I think it’d be good to get these people who dismiss deep learning to explicitly state whether or not the only thing keeping us from imploding, is an inability by their field to solve a core problem it’s explicitly trying to solve. In particular it seems weird to answer a question like “why isn’t AI X-risk a problem” with “because the ML industry is failing to barrel towards that target fast enough”.
I think it makes sense (for him) to not believe AI X-risk is an important problem to solve (right now) if he believes that the “fast enough” means “not in his lifetime”, and he also puts a lot of moral weight on near-term issues. For completeness sake, here are some claims more relevant to “not being able to solve the core problem”.
1) From the part about compositionality, I believe he is making a point about the inability of generating some image that would contradict the training set distribution with the current deep learning paradigm
Generating an image for the caption, a horse riding on an astronaut. That was the example that Gary Marcus talked about, where a human would be able to draw that because a human understand the compositional semantics of that input and current models are struggling also because of distributional statistics and in the image to text example, that would be for example, stuff that we’ve been seeing with Flamingo from DeepMind, where you look at an image and that might represent something very unusual and you are unable to correctly describe the image in the way that’s aligned with the composition of the image. So that’s the parsing problem that I think people are mostly concerned with when it comes to compositionality and AI.
2) From the part about generalization, he is saying that there is some inability to build truly general systems. I do not agree with his claim, but if I were to steelman the argument it would be something like “even if it seems deep learning is making progress, Boston Robotics is not using deep learning and there is no progress in the kind of generalization needed for the Wozniak test”
the Wozniak test, which was proposed by Steve Wozniak, which is building a system that can walk into a room, find the coffee maker and brew a good cup of coffee. So these are tasks or capacities that require adapting to novel situations, including scenarios that were not foreseen by the programmers where, because there are so many edge cases in driving, or indeed in walking into an apartment, finding a coffee maker of some kind and making a cup of coffee. There are so many potential edge cases. And, this very long tail of unlikely but possible situations where you can find yourself, you have to adapt more flexibly to this kind of thing.
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But I don’t know whether that would even make sense, given the other aspect of this test, which is the complexity of having a dexterous robot that can manipulate objects seamlessly and the kind of thing that we’re still struggling with today in robotics, which is another interesting thing that, we’ve made so much progress with disembodied models and there are a lot of ideas flying around with robotics, but in some respect, the state of the art in robotics where the models from Boston Dynamics are not using deep learning, right?
I think it’d be good to get these people who dismiss deep learning to explicitly state whether or not the only thing keeping us from imploding, is an inability by their field to solve a core problem it’s explicitly trying to solve. In particular it seems weird to answer a question like “why isn’t AI X-risk a problem” with “because the ML industry is failing to barrel towards that target fast enough”.
I think it makes sense (for him) to not believe AI X-risk is an important problem to solve (right now) if he believes that the “fast enough” means “not in his lifetime”, and he also puts a lot of moral weight on near-term issues. For completeness sake, here are some claims more relevant to “not being able to solve the core problem”.
1) From the part about compositionality, I believe he is making a point about the inability of generating some image that would contradict the training set distribution with the current deep learning paradigm
2) From the part about generalization, he is saying that there is some inability to build truly general systems. I do not agree with his claim, but if I were to steelman the argument it would be something like “even if it seems deep learning is making progress, Boston Robotics is not using deep learning and there is no progress in the kind of generalization needed for the Wozniak test”