104. Ken Stanley - AI without objectives

Towards Data Science

11/24/21

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66m

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Today, most machine learning algorithms use the same paradigm: set an objective, and train an agent, a neural net, or a classical model to perform well against that objective. That approach has given good results: these types of AI can hear, speak, write, read, draw, drive and more.

But they’re also inherently limited: because they optimize for objectives that seem interesting to humans, they often avoid regions of parameter space that are valuable, but that don’t immediately seem interesting to human beings, or the objective functions we set. That poses a challenge for researchers like Ken Stanley, whose goal is to build broadly superintelligent AIs — intelligent systems that outperform humans at a wide range of tasks. Among other things, Ken is a former startup founder and AI researcher, whose career has included work in academia, at UberAI labs, and most recently at OpenAI, where he leads the open-ended learning team.

Ken joined me to talk about his 2015 book Greatness Cannot Be Planned: The Myth of the Objective, what open-endedness could mean for humanity, the future of intelligence, and even AI safety on this episode of the TDS podcast.

Previous Episode

103. Gillian Hadfield - How to create explainable AI regulations that actually make sense

November 17, 2021

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51m

It’s no secret that governments around the world are struggling to come up with effective policies to address the risks and opportunities that AI presents. And there are many reasons why that’s happening: many people — including technical people — think they understand what frontier AI looks like, but very few actually do, and even fewer are interested in applying their understanding in a government context, where salaries are low and stock compensation doesn’t even exist.

So there’s a critical policy-technical gap that needs bridging, and failing to address that gap isn’t really an option: it would mean flying blind through the most important test of technological governance the world has ever faced. Unfortunately, policymakers have had to move ahead with regulating and legislating with that dangerous knowledge gap in place, and the result has been less-than-stellar: widely criticized definitions of privacy and explainability, and definitions of AI that create exploitable loopholes are among some of the more concerning results.

Enter Gillian Hadfield, a Professor of Law and Professor of Strategic Management and Director of the Schwartz Reisman Institute for Technology and Society. Gillian’s background is in law and economics, which has led her to AI policy, and definitional problems with recent and emerging regulations on AI and privacy. But — as I discovered during the podcast — she also happens to be related to Dyllan Hadfield-Menell, an AI alignment researcher whom we’ve had on the show before. Partly through Dyllan, Gillian has also been exploring how principles of AI alignment research can be applied to AI policy, and to contract law. Gillian joined me to talk about all that and more on this episode of the podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

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Chapters:

1:35 Gillian’s background
8:44 Layers and governments’ legislation
13:45 Explanations and justifications
17:30 Explainable humans
24:40 Goodhart’s Law
29:10 Bringing in AI alignment
38:00 GDPR
42:00 Involving technical folks
49:20 Wrap-up

Next Episode

105. Yannic Kilcher - A 10,000-foot view of AI

December 1, 2021

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63m

There once was a time when AI researchers could expect to read every new paper published in the field on the arXiv, but today, that’s no longer the case. The recent explosion of research activity in AI has turned keeping up to date with new developments into a full-time job.

Fortunately, people like YouTuber, ML PhD and sunglasses enthusiast Yannic Kilcher make it their business to distill ML news and papers into a digestible form for mortals like you and me to consume. I highly recommend his channel to any TDS podcast listeners who are interested in ML research — it’s a fantastic resource, and literally the way I finally managed to understand the Attention is All You Need paper back in the day.

Yannic is joined me to talk about what he’s learned from years of following, reporting and doing AI research, including the trends, the challenges and the opportunities that he expects are going to shape the course of AI history in coming years.

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Intro music:

➞ Artist: Ron Gelinas

➞ Track Title: Daybreak Chill Blend (original mix)

➞ Link to Track: https://youtu.be/d8Y2sKIgFWc

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Chapters:

0:00 Intro

1:20 Yannic’s path into ML

7:25 Selecting ML news

11:45 AI ethics → political discourse

17:30 AI alignment