41–50 of 92 posts
Earlier quoted context omitted.
>Jay McClelland's office had a little corkboard thingy with Chomsky mockery on the side, for example. I've never understood why the idea of linguistic nativism is so upsetting to people.
Indeed, operating human lips, teeth, tongue, and larynx is far beyond language models.
It's an optimistic point of view. Still, when people use large neural nets to model physics, they also have a lot of parameters but they replicate very simple laws. So there's something deeper about this. Something like a simulation of theory.
Hence every system we get to see in nature is built from smaller components that generate complexity via repetition.
Our computers don't escape from this either. As the components get smaller you end up with your charge probability field outside of your component traces.
I disagree with the article. I think it is always possible to come up with reasonably small theories that capture most of the given phenomena. So in a sense, you don't need complex theories in the form of large NNs (models? functions? programs?), other than for more precise prediction. For example - global warming. It's nice to have AOGCMs that have everything and the carbon sink in them. But if you want to understan…
> I also don't think poverty is a complex problem, but that's a minor point. I'm not sure it's a minor point. I don't think poverty is a "complex" problem either, as that term is used in the article, but that doesn't mean I think it fits into one of the other two categories in the article. I think it is in a fourth category that the article doesn't even consider. For lack of a better term, I'll call that category "po…
Pretty simple.
This might be an unkind reading, but to me this just sounds like an attempt to reinvent the very same kind of mysticism that it mentions in the first paragraph. “No need to study the world around you and wonder about its rules, peasant - it’s far beyond your understanding! Only ~the gods~ computers can ever know the truth!” I shudder to think about a future where people give up on working to understand complex system…
Mark Cubain had a good line, I don't know if he came up with it or who, but he reportedly said: " There are 2 types of people using AI: Those who use it so they can know everything, and those who use it so they don't have to know anything. " :-
Two handwavey ideas upon reading this: - Even for billion-parameter theories, a small amount of vectors might dominate the behaviour. A coordinate shift approach (PCA) might surface new concepts that enable us to model that phenomenon. "A change in perspective is worth 80 IQ points", said Alan Kay. - There is analogue of how we come up with cognitive metaphors of the mind ("our models of the mind resemble our latest…
We kinda-sorta already know this is true. The lottery-ticket hypothesis [0] says that every large network contains a randomly initialized small network that performs as well as the overall network, and over the past eight years or so researchers have indeed managed to find small networks inside large networks of many different architectures that demonstrate this phenomenon.
Nobody talks much about the lottery-ticket hypothesis these days because it isn’t practically useful at the moment. (With the pruning algorithms and hardware we have, pruning is more costly than just training a big network.) But the basic idea does suggest that there may be hope for interpretability, at least in the odd application here or there.
That is, the (strong) lottery-ticket hypothesis suggests that the training process is a search through a large parameter space for a small network that already (by random initialization) exhibit the overall desired network behavior; updating parameters during the training process is mostly about turning off the irrelevant parts of the network.
For some applications, one would think that the small sub-network hiding in there somewhere might be small enough to be interpretable. I won’t be surprised if some day not too far into the future scientists investigating neural networks start to identify good interpretable models of phenomena of intermediate complexity (those phenomena that are too complex to be amenable to classic scientific techniques, but simple enough that neural networks trained to exhibit the phenomena yield unusually small active sub-networks).
Instead of "I understand the causal mechanism and can predict what happens if I change X," you get something more like "I have a sufficiently rich model that I can simulate what happens if I change X, with probabilistic confidence." The answers are distributions, not deterministic outputs. That's a different kind of knowing.
At the beginning this sounded like, "hard problems are complex, machine learning can help us manage complexity, therefore we will be able to solve hard problems with machine learning", which betrays a shallowness of understanding. I think what this essay argues here is a little deeper than that trite tech-bro hype meme.
But I disagree with this conclusion: I don't know that we can begin to build these models to begin with or that our new LLM/transformer-powered tools can help solve these problems. If simulation were the answer to everything, why will new ML tools make a significant difference in ways that existing simulation tools do not?
Stuff like AlphaFold is amazing—I'm not saying that better medical results won't come about from ML—but I feel like there's some substance missing and that even this level of excitement that the author expresses here needs more and better backing.
> It's remarkable how much of reality turned out to be modelable by theories that fit in a few symbols. The admiration for "remarkable" things puts humanity on a dangerous path that is disconnected from the real goals of human progress as a species. You don't need any of this compression of knowledge or truths. Folklore tales about celestial bodies are fine and hood enough. The vulgar pursuit for knowledge is paving…
Right, dinosaurs were perfectly fine, their ignorance worked out well for them. The universe is uncaring, simply not giving a shit if you have knowledge or not. Knowledge gives you the ability to survive minor conniption fits of cosmic magnitude, and at the same time gives you a gun to shoot your own foot off. There ain't no such thing as a free lunch.
> It's remarkable how much of reality turned out to be modelable by theories that fit in a few symbols. The admiration for "remarkable" things puts humanity on a dangerous path that is disconnected from the real goals of human progress as a species. You don't need any of this compression of knowledge or truths. Folklore tales about celestial bodies are fine and hood enough. The vulgar pursuit for knowledge is paving…
Two handwavey ideas upon reading this: - Even for billion-parameter theories, a small amount of vectors might dominate the behaviour. A coordinate shift approach (PCA) might surface new concepts that enable us to model that phenomenon. "A change in perspective is worth 80 IQ points", said Alan Kay. - There is analogue of how we come up with cognitive metaphors of the mind ("our models of the mind resemble our latest…
> Even for billion-parameter theories, a small amount of vectors might dominate the behaviour. We kinda-sorta already know this is true. The lottery-ticket hypothesis [0] says that every large network contains a randomly initialized small network that performs as well as the overall network, and over the past eight years or so researchers have indeed managed to find small networks inside large networks of many differ…