Recent comments in /f/MachineLearning

TFenrir t1_jdim3vv wrote

Reply to comment by light24bulbs in [N] ChatGPT plugins by Singularian2501

That is a really good tip.

I'm using langchainjs (I can do python, but my js background is 10x python) - one of the things I want to play with more is getting consistent json output from a response - there is a helper tool I tried with a bud a while back when we were pairing... Typescript validator or something or other, that seemed to help.

Any tips with that?

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light24bulbs t1_jdijmr3 wrote

Reply to comment by TFenrir in [N] ChatGPT plugins by Singularian2501

My strategy was to have the outer LLM make a JSON object where one of the args is an instruction or question, and then pass that to the inner LLM wrapped in a template like "given the following document, <instruction>"

Works for a fair few general cases and it can get the context that ends up in the outer LLM down to a few sentences aka few tokens, meaning there's plenty of room for more reasoning and cost savings

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inglandation t1_jdijeu5 wrote

Reply to comment by omgpop in [D] "Sparks of Artificial General Intelligence: Early experiments with GPT-4" contained unredacted comments by QQII

> One way to get really good at approximating what a human would likely write given certain information would be to actually approximate human cognitive structures internally.

Yes, I hope that we'll be able to figure out what those structures are, in LLMs and in humans. It could also help us figure out how to align those models better if we can create more precise comparisons.

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inglandation t1_jdij4o8 wrote

Reply to comment by Username2upTo20chars in [D] "Sparks of Artificial General Intelligence: Early experiments with GPT-4" contained unredacted comments by QQII

> why should the next generation be fundamentally different?

Emergent abilities from scale are the reason. There are many examples of that in nature and many fields of study. The patterns of snowflakes cannot easily be explained by the fundamental properties of water. You need enough water molecules in the right conditions to create the patterns of snowflakes. I suspect that a similar phenomenon is happening with LLMs, but we haven't figured out yet what the patterns are and what are the right conditions for them to materialize.

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LifeScientist123 t1_jdiis55 wrote

Reply to comment by nicku_a in [P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up by nicku_a

I'm also new to this so forgive me if this is a dumb question. My understanding was that RL is superior to evolutionary algorithms because in evolutionary algos "mutation" is random, so you evaluate a lot of dud "offspring". In RL algos, eg MCTS, you also do tree search randomly, but you're iteratively picking the best set of actions, without evaluating many dud options. Am I wrong? Somehow mixing RL with evolutionary algorithms seems like a step backwards

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TikiTDO t1_jdiirji wrote

Reply to comment by harharveryfunny in [D] I just realised: GPT-4 with image input can interpret any computer screen, any userinterface and any combination of them. by Balance-

For a computer words are just bits of information. If you wanted a system that used text to communicate this info, it would just assign some values to particular words, and you'd probably end up with ultra long strings of descriptions relating things to each other using god knows what terminology. It probably wouldn't really make sense to you if you were reading it because it would just be a text-encoded representation of an embedding vector describing finer relations that would only make sense to AIs.

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