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Comment by lopatin | Hacker News Reader

Can someone explain for a dummy how this is possible? How does it know that range() is zero indexed? Was it specifically trained on Python input/function/output data? Or did it just "learn" it? Do the researchers know how it learned it?

Does it actually "run" the code? Like, if it was looping over 1 billion iterations would it take 1B times longer than if it was just one iteration? I have so many questions.

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> How does it know that range() is zero indexed?

If you read through all of the internet once, would you know that range() is zero indexed?

> Like, if it was looping over 1 billion iterations would it take 1B times longer than if it was just one iteration?

It clearly cannot, because querying the network for a token executes the exact same sequence of operations every time.

But it's very impressive that it can basically recognize the Collatz Conjecture in the code and mostly guess in the right ballpark for the results.

The fact that it's just liking (in a loose sense) inputs to inputs it has seen is quite visible in the f(g(x)) vs g(f(x)) behavior - the former is significantly more common, so it struggles to work with the latter.

https://alphacode.deepmind.com/ gives you a glimpse inside of what emerged from a similar attention net trained on code. however, whether the attention net has been forced upon pixels, language, amino acid sequences, the resultant representations are a bit beyond human reasoning, even if we can examine what individual attention heads are 'looking' at

It seems more likely that it learned it. If you knew nothing about Python, but understood the word "for" a little, and understood code a little, you're likely to figure out that range() is zero-indexed after you see something like this a few times

>>> for i in range(3): print(i)

0 1 2

My mind is just blown that it learned a language runtime based on examples. What would happen if you gave it an infinitely recusrive function? It can't stack overflow, there's no stack! Wait, is there?

My guess is it would respond with the standard stack overflow error, from examples of similar output posted in its training set.

It hasn't. It's memorised the examples and can generate them with some variation and according to what output is more likely given the input. But that's generation, not computation.

I don't know if this example helps, but a computer can generate (pseudo-)random numbers by executing an algorithm. A pair of dice can also generate random numbers because they're thrown so that they land at random and someone has marked pips on their faces that a human can read as numbers. The result may be similar, but one is generated by a computation and the other by a random process. The random process is not a computation. It's a random process.

(Or just unpredictable).

How do you know a range is zero indexed? (As in how is it stored in your brain)

I have no idea how it's stored in my brain. Is that the same way it's stored in GPT-3?

Probably similar. GPT3 also doesn't know how it's stored in its brain, and neither do we.

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