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With a bit (OK, a lot) of reinforcement learning that prioritizes the best chains-of-thoughts, this compression engine becomes a generator of missing training d
by red75prime 2y ago
With a bit (OK, a lot) of reinforcement learning that prioritizes the best chains-of-thoughts, this compression engine becomes a generator of missing training data on how to actually think about something instead of trying to come up with the answer right away as internet text data suggests it should do.
That's the current ML technology. What you've described is the past. About 4 year old past to be precise.
- jandrese 2y agoThat's just tweaking the constants on the compression algorithm.
- red75prime 2y agoIf you think that "compression" somehow means "non-intelligent", consider this: The best compression of data that is theoretically achievable (see Kolmogorov complexity) is an algorithm that approximates process that produces the data. And which process produces texts on the internet? Activity of the human brain. (I described it a bit sloppily. We are dealing with probability distribution of the data, not the data itself. But the general idea still holds.) Using chain-of-thought removes the constraint that the output of the resultant algorithm should use fixed amount of compute per token.