I found this essay very useful, thank you for writing it!
However, the CPU/GPU analogy doesn’t make sense to me. There are two main points of confusion: - I don’t know what you mean by “training your GPU software”. What is GPU software? What does it mean to train software? - If I understood correctly, you think logic is useful for verification and communication of intuitions. However, in analogizing logic to a CPU, you say that logic is useful to facilitate the training of your intuition. It doesn’t sound like verification and communication is part of facilitating training. Am I missing something?
Here’s my attempt at an alternate analogy: “Your intuition is the giant, high-performing neural net while your logic is the interpretable version of it that is derived from the original and sometimes performs worse, but is more communicable and can be inspected more easily”. Maybe this is closer to what you meant? Let me know.
By training GPU software I mean improving it in an indirect way, like how you train a neural net.
I think verification is part of training. You feed your intuition data, and use logic to detect when this process is going astray.
The GPU/CPU analogy is just to show that the intuition is a powerful parallel computing process, whereas the logical mind is not parallel and is computationally limited.
I found this essay very useful, thank you for writing it!
However, the CPU/GPU analogy doesn’t make sense to me. There are two main points of confusion:
- I don’t know what you mean by “training your GPU software”. What is GPU software? What does it mean to train software?
- If I understood correctly, you think logic is useful for verification and communication of intuitions. However, in analogizing logic to a CPU, you say that logic is useful to facilitate the training of your intuition. It doesn’t sound like verification and communication is part of facilitating training. Am I missing something?
Here’s my attempt at an alternate analogy: “Your intuition is the giant, high-performing neural net while your logic is the interpretable version of it that is derived from the original and sometimes performs worse, but is more communicable and can be inspected more easily”. Maybe this is closer to what you meant? Let me know.
By training GPU software I mean improving it in an indirect way, like how you train a neural net.
I think verification is part of training. You feed your intuition data, and use logic to detect when this process is going astray.
The GPU/CPU analogy is just to show that the intuition is a powerful parallel computing process, whereas the logical mind is not parallel and is computationally limited.