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Self-supervised learning: The dark matter of intelligence
- montenegrohugo 6y agoOne 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 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.
- iujjkfjdkkdkf 6y agoI 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.
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- Forge36 6y agoI have this same problem with people. Just because we don't understand perfectly doesn't mean the output is not useful.
- montenegrohugo 6y agoSure, 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.
- tasogare 6y ago> 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.
- epidemian 6y agoon 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.
- Voloskaya 6y agoNot 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?
- loopz 6y agoWhat's the algorithm?
- visarga 6y ago> 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.
- vincnetas 6y agoYes. Humans make mistakes, humans with computers make mistakes faster. Computers without humans make mistakes even faster.
- uoaei 6y agoHumans can explain their reasoning when you ask them. The best we have with NNs is a fancy version of first-order sensitivity analysis.
- elcomet 6y agoHumans cannot really explain their reasoning. It has been shown that they usually invent an explanation that matches what has already been decided in their black-box.They mostly use post-hoc explanation, but cannot explain the true decision mechanism. If you make them believe that they choose another decision, they will create another explanation on the fly.
- ReDeiPirati 6y agoThis! Decision making is mostly "irrational". I wish it would be different.
- visarga 6y agoDecision making in humans is a mix of slow and fast thinking. Fast thinking - like reading, writing, physical reflexes - requires little effort and is best suited for repetitive situations. Slow thinking - such as reasoning - is more expensive so we only use it when we are faced with a new situation for which we have not 'cached' the answer. It entails the creation of a mental model and imagining outcomes. The exact mix of the two modes is tuned by evolution. If all we had was the slow system we'd be much less efficient at survival.
- knuthsat 6y agoTools for exploring the models are already being developed. Language models that are not given concepts of part-of-speech, dependency trees, grammars, develop representations of their own and people have found ways to inspect them.
- Schiphol 6y agoCould you please provide pointers to relevant papers? I'm very interested.
- desku 6y agoHere's a paper on how BERT (a large Transformer model trained using self-supervised learning) implicitly learns the traditional NLP pipeline: https://arxiv.org/abs/1905.05950 https://arxiv.org/abs/1905.05950
- gremlinsinc 6y ago> 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.
- jtsiskin 6y agoI think you misunderstood. That’s exactly the point they are making.
- NotEvil 6y agoI think you miss the point, it's not strictly about "it take 20 hour for an child to drive a car" and more of it can do this without external help. From your example, It's about how the child knows to stop at red light when he was never told that you have to stop at red light.
- Reebz 6y agoState 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.
- visarga 6y agoHigh risk models should work with humans in the loop, not autonomous.
- a_imho 6y agoWould this rule out self driving cars from the get go?
- 7_my_mind 6y agoDepends on what you mean by self driving cars. To me it is not even clear that driving assist tech will ever be advanced enough for cars to drive themselves reliably.* But that aside. Human in control + driving assists is already safer than fully manual driving. The question is whether the opposite is even safer. It is obvious to me that self driving with the human as backup is a joke. Humans cannot react fast enough in real emergencies. So if you want the car to mostly drive itself, it has to actually always drive itself. And to me it is not clear whether this is really going to be safer than the human+driving_assist scenario. *Of course I am talking about tech based on the current DL approaches. If we had AGI, then the question does not even need to be asked.
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- vladmk 6y agoDoes not anyone worry about this whole human level AI Pandora’s box trap? Why not work on nukes themselves, sounds safer to me
- ramraj07 6y agoGiven 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?
- ankfo04300 6y agoDon't worry. A sufficiently powerful AGI won't destroy the universe to satisfy its own goals; it'll just build a holodeck (by tampering with its sensors) so that it can gain +infinity pleasure right away. Unlike software, which can be mathematically airtight, there's no reason to assume that physical devices built by humans are unexploitable in the face of the massive intelligence of the AGI. So the AGI will just ask itself which is easier: delicately performing surgery on its own sensors, or building tons of nukes. Honestly, that people are even worried about AGI doomsday shows to me the power of narrative. Everybody has heard of SkyNet and the sorcerer's apprentice, nobody has heard of the AGI who outsmarted itself by creating digital porn for its objective function. Therefore, through the miracle of the availability heuristic, AGI doomsday it is.
- cat199 6y agoi'm not as scared of real AGI as I am of some pseudo-AGI convincing enough to fool everyone into following what it comes up with and ascribing it magical powers - we have enough problems with personality cults and herd mentality as it is..
- just1morething 6y agoIf 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.
- visarga 6y agoOne could make a similarly long list of poorly understood concepts in psychology.
- just1morething 6y agoI've found that plucking words from paragraphs has, on occasion, brought them more firmly under my control-- most of them remain mysterious to me when they pop up in papers on programming or psychology. Rather than list "ideas" or "concepts" I try to make a record of what appeared and the response I had to its appearance. How are we to solve difficult problems if we stop mentioning them or their difficulty?
- fantod 6y agoIf the person who filled in the blanks already had a good grasp of the language, they probably didn't learn much. However, fill-in-the-blank exercises can be a good language learning tool for children as well as adults learning a foreign language.
- Xplune13 6y agoNot directly related to the SSL part, but I have a question about how the work on AI is going. - Why are we even trying to create an AGI when we have no idea how it will turn out? - AI in day to day functions can be useful and I'm all for that but what is the point of even trying to give the AI self-awareness?
- drdeca 6y agoSo it can handle the strategy of making sure no one else makes one which is more harmful. :P But as a serious answer: of course, there is no major economic use of trying to make sure it has an internal subjective experience, but that’s not what people are aiming for when referring to AGI. The goal is that it be able to accomplish general tasks and goals. Like, what can you do with it? Everything you can do at all. That’s what people are aiming at. What tasks of reasoning and planning could a person do for you? An AGI would, basically by definition, be able to do those same kinds of things. (Of course, something could be an AGI but not as intelligent as a typical human, so long as it could still reason about all the same kinds of things. But at that point it would presumably just be a question of scaling things up.)
- TheOtherHobbes 6y agoAGI definitions are incredibly fuzzy and agency reliably seems to be confused with intelligence. The fundamental problem is whether AGI is outer-directed or inner-directed - i.e. whether it sets its own goals, or whether you tell it what to do and it improvises a solution. AGI is most useful when it's outer-directed with limited agency but some improvisational autonomy. You can give that kind of AGI specific problems and it will solve them in useful but unexpected ways. Then it will stop. AGI is most dangerous when it's inner-directed with full independent agency. Not only will humans have no control over it, there's a good chance humans won't even understand what it's aiming for. Agency is almost entirely unrelated to symbolic intelligence. You can have agency with very limited intelligence - most animals manage this - and no agency with very high symbolic intelligence. This is not a Boolean. But there will be a cutoff beyond which inner-directed behaviour predominates, initially driven by programmed "curiosity", leading to unpredictable consequences.
- ilaksh 6y agoSelf-supervised learning is very promising. But it is strange to me that so much computer vision research has no mention of the 3d structure of objects. That's why I like things like this https://arxiv.org/abs/2102.12627 https://arxiv.org/abs/2102.12627 "How to represent part-whole hierarchies in a neural network" by Hinton.
- jtsiskin 6y agoYeah, humans don’t learn from still pictures, we see things moving through time and space and rotating. This is likely providing us with essentially a lot of “self supervised” data. For instance https://www.ted.com/talks/pawan_sinha_how_brains_learn_to_see/transcript https://www.ted.com/talks/pawan_sinha_how_brains_learn_to_se... starting at 8:11
- ilaksh 6y agoGreat video. It's interesting to see how far semantic segmentation has progressed since 2009.
- d_burfoot 6y agoI wrote a book about this 10 years ago: https://arxiv.org/abs/1104.5466 https://arxiv.org/abs/1104.5466 Annotation doesn't scale. You are not going to get to real intelligence by labelling billions of data points. I expect that computer vision for autonomous vehicles will become good enough for practical use quite soon after the AV companies start doing large scale self-supervised learning (I don't like that term). Maybe Tesla and Waymo and others are doing that already. Another great application area is medical imaging. Progress in this area has been seriously hampered by the limitations of the annotation paradigm. Once people start to do SSL, there should be rapid progress.
- neoncontrails 6y agoThese are interesting epistemological questions. Is there a compelling reason to label a certain neural architecture as a "siamese network"? I'm guessing that this is a reference to conjoined twins, a biological phenomenon with no connection to the country previously known as Siam (now Thailand).
- imvetri 6y agoLooking for more visuals and less verbal