114. Sam Bowman - Are we under-hyping AI?

Towards Data Science

03/02/22

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47m

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Google the phrase “AI over-hyped”, and you’ll find literally dozens of articles from the likes of Forbes, Wired, and Scientific American, all arguing that “AI isn’t really as impressive at it seems from the outside,” and “we still have a long way to go before we come up with *true* AI, don’t you know.”

Amusingly, despite the universality of the “AI is over-hyped” narrative, the statement that “We haven’t made as much progress in AI as you might thinkTM️” is often framed as somehow being an edgy, contrarian thing to believe.

All that pressure not to over-hype AI research really gets to people — researchers included. And they adjust their behaviour accordingly: they over-hedge their claims, cite outdated and since-resolved failure modes of AI systems, and generally avoid drawing straight lines between points that clearly show AI progress exploding across the board. All, presumably, to avoid being perceived as AI over-hypers.

Why does this matter? Well for one, under-hyping AI allows us to stay asleep — to delay answering many of the fundamental societal questions that come up when widespread automation of labour is on the table. But perhaps more importantly, it reduces the perceived urgency of addressing critical problems in AI safety and AI alignment.

Yes, we need to be careful that we’re not over-hyping AI. “AI startups” that don’t use AI are a problem. Predictions that artificial general intelligence is almost certainly a year away are a problem. Confidently prophesying major breakthroughs over short timescales absolutely does harm the credibility of the field.

But at the same time, we can’t let ourselves be so cautious that we’re not accurately communicating the true extent of AI’s progress and potential. So what’s the right balance?

That’s where Sam Bowman comes in. Sam is a professor at NYU, where he does research on AI and language modeling. But most important for today’s purposes, he’s the author of a paper titled, “When combating AI hype, proceed with caution,” in which he explores a trend he calls under-claiming — a common practice among researchers that consists of under-stating the extent of current AI capabilities, and over-emphasizing failure modes in ways that can be (unintentionally) deceptive.

Sam joined me to talk about under-claiming and what it means for AI progress on this episode of the Towards Data Science 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:

2:15 Overview of the paper
8:50 Disappointing systems
13:05 Potential double standard
19:00 Moving away from multi-modality
23:50 Overall implications
28:15 Pressure to publish or perish
32:00 Announcement discrepancies
36:15 Policy angle
41:00 Recommendations
47:20 Wrap-up

Previous Episode

113. Yaron Singer - Catching edge cases in AI

February 9, 2022

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35m

It’s no secret that AI systems are being used in more and more high-stakes applications. As AI eats the world, it’s becoming critical to ensure that AI systems behave robustly — that they don’t get thrown off by unusual inputs, and start spitting out harmful predictions or recommending dangerous courses of action. If we’re going to have AI drive us to work, or decide who gets bank loans and who doesn’t, we’d better be confident that our AI systems aren’t going to fail because of a freak blizzard, or because some intern missed a minus sign.

We’re now past the point where companies can afford to treat AI development like a glorified Kaggle competition, in which the only thing that matters is how well models perform on a testing set. AI-powered screw-ups aren’t always life-or-death issues, but they can harm real users, and cause brand damage to companies that don’t anticipate them.

Fortunately, AI risk is starting to get more attention these days, and new companies — like Robust Intelligence — are stepping up to develop strategies that anticipate AI failures, and mitigate their effects. Joining me for this episode of the podcast was Yaron Singer, a former Googler, professor of computer science and applied math at Harvard, and now CEO and co-founder of Robust Intelligence. Yaron has the rare combination of theoretical and engineering expertise required to understand what AI risk is, and the product intuition to know how to integrate that understanding into solutions that can help developers and companies deal with AI risk.

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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
2:30 Journey into AI risk
5:20 Guarantees of AI systems
11:00 Testing as a solution
15:20 Generality and software versus custom work
18:55 Consistency across model types
24:40 Different model failures
30:25 Levels of responsibility
35:00 Wrap-up

Next Episode

115. Irina Rish - Out-of-distribution generalization

March 9, 2022

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50m

Imagine, for example, an AI that’s trained to identify cows in images. Ideally, we’d want it to learn to detect cows based on their shape and colour. But what if the cow pictures we put in the training dataset always show cows standing on grass?

In that case, we have a spurious correlation between grass and cows, and if we’re not careful, our AI might learn to become a grass detector rather than a cow detector. Even worse, we could only realize that’s happened once we’ve deployed it in the real world and it runs into a cow that isn’t standing on grass for the first time.

So how do you build AI systems that can learn robust, general concepts that remain valid outside the context of their training data?

That’s the problem of out-of-distribution generalization, and it’s a central part of the research agenda of Irina Rish, a core member of the Mila— Quebec AI Research institute, and the Canadian Excellence Research Chair in Autonomous AI. Irina’s research explores many different strategies that aim to overcome the out-of-distribution problem, from empirical AI scaling efforts to more theoretical work, and she joined me to talk about just that on this episode of the podcast.

***

Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

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

***

Chapters: