Live data from Hacker News

Self-supervised learning: The dark matter of intelligence

ai.facebook.com

11–20 of 76 posts

Re: Self-supervised learning: The dark matter of intelligence

#11

One 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…

Not sure I see your point.

How is it harder to understand a model that was trained to predict which two images crops come from the same source image (a la contrastive loss for example), than a model that was trained to predict which class an image belongs to?

Re: Self-supervised learning: The dark matter of intelligence

#12
post #5

One 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.

In a medical context, the doctor won't use it if he doesn't know where it gets its score from

Re: Self-supervised learning: The dark matter of intelligence

#13
State of the art performance is being broken in multiple fields rapidly these days. However, AI explainability has a long way to go. Scaling opaqueness makes this problem worse.

Fine tuning labels black-box style is a terrifying concept to most who are working in fields where great risk must be managed to avoid unintended and disparate impact. Facebook making an oopsies suggesting my friends face in a photo instead of mine with a SSL trained model may seem trivial, but this non-human oversight is concerning for other applications.

If you were rejected by a bank for a home loan, you would want to know why your creditworthiness wasn’t evaluated positively (and banks must explain precisely why to regulators). And if a self-driving car made a decision in a collision, or a CV model performing cancer screening in X-rays that gave a bad result - ...or recommending politically divisive content... - or a thousand other real world examples that have human impact.

Re: Self-supervised learning: The dark matter of intelligence

#14
post #9

Earlier quoted context omitted.

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 t…

on one hand i agree with what you're saying. we humans have done terrible stuff like wars, genocides, famines, destruction of ecosystems, extinction of entire species, etc. and that's only the things we did more or less deliberately. we might cause our own extermination or a mass extinction event "by mistake", and we don't even understand how basic parts of our minds work.

but all of that being said, i think it's also worth considering what a non-human entity with more power than us might be able to do. after all, even the worst examples of humanity are confined to at most "only" causing millions of deaths, but not the eradication of all life on Earth for example. it's not difficult to imagine that a non-human entity, with non-human goals, and with super-human powers, might act in a way that is contrary to our interests, or even to our existence.

Re: Self-supervised learning: The dark matter of intelligence

#15
post #9

Earlier quoted context omitted.

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 t…

That was not the original point. Humans are relatively predictable whether we understand how the brain works exactly. An AI model isn’t an actual intelligence in the normal use of the word. It’s a functional fragment that is able to emulate the ability to absorb a pattern of information for repetitive conditional recall in future processes. Until a researcher has had ALOT of time spent on observing the limits db particulars of a model, there are unforeseen behaviors it can have that can have incredibly real and serious consequences depending on the application. A more fair comparison would be training a new intelligent species to do something before learning about the characteristic behavior quirks of said species

Re: Self-supervised learning: The dark matter of intelligence

#16
post #5

Earlier 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.

Very few individual humans can act in a way that affects hundreds of millions of people, computers can do that in a nanosecond.

Can't humans with computers affect hundreds of millions of people too?

Re: Self-supervised learning: The dark matter of intelligence

#18
post #17

Does not anyone worry about this whole human level AI Pandora’s box trap? Why not work on nukes themselves, sounds safer to me

Given that we all haven’t been obliterated by nukes, it does seem your argument seems to argue against your own premise. We will probably be fine even if we inexplicably create superior intelligence. Besides? Who’s to not say that the best course of action an infinitely intelligent being would choose is to just switch itself off given the futility of everything?

Re: Self-supervised learning: The dark matter of intelligence

#19
If you cut some words out of a newspaper and a person writes words in those blanks then what has happened? If we cut out parts of a painting and a person paints in those blanks then what has happened? How is the person who wrote in the blanks or the person who painted in the blanks changed by writing or painting in the blanks? How are they changed when someone responds to what they've written or painted? How did the original writer or painter come to write and paint what was later blanked out?

Here are some words that I read but did not grasp firmly: learn, data, task, train, intelligence, general, model, skill, label, language, understanding, reality, observation, predictive, objects, concepts, act, hypotheses, knowledge, 'common sense', 'dark matter', 'artificial intelligence', teaching, classify, supervision, autonomous, 'self-supervised learning', recognize, patterns, representations, processing, systems, pretrained, vision, real-world, helpful, promising, 'energy-based models', prediction, uncertainty, 'joint embedding methods', 'latent-variable architectures', reasoning, 'predictive learning', 'supervisory signals', signals, structure, unobserved, property, input, 'co-occuring modalities', 'unsupervised learning', feedback, reinfrocement, 'downstream tasks', meaning, syntactic, word, associate, probability, vocabulary, 'convolutional network', network, 'prediction uncertainty', 'predicting missing words', computing, softmax layer, probability distribution, energy, incompatible, computer vision, and so on.

Re: Self-supervised learning: The dark matter of intelligence

#20

One 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…

> How do you understand what the model has learned?

How do you do that with humans? You watch their behavior, language and you test them.

Post reply on HN