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The Bitter Lesson

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Re: The Bitter Lesson

#21

Post by beefcafe »

tschellenbach wrote: post #10

On the other hand at some point we will want AI to learn based on a small number of interactions. IE an AI that beats a human after playing 10 games of chess/starcraft etc. Right now it takes millions of training matches. Many real world situations don't happen that often so this fundamentally limits applications of the current generation of AI.

Show me a human that can win a Starcraft championship after only playing ten games. If you find any, they learned the mechanics and strategy somewhere else. That’s transfer learning, appears to be in its infancy in the ML community but making progress.

Re: The Bitter Lesson

#22

Post by beefcafe »

notacoward wrote: post #20
cs702 wrote: post #5

The majority of businesses and governments are insisting on learning this bitter lesson anew. In the minds of many business executives and government officials, "explainable AI" means, quite literally, "show it to me as a linear combination of a small number of features" (sometimes called "drivers" or "factors") that have monotonic relationships with measurable outcomes. I would go further: most people are understand…

If explainable AI is too limiting, what's the alternative? What's going to happen when someone gets hauled into court to be held liable for their non-explainable AI's outcomes? Oh right, I know, they'll hide behind corporate limited-liability shenanigans, until people get tired of that and go straight for the guillotines. Or maybe the non-explainable AI's owners will decide they want to prevent that, and ... do you w…

I love the phrase “explainable AI”. We still can’t explain how our intelligence works with any degree of biological detail.

Re: The Bitter Lesson

#23

Post by albntomat0 »

beefcafe wrote: post #21
tschellenbach wrote: post #10

On the other hand at some point we will want AI to learn based on a small number of interactions. IE an AI that beats a human after playing 10 games of chess/starcraft etc. Right now it takes millions of training matches. Many real world situations don't happen that often so this fundamentally limits applications of the current generation of AI.

Show me a human that can win a Starcraft championship after only playing ten games. If you find any, they learned the mechanics and strategy somewhere else. That’s transfer learning, appears to be in its infancy in the ML community but making progress.

The scale matters here. I think a better metric for your parent comment would be the delta in skill per game played.

A human is significantly better on game 11 than game 1 (I recently got into Starcraft). Current ML systems are not. It's up for discussion how to take the human's previous experience into account, but the total amount of experience is significantly less that the computer's.

Re: The Bitter Lesson

#24

Post by albntomat0 »

beefcafe wrote: post #22
notacoward wrote: post #20

Earlier quoted context omitted.

If explainable AI is too limiting, what's the alternative? What's going to happen when someone gets hauled into court to be held liable for their non-explainable AI's outcomes? Oh right, I know, they'll hide behind corporate limited-liability shenanigans, until people get tired of that and go straight for the guillotines. Or maybe the non-explainable AI's owners will decide they want to prevent that, and ... do you w…

I love the phrase “explainable AI”. We still can’t explain how our intelligence works with any degree of biological detail.

We can't explain the implementation details, but a human system can literally explain the logic she used to reach a decision. For example, for applications in the justice system that AI has been recommended for, this is a highly important quality.

Re: The Bitter Lesson

#25

Post by taneq »

olooney wrote: post #12

Very interested article. I've often railed against putting your thumb on the scale (or even worse, second-guessing) machine learning models by applying too many so-called "business rules," especially post hoc rules. If the model doesn't learn on its own what you consider to be obvious structure of the data, then either you've chosen the completely wrong model and it won't be able to learn non-obvious truths either, O…

> Sutton takes an even more extreme point of view, suggesting that most human feature engineering is similarly a waste of time.

In fact, wasn't there an article posted here recently saying that they'd had good results with using learned features to feed traditional non-NN-based machine learning?

Re: The Bitter Lesson

#26

Post by taneq »

albntomat0 wrote: post #24
beefcafe wrote: post #22

Earlier quoted context omitted.

I love the phrase “explainable AI”. We still can’t explain how our intelligence works with any degree of biological detail.

We can't explain the implementation details, but a human system can literally explain the logic she used to reach a decision. For example, for applications in the justice system that AI has been recommended for, this is a highly important quality.

Eeeeeeh... what we do is more like parallel construction. We can give a series of plausible steps to explain where we ended up, but sometimes we can't really explain why we did some of the steps.

Re: The Bitter Lesson

#27

Post by sevensor »

What's missing in this account is all the interesting stuff that came from the attempt to emulate human reasoning. Sure, it didn't get us image recognition or chess mastery, but we have Prolog, much of what we now know as Lisp, and proof assistants. Deep, powerful tools that augment, but do not replace, human cognition.

Re: The Bitter Lesson

#29

Post by zalnyx »

Open AI said as much when discussing their move to a for-profit LP model.

They anticipate that real advances will be made by massively scaling up the compute power they throw at any given problem. That’s driving their fundraising efforts.

If the past 5 years are anything to go by, they’re right.

Re: The Bitter Lesson

#30

Post by cbames89 »

I learned a similar lesson from working on robots. At first I attempted to devise methods for Robonaut 2 to do things the way I do because it was designed to be like me. It was missing little things that made my approaches unfeasible, and it was infuriating. At that point I decided it only made sense to make methods that allow the agent to discover its own behaviors, because its merkwelt and my own will never be the same.
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