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As ai improves, most tasks will become something like this. Environments setup where the model learns through trial and error Any human endeavor that can be objectively verified in some environment like this can be completely automated
don't forget the size of the search space...
Earlier quoted context omitted.
Forgot the /s
AI currently lacks agency but if it can achieve greater goal setting and agency I can't see why self-improvement could not be achieved. I think the most disappointing thing will be that even we do achieve ASI, everything will carry on as business as usual for a while before it starts making an economic impact because of how resistant to change we have made society.
There are a lot of people working on the cause of the change. There are a lot of people criticising the nature of the change. There are a lot of people rejecting the change.
How many are there preparing the world for the change?
Some form of change is coming, how are we preparing society to deal with what is happening?
Job losses due to technology have happened over and over again. Rendering particular forms of employment redundant (typing pools, clearing horse manure, Video rental store workers, and of course, the loom). Most agree that the world is better when those are jobs that need to be done. It's the livelihood of the workers that is the concern.
Instead of fighting the change we need to address the inevitability of change the responsibility to those who it will affect.
Is there a Autoresearch for Jupyter somewhere? I point it to a Jupyter cell to improve based on another which calculates the target metric?
Up next: auto-autoresearch, LLMs searching for autoresearch harnesses and prompts that produce the best results
Something along the lines of auto research is what I have in mind for this psychology agent. It is currently working on training a model, with handholding right now.
Earlier quoted context omitted.
this is very far from hyperparameter tuning in at least three important ways: - it can modify code arbitrarily, the notion of a "hyperparameter" dissolves - there is no need to run "sweeps" - this is the standard parallel process that wastes compute. because LLM agents are sequential, they can do more efficient versions such as binary search to narrow in on the right setting very quickly (usually many parameters will…
How about the very last "Kept Improvement" in the plot? It's titled "random seed 42 -> 137". I do think this project is quite conceptually interesting, but the model literally choosing a different random seed to achieve lower loss feels pretty far removed from the flowery sci-fi writing at the top of the readme.
https://github.com/karpathy/autoresearch/discussions/32
Look at its comment about this "improvement":
""" Surprising non-results:
- Changing random seed from 42→137 improved by 0.0004. Seed 7 was worse. Make of that what you will. """
So the model knows! It knows that this is a weird thing to do after the fact. I think it's silly that the model even tried and that it ran this, but some part of it also knows that it was wrong. This means that this is fixable by prompt.md
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Up next: auto-autoresearch, LLMs searching for autoresearch harnesses and prompts that produce the best results
[1] https://karpathy.github.io/2015/05/21/rnn-effectiveness/
"In particular, setting temperature very near zero will give the most likely thing that Paul Graham might say:
“is that they were all the same thing that was a startup is that they were all the same thing that was a startup is that they were all the same thing that was a startup is that they were all the same”
looks like we’ve reached an infinite loop about startups."