We can’t trust AI systems built on deep learning alone
technologyreview.com
We can’t trust AI systems built on deep learning alone
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Re: We can’t trust AI systems built on deep learning alone
#2The opposing argument, by Rich Sutton, Distinguished Research Scientist at DeepMind:
Re: We can’t trust AI systems built on deep learning alone
#3In cognitive science we talk about having cognitive models of things. So I’m sitting in a hotel room, and I understand that there’s a closet, there’s a bed, there’s the television that’s mounted in an unusual way. I know that there are all these things here, and I don’t just identify them. I also understand how they relate to one another. I have these ideas about how the outside world works. They’re not perfect. They’re fallible, but they’re pretty good. And I make a lot of inferences around them to guide my everyday actions.
The opposite extreme is something like the Atari game system that DeepMind made, where it memorized what it needed to do as it saw pixels in particular places on the screen. If you get enough data, it can look like you’ve got understanding, but it’s actually a very shallow understanding. The proof is if you shift things by three pixels, it plays much more poorly. It breaks with the change. That’s the opposite of deep understanding.
Of course. There are an infinte way to make interpretations of perceptions and a finite subset of possible valid ones.
It's among those possible, that the AI will be a concrete implementation of an ideology.
To select which one is always done by humans.
Re: We can’t trust AI systems built on deep learning alone
#4The opposing argument, by Rich Sutton, Distinguished Research Scientist at DeepMind: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Seems talking about different issues
Re: We can’t trust AI systems built on deep learning alone
#5I started reading Rebooting AI last night. I think that Marcus and Davis (so far in the book) take a reasonable approach by wanting to design robust AI. Robust AI requires general real world intelligence that is not provided by deep learning.
I have earned over 90% of my income over the last five or six years as a deep learning practitioner. I am a fan of DL based on great results for perception tasks as well as solid NLP results like using BERT like models for things like anaphora resolution.
But, I am in agreement with Marcus and Davis that our long term research priorities are wrong.
Re: We can’t trust AI systems built on deep learning alone
#6The opposing argument, by Rich Sutton, Distinguished Research Scientist at DeepMind: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
I would say this is also an opposing argument:
https://twitter.com/ylecun/status/1066568396177842176
i.e. gradient-based learning is the final word on the matter.
Re: We can’t trust AI systems built on deep learning alone
#7Understanding a sentence is fundamentally different from recognizing an object. But people are trying to use deep learning to do both.
I agree with most of the article but I think this^^ skips over the different types of networks used to solve perception and language problems. A CNN is very different from say, word2vec, which isn't a very deep network at all.
Re: We can’t trust AI systems built on deep learning alone
#8This article doesn’t have any substance. It’s full of anecdata like shifting by 3 pixels to mess up a video game AI or some vague nonsense about “a model of this chair or this tv mounted to the wall.” It’s all casual hypotheticals.
There’s plenty of research on Bayesian neural networks for causal inference. But even more, a lot of causal inference problems are “small data” problems where choosing a strongly informative prior to pair with simple models is needed to prevent overfitting and poor generalization and to account for domain expertise.
Deep learning practitioners generally know plenty about this stuff and fully understand that deep neural networks are just one tool in the tool box, not applicable to all problems and certainly not approaching any kind of general AI solution that supersedes causal inference, feature engineering, etc.
This article is just a sensationalist hit job trying to capitalize on public anxieties about AI to raise the profile of this academic and try to sell more copies of his book.
I’d say, let’s not waste time on this crap. There are engineering problems that deep learning allows us to safely & reliably solve where other methods never could. We absolutely can trust these models for specific use cases. Let’s just get on with doing the work.
Re: We can’t trust AI systems built on deep learning alone
#9The opposing argument, by Rich Sutton, Distinguished Research Scientist at DeepMind: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
there are some surprisingly weak arguments in the text. It's correct to not treat computational resources as constant, but ot treat them as unimportant or negligible is awful as well.
Already computational resources are becoming prohibitive with only a few institutions producing state of the art models at high financial cost. If the goal is AGI this might get exponentially worse. Intelligence needs to take resource consumption into account. The models we produce aren't even close to high level reasoning and we're already consuming significantly more energy than humans or animals, something is wrong.
The scale argument isn't great either because deep learning is running into the inverse issue of classical AI. Now instead of having to program all logic explicitly we have to formulate every individual problem as training data. This doesn't scale either. If an AI gets attacked by a wild animal the solution can't be to first produce 10k pictures of mauled victims, intelligence includes to reason about things in the abscence of data. We can't have autonomous cars constantly running into things until we provide huge amounts of data for every problem, this does not scale either.
Re: We can’t trust AI systems built on deep learning alone
#10I started reading Rebooting AI last night. I think that Marcus and Davis (so far in the book) take a reasonable approach by wanting to design robust AI. Robust AI requires general real world intelligence that is not provided by deep learning. I have earned over 90% of my income over the last five or six years as a deep learning practitioner. I am a fan of DL based on great results for perception tasks as well as soli…
It’s the classic: ML + business logic
I think that’s fair, deep learning today has an issue with learning guide rails and obviously it is only as good as the data you feed it. I think it’s fair that our models need more