnegie Mellon, about how the group came together and about how the organization is developing.

Automated machine-learning tools – or tools that automate the creation of machine-learning applications – are increasingly important in the current talent-scarce environment. Expensive ML engineers shouldn't spend their time doing stuff that machines can do quicker and cheaper. This week, we talk to Evan Sparks and Ameet Talwalkar, two of the founders of Determined AI, which builds tools that streamline workflows for machine-learning teams and, the company hopes, will eventually democratize AI.

This week, I talk to Brendan McCord, who wrote the Pentagon’s AI strategy and is now a Special Government Employee at the National Security Commission on AI. Brendan talks about what he believes the US needs to do to stay competitive with China and promote an alternative vision of AI-powered security and prosperity to the world.

This week I enter the ‘smelliverse,’ the dimension of odors, with Alexei Koulakov, a professor at Cold Spring Harbor Laboratory. Alexei uses machine learning to explore the least understood of our senses: the sense of smell. He’s working to unlock the mechanism by which we perceive odors and to classify millions of volatile molecules by their smell.

This week I talk to John Platt, a Distinguished Scientist at Google, about twin problems: finding cheap zero-carbon energy sources and mitigating global warming. John is a polymath, having discovered asteroids, helped put the touch in computer touchpads and even won an Academy Award for scientific and technical achievements in computer animation. Now, he is part of a growing movement of machine learning researchers tackling climate change.

This week we return to the world of thinking robots with Chelsea Finn, one of the youngest experts in the field, who talks about her journey, about her work in meta-learning and about lifelong learning for robots.