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Great, we can spend crazy amount of computational resources and hand-holding in order to (maybe) reproduce three lines of code.
Great, we can spend crazy amount of computational resources and hand-holding in order to (maybe) reproduce three lines of code.
Like for learning the English language we don’t fully understand the way LLMs work. We can’t fully characterize it. So we have debates on whether the LLM actually understands English or understands what it’s talking about. We simply don’t know.
The results of this show that the transformer understands the game of life. Or whatever the transformer does with the rules of the game of life it’s safe to say that it fits a definition of understanding as mankind knows it.
Like much of machine learning where we use the abstraction of curve fitting to understand higher dimensional learning we can do the same extrapolation here.
If the transformer understands the game of life then that understanding must translate over to the LLM. The LLM understands English and understands the contents of what it is talking about.
There was a clear gradient of understanding before understanding the game of life hit saturation. The transformer lived in a state where it didn’t get everything right but it understood the game of life to a degree.
We can extrapolate that gradient to LLMs as well. LLMs are likely on that gradient, not yet at saturation. Either way, I think it’s safe to say that LLMs understand what they are talking about. It’s just that they haven’t hit saturation yet. There’s clearly things that we as humans understand better than the LLM.
But let’s extrapolate this concept to an even higher level:
Have we as humans hit saturation yet?
Great, we can spend crazy amount of computational resources and hand-holding in order to (maybe) reproduce three lines of code.
Great, we can spend crazy amount of computational resources and hand-holding in order to (maybe) reproduce three lines of code.
It's a theoretical result to help determine what they're capable of, not a practical solution. Of course you can write the code yourself - but that's not the point!
There are only 512 training examples needed for that, and it would be a lot more interesting if a learning algorithm were able to fit that 3x3 convolution layer from those 512 examples. IIRC, and don't quote me on that, but that's not been done.
Earlier quoted context omitted.
> it's brute forcing a small grid If by small grid you are referring to the attention matrix plot shown, then that is not a correct interpretation. That diagonal-like pattern it learns, is 3x3 convolution, so it can compare the neighbours of a given cell. Edit: and note that every grid it is trained on / runs inference on is randomly generated and completely unique, so it cannot just memorise examples
My interpretation is that while it did learn the exact computation and not just a statistical approximation, it's still limited to a grid of a given size. In that sense the attention matrix is brute forced and the network did not learn a generalization. The article itself says "The largest grid size we successfully trained was 16x16".
Would it be possible to train an LLM on the rules how we would teach them to a human?
I don't get the point. A simple CNN with stride =1 should be able to solve it perfectly and generalize it to any size.
Great, we can spend crazy amount of computational resources and hand-holding in order to (maybe) reproduce three lines of code.
Exactly my thoughts. This is not useful at all. We already know how to write exact and correct code to implement that. This is no task that we should throw ANNs at.
It’s similar to comparing hardware radio and software-defined radio: Yes, we already know how to build a radio with hardware but a software-defined one offers greater flexibility.