In the third of a series of six podcast episodes looking at the National Security Commission on AI 's first quarter recommendations to Congress, we speak with Katharina McFarland, NSCAI Commissioner and 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 to Congress 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, the Director of Google Cloud AI and NSCAI commissioner about his line of effort’s recommendations to increase AI R&D funding.

Vittorio Sebastiano an assistant professor of stem cell biology at Stanford University, has recently demonstrated that old or damaged cells can be returned to their youthful state with gene expression techniques. While this doesn't mean that we're on the road to immortality, it does suggest possible treatment for a host of diseases. Vittorio talked about the mechanisms at work in the process, his hope for in vivo human trials in the coming years, and 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 an academic member at Mila, Quebec’s AI institute, 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.

There has been a raging debate in the past few years between the symbolists and the connectionists about the future of artificial intelligence. The symbolists say that traditional, explainable, logic-based approaches still hold tremendous promise while the connectionists say that the power of deep learning holds the key to more general forms of machine intelligence. This week, I speak with David Cox, IBM Director of the MIT-IBM Watson AI Lab, which is blending the two traditions in what they call neuro-symbolic AI in hopes to move AI forward.

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. We talked with Eric Schmidt and Bob Work and Kaifu Lee. 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 accurately 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.

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. We talked about how she and her colleagues accomplished that feat and her work 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 dirty little 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. In order to efficiently do that work, in order for data science teams to work with each other and with labelers around the world, they need a platform and tools. 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.

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 and integrity of blockchain and the privacy and security offered by trusted execution environments to give data owners the ability to control their data and audit how it is used.

A few months ago, at the recent International Conference on Machine Learning, a workshop and research paper launched a movement to use machine-learning to either help mitigate global warming or help humans adapt to a warming world. This week I talk to David Rolnick, a postdoc at UPenn, and Priya Donti, a PhD student at Carnegie Mellon, who are engaged daily in maintaining its momentum. We talked about how the group came together and how climatechange.ai is developing and what you can do to get involved.

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, in the first of a periodic series of sponsored episodes, 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.

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This week, we look at AI in India. With its massive population, fast-growing economy, English-language education and large supply of brilliant researchers and engineers, it should be competing with China and the U.S. for dominance in the space. But it is not. I talk to Partha Talukdar, a professor at the Indian Institute of Science, Bangalore, about the challenges that have kept India from realizing its AI potential.

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I talk this week to Yann Lecun, one of the brightest minds in machine learning today. Yann's work lies behind some of the most critical AI applications, notably computer vision systems that power everything from face recognition software to self-driving cars. He recently won the Turing Award, the highest prize in computer science. We talked about Yann's first computer, about how music led him into computer science, and about his work on self-supervised learning, which he believes will take us to human-level intelligence in machines.

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There is an implicit race underway among the world’s great powers, particularly China, Russia and the United States, to enhance their militaries with AI. To understand this, I talk this week with Trae Stephens and Brian Schimpf from Anduril Industries, an AI defense contractor. We talked about the current state of AI research and deployment for national security, including how the US stacks up against China. We also talked about the resistance among US engineers to work on defense applications and whether that hobbles the US in the global AI arms race.

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