Self-supervised learning: The dark matter of intelligence
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Self-supervised learning: The dark matter of intelligence
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Re: Self-supervised learning: The dark matter of intelligence
#2One of the problems we have to be conscious about with self supervised learning is that the already existing problem of transparency and understandability gets worsened by orders of magnitude. If current billion parameter models are opaque, the result of SSL is, or will be, the equivalent of a black hole. How do you understand what the model has learned? Why has it learned that way? What things could have possible gone completely, horribly wrong in its quest to optimize its loss function?
This somewhat humorous but also worrying paper comes to mind: https://youtu.be/Lu56xVlZ40M
I think we really need to consider how we can mitigate the risk of raising completely ununderstandable yet scarily capable machines.
Re: Self-supervised learning: The dark matter of intelligence
#3One of the problems we have to be conscious about with self supervised learning is that the already existing problem of transparency and understandability gets worsened by orders of magnitude. If current billion parameter models are opaque, the result of SSL is, or will be, the equivalent of a black hole. How do you understand what the model has learned? Why has it learned that way? What things could have possible go…
I dont agree with this, either we know what the pretext task was, or we fine tune with labels, but in either case, it's no different to analyze than supervised models.
I do agree that more parameters and a bigger training set makes the models more opaque (or at least more expensive to understand), but the self supervision is not the reason.
Re: Self-supervised learning: The dark matter of intelligence
#4One of the problems we have to be conscious about with self supervised learning is that the already existing problem of transparency and understandability gets worsened by orders of magnitude. If current billion parameter models are opaque, the result of SSL is, or will be, the equivalent of a black hole. How do you understand what the model has learned? Why has it learned that way? What things could have possible go…
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Re: Self-supervised learning: The dark matter of intelligence
#5One of the problems we have to be conscious about with self supervised learning is that the already existing problem of transparency and understandability gets worsened by orders of magnitude. If current billion parameter models are opaque, the result of SSL is, or will be, the equivalent of a black hole. How do you understand what the model has learned? Why has it learned that way? What things could have possible go…
I have this same problem with people. Just because we don't understand perfectly doesn't mean the output is not useful.
Re: Self-supervised learning: The dark matter of intelligence
#6One of the problems we have to be conscious about with self supervised learning is that the already existing problem of transparency and understandability gets worsened by orders of magnitude. If current billion parameter models are opaque, the result of SSL is, or will be, the equivalent of a black hole. How do you understand what the model has learned? Why has it learned that way? What things could have possible go…
I have this same problem with people. Just because we don't understand perfectly doesn't mean the output is not useful.
Sure, but humans are a relatively known entity. They exist with a level of variance that is not too extreme, and you can more or less, in a general way, predict their behavior to a tolerable risk level. We've seen billions of humans, we know what to expect of them. We might not understand how other humans work internally, but we understand their actionspace.
For AI models, that isn't really the case. We don't know how each individual model will act in the real world, and certainly not after having spent millions of hours training against itself or in a simulated environment. And as these models get more and more powerful, this growing uncertainty is something that I believe is worrisome.
Re: Self-supervised learning: The dark matter of intelligence
#7> How is it that humans can learn to drive a car in about 20 hours of practice with very little supervision, while fully autonomous driving still eludes our best AI systems trained with thousands of hours of data from human drivers?
Wow, are these so-called ai scientists really that daft?
Sure on paper we drive 20-40 hours "practicing" then hit the roads, but we've been back-seat driving and driving via video games or TV since the day we're born.
While a 2-year old doesn't know the intricacies of driving my 3-year old can definitely yell hey dad the lights red slow down.
Full-immersive life experience. Perhaps ai's need to be put into a real-world "birth" simulation (perhaps we already are this experiment) to learn as they grow.
The problem I see is the narrowness, if you're training on a narrow subset then you're gonna get narrow results. I don't know the best way of doing it but you need to start thinking of an ai's "brain" like that of a child's and how it absorbs things - the human brain is remarkable sure, but I don't doubt it's duplicatable in silicon.
Re: Self-supervised learning: The dark matter of intelligence
#8One of the problems we have to be conscious about with self supervised learning is that the already existing problem of transparency and understandability gets worsened by orders of magnitude. If current billion parameter models are opaque, the result of SSL is, or will be, the equivalent of a black hole. How do you understand what the model has learned? Why has it learned that way? What things could have possible go…
I have this same problem with people. Just because we don't understand perfectly doesn't mean the output is not useful.
Very few individual humans can act in a way that affects hundreds of millions of people, computers can do that in a nanosecond.
Re: Self-supervised learning: The dark matter of intelligence
#9Earlier quoted context omitted.
I have this same problem with people. Just because we don't understand perfectly doesn't mean the output is not useful.
Sure, but humans are a relatively known entity. They exist with a level of variance that is not too extreme, and you can more or less, in a general way, predict their behavior to a tolerable risk level. We've seen billions of humans, we know what to expect of them. We might not understand how other humans work internally, but we understand their actionspace. For AI models, that isn't really the case. We don't know ho…
> Sure, but humans are a relatively known entity.
We don’t know how brain works, why sleep exist, most of humans cultures and languages are not documented, hundreds if not thousands of psychological and medical things are unknown, etc. Machines and their applications are a few more magnitudes more understood than any human matter, by the sheer fact we created them. Their complexity is ridiculously low in comparison to human, their biology, psychology, etc.
Re: Self-supervised learning: The dark matter of intelligence
#10> How is it that humans can learn to drive a car in about 20 hours of practice with very little supervision, while fully autonomous driving still eludes our best AI systems trained with thousands of hours of data from human drivers? Wow, are these so-called ai scientists really that daft? Sure on paper we drive 20-40 hours "practicing" then hit the roads, but we've been back-seat driving and driving via video games o…
I think you misunderstood. That’s exactly the point they are making.