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The formalism that data-driven machine learning leans on is empirical tuning of stochastic search to drive approximation of functions, and despite what Silicon

by advael 2y ago

The formalism that data-driven machine learning leans on is empirical tuning of stochastic search to drive approximation of functions, and despite what Silicon Valley would have you believe, most of the significant advances have been in creating useful meta-structures for modeling certain kinds of problems (e.g. convolution for efficiently processing transformations that care about local structure across dimensions of data, or qkv attention for keeping throughlines of non-local correspondences intact through a long sequence). Neural networks as a flavor of empirical function approximation happened to scale well, and then a bunch of people who saw how much this scale improved the models' capabilities but couldn't be bothered to understand the structural component concluded that scale somehow magically gets you to every unsolved problem being solved. It's also convenient for business types that if you buy this premise, any unicorn they want to promise is just a matter of throwing obscene amounts of resources at the problem (through their company of course) I think probably the general idea of dynamic structures that are versatile in their ability to approximate functional models is at least a solid hypothesis for how some biological intelligence works at some level (I think maybe the "fluid/crystallized" intelligence distinction some psychology uses is informative here - a strong world model probably informs a lot of quick acquisition of relationships, but most intelligent systems clearly posess strong feedback mechanisms for capturing new models), though I definitely agree that a focus on how best to throw a ton of scale at these models doesn't seem like a fruitful path for actionably learning how to build or analyze intelligent systems in the way we usually think about, nor is it, well, sustainable. Moore's law appeals to business people because buying more computronium feels more like a predictable input-output relationship to put capital into, but even if we're just talking about raw computation speed advances in algorithms tend to dwarf advances in computing power in the long run. I think the same will hold true in AGI

YeGoblynQueenne 2y ago
Yeah, very good points. To be fair there are people who have argued the big data side who have clearly solid knowledge of AI and are not just SV suits, for example I remember Yann LeCun in a debate with Christopher Manning, where Manning was arguing for the importance of "structure" and LeCun was arguing against it. Or see the "Bitter Lesson", mentioned in a parent comment. That may have become a total shibboleth of the Silicon bros but Rich Sutton, who wrote the eponymous article, is the guy who wrote the book on Reinforcement Learning (literally). And then Rodney Brooks' replied with his "Better Lesson" (https://rodneybrooks.com/a-better-lesson/ https://rodneybrooks.com/a-better-lesson/). So there's a lot of debate in this and I don't reckon we'll have a consensus soon. It should be clear which side I'm on- I work with firmly model-based AI ("planning is the model-based approach to autonomous behaviour" has become my shibboleth - see Bonnet and Geffner's book on planning: https://link.springer.com/book/10.1007/978-3-031-01564-9 https://link.springer.com/book/10.1007/978-3-031-01564-9) so maybe it's a deformation professionelle. And even LCun's recent plans for JEPA are very consciously model-based, except he wants to learn his models from data; which is not a bad idea I suppose.
- advael 2y ago
  I've commented here before that I find myself really conflicted on LeCunn's public statements. I think it's really hard to reconcile the fact that he's undeniably a world-leading expert with the fact that he does work for and represent a tech company in a big way, which means that it's both hard to tell when what he says, especially publicly, is filtered through that lens, either explicitly or just via cultural osmosis. I know some people still in academia (e.g. "Bitter Lesson") are following suit but given how much of this field has been scooped up by large tech firms, this necessarily means that what we get out of research from those firms is partially filtered through them. Like it sounds like you're in CS/AI academia so I'm sure you're familiar with the distorting effect this brain drain has had on the field. Research out of places like FAIR or deepmind or OpenAI (arguably they were different until about 2019 or so? Hard to say how much of that was ever true unfortunately) are being done and published by world-leading experts hired by these companies and obviously this research has continued to be crucial to the field, but the fact that it's in industry means there's obviously controls on what they can publish, and the culture of an institution like Facebook is definitely going to have some different effects on priorities than that of most universities, and so while we can all collectively try to take it all with a grain of salt in some way, there is no way to be careful enough to avoid tribal knowledge in the field being heavily influenced by the cultures and priorities of these organizations. But even if this kind of thinking is totally organic, I think it could arise from the delayed nature of the results of data-driven methods. Often a major structural breakthrough for a data-driven approach drastically predates the most obviously impactful results from that breakthrough, because the result impressive enough to draw people's attention comes from throwing lots of data and compute at the breakthrough. The people who got the impressive result might not even be the same team as the one that invented the structure they're relying on, and it's really easy to get the impression that what changed the game was the scale alone, I imagine even if you're on one of those research teams. I've been really impressed by some of the lines of research that show that you can often distill some of these results to not rely so heavily on massive datasets and enormous parallel training runs, and think we should properly view results that come from these to be demonstrations of the power of the underlying structural insights rather than new results. But I think this clashes with the organizational priorities of large tech firms, which often view scale as a moat, and thus are motivated to emphasize the need for it

by advael 2y ago

YeGoblynQueenne 2y ago

Yeah, very good points. To be fair there are people who have argued the big data side who have clearly solid knowledge of AI and are not just SV suits, for example I remember Yann LeCun in a debate with Christopher Manning, where Manning was arguing for the importance of "structure" and LeCun was arguing against it. Or see the "Bitter Lesson", mentioned in a parent comment. That may have become a total shibboleth of the Silicon bros but Rich Sutton, who wrote the eponymous article, is the guy who wrote the book on Reinforcement Learning (literally). And then Rodney Brooks' replied with his "Better Lesson" (https://rodneybrooks.com/a-better-lesson/ https://rodneybrooks.com/a-better-lesson/). So there's a lot of debate in this and I don't reckon we'll have a consensus soon. It should be clear which side I'm on- I work with firmly model-based AI ("planning is the model-based approach to autonomous behaviour" has become my shibboleth - see Bonnet and Geffner's book on planning: https://link.springer.com/book/10.1007/978-3-031-01564-9 https://link.springer.com/book/10.1007/978-3-031-01564-9) so maybe it's a deformation professionelle. And even LCun's recent plans for JEPA are very consciously model-based, except he wants to learn his models from data; which is not a bad idea I suppose.

advael 2y ago
I've commented here before that I find myself really conflicted on LeCunn's public statements. I think it's really hard to reconcile the fact that he's undeniably a world-leading expert with the fact that he does work for and represent a tech company in a big way, which means that it's both hard to tell when what he says, especially publicly, is filtered through that lens, either explicitly or just via cultural osmosis. I know some people still in academia (e.g. "Bitter Lesson") are following suit but given how much of this field has been scooped up by large tech firms, this necessarily means that what we get out of research from those firms is partially filtered through them. Like it sounds like you're in CS/AI academia so I'm sure you're familiar with the distorting effect this brain drain has had on the field. Research out of places like FAIR or deepmind or OpenAI (arguably they were different until about 2019 or so? Hard to say how much of that was ever true unfortunately) are being done and published by world-leading experts hired by these companies and obviously this research has continued to be crucial to the field, but the fact that it's in industry means there's obviously controls on what they can publish, and the culture of an institution like Facebook is definitely going to have some different effects on priorities than that of most universities, and so while we can all collectively try to take it all with a grain of salt in some way, there is no way to be careful enough to avoid tribal knowledge in the field being heavily influenced by the cultures and priorities of these organizations. But even if this kind of thinking is totally organic, I think it could arise from the delayed nature of the results of data-driven methods. Often a major structural breakthrough for a data-driven approach drastically predates the most obviously impactful results from that breakthrough, because the result impressive enough to draw people's attention comes from throwing lots of data and compute at the breakthrough. The people who got the impressive result might not even be the same team as the one that invented the structure they're relying on, and it's really easy to get the impression that what changed the game was the scale alone, I imagine even if you're on one of those research teams. I've been really impressed by some of the lines of research that show that you can often distill some of these results to not rely so heavily on massive datasets and enormous parallel training runs, and think we should properly view results that come from these to be demonstrations of the power of the underlying structural insights rather than new results. But I think this clashes with the organizational priorities of large tech firms, which often view scale as a moat, and thus are motivated to emphasize the need for it