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 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.