Comment by gessha
3 months ago
Your example is somewhat inadequate. We _fundamentally_ don’t understand how deep learning systems works in the sense that they are more or less black boxes that we train and evaluate. Innovations in ML are a whole bunch of wizards with big stacks of money changing “Hmm” to “Wait” and seeing what happens.
Would a different sampler help you? I dunno, try it. Would a smaller dataset help? I dunno, try it. Would training the model for 5000 days help? I dunno, try it.
Car technology is the opposite of that - it’s a white box. It’s composed of very well defined elements whose interactions are defined and explained by laws of thermodynamics and whatnot.
> _fundamentally_ don’t understand how deep learning systems works.
It's like saying we don't understand how quantum chromodynamics works. Very few people do, and it's the kind of knowledge not easily distilled for the masses in an easily digestible in a popsci way.
Look into how older CNNs work -- we have very good visual/accesible/popsci materials on how they work.
I'm sure we'll have that for LLM but it's not worth it to the people who can produce that kind of material to produce it now when the field is moving so rapidly, those people's time is much better used in improving the LLMs.
The kind of progress being made leads me to believe there absolutely ARE people who absolutely know how the LLMs work and they're not just a bunch of monkeys randomly throwing things at GPUs and seeing what sticks.
As a person who has trained a number of computer vision deep networks, I can tell you that we have some cool-looking visualizations on how lower layers work but no idea how later layers work. The intuition is built over training numerous networks and trying different hyperparameters, data shuffling, activations, etc. it’s absolutely brutal over here. If the theory was there, people like Karpathy who have great teacher vibes would’ve explained it for the mortal grad students or enthusiast tinkerers.
> The kind of progress being made leads me to believe there absolutely ARE people who absolutely know how the LLMs work and they're not just a bunch of monkeys randomly throwing things at GPUs and seeing what sticks
I say this less as an authoritative voice but more as an amused insider: Spend a week with some ML grad students and you will get a chuckle whenever somebody says we’re not some monkeys throwing things at GPUs.
It may be as simple as this:
https://youtube.com/shorts/7GrecDNcfMc
Many many layers of that. It’s not a profound mechanism. We can understand how that works, but we’re dumbfounded how such a small mechanism is responsible for all this stuff going on inside a brain.
I don’t think we don’t understand, it’s a level beyond that. We can’t fathom the implications, that it could be that simple, just scaled up.
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> The kind of progress being made leads me to believe there absolutely ARE people who absolutely know how the LLMs work
Just like alchemists made enormous strides in chemistry, but their goal was to turn piss into gold.
Isn't that just scale? Even small LLMs have more parts than any car.
LLMs are more analogous to economics, psychology, politics -- it is possible there's a core science with explicability, but the systems are so complex that even defining the question is hard.
You can make a bigger ICE engine (like a container ship engine) and still understand how the whole thing works. Maybe there’s more parts moving but it still has the structure of an ICE engine.
With neural networks big or small, we got no clue what’s going on. You can observe the whole system, from the weights and biases, to the activations, gradients, etc and still get nothing.
On the other hand, one of the reasons why economics, psychology and politics are hard is because we can’t open up people’s heads and define and measure what they’re thinking.
One way I've heard it summarized: Computer Science as a field is used to things being like physics or chemistry, but we've suddenly encountered something that behaves more like biology.
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Could be, but it does not change the fact that we do not understand them as of now.