55. Rohin Shah - Effective altruism, AI safety, and learning human preferences from the state of the world
Towards Data Science
10/28/20
•51m
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If you walked into a room filled with objects that were scattered around somewhat randomly, how important or expensive would you assume those objects were?
What if you walked into the same room, and instead found those objects carefully arranged in a very specific configuration that was unlikely to happen by chance?
These two scenarios hint at something important: human beings have shaped our environments in ways that reflect what we value. You might just learn more about what I value by taking a 10 minute stroll through my apartment than by spending 30 minutes talking to me as I try to put my life philosophy into words.
And that’s a pretty important idea, because as it turns out, one of the most important challenges in advanced AI today is finding ways to communicate our values to machines. If our environments implicitly encode part of our value system, then we might be able to teach machines to observe it, and learn about our preferences without our having to express them explicitly.
The idea of leveraging deriving human values from the state of an human-inhabited environment was first developed in a paper co-authored by Berkeley PhD and incoming DeepMind researcher Rohin Shah. Rohin has spent the last several years working on AI safety, and publishes the widely read AI alignment newsletter — and he was kind enough to join us for this episode of the Towards Data Science podcast, where we discussed his approach to AI safety, and his thoughts on risk mitigation strategies for advanced AI systems.
Previous Episode
Reinforcement learning can do some pretty impressive things. It can optimize ad targeting, help run self-driving cars, and even win StarCraft games. But current RL systems are still highly task-specific. Tesla’s self-driving car algorithm can’t win at StarCraft, and DeepMind’s AlphaZero algorithm can with Go matches against grandmasters, but can’t optimize your company’s ad spend.
So how do we make the leap from narrow AI systems that leverage reinforcement learning to solve specific problems, to more general systems that can orient themselves in the world? Enter Tim Rocktäschel, a Research Scientist at Facebook AI Research London and a Lecturer in the Department of Computer Science at University College London. Much of Tim’s work has been focused on ways to make RL agents learn with relatively little data, using strategies known as sample efficient learning, in the hopes of improving their ability to solve more general problems. Tim joined me for this episode of the podcast.
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56. Annette Zimmermann - The ethics of AI
November 4, 2020
•56m
As AI systems have become more powerful, they’ve been deployed to tackle an increasing number of problems.
Take computer vision. Less than a decade ago, one of the most advanced applications of computer vision algorithms was to classify hand-written digits on mail. And yet today, computer vision is being applied to everything from self-driving cars to facial recognition and cancer diagnostics.
Practically useful AI systems have now firmly moved from “what if?” territory to “what now?” territory. And as more and more of our lives are run by algorithms, an increasing number of researchers from domains outside computer science and engineering are starting to take notice. Most notably among these are philosophers, many of whom are concerned about the ethical implications of outsourcing our decision-making to machines whose reasoning we often can’t understand or even interpret.
One of the most important voices in the world of AI ethics has been that of Dr Annette Zimmermann, a Technology & Human Rights Fellow at the Carr Center for Human Rights Policy at Harvard University, and a Lecturer in Philosophy at the University of York. Annette is has focused a lot of her work on exploring the overlap between algorithms, society and governance, and I had the chance to sit down with her to discuss her views on bias in machine learning, algorithmic fairness, and the big picture of AI ethics.
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