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I can't tell whether you're trying to convince humans, parody someone who might be, or give superficial sentiment for automated traders' webscrapers to be influenced by
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#32Was in a startup where we were trying to do this (our tagline was "using AI to make AI run faster and more efficiently"). But we ran out of funding at the end of '22 :( We were just a little early, I think.
Interesting, did you have any learnings that would apply to this problem now?
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#33I wonder if this type of work can be applied towards translating kernels between GPU vendors, e.g. CUDA → AMD. Does anyone know if that's possible or whether that kind of problem is AGI-complete?
It seems like it could be possible now with a bit of work. I don't think that it would require AGI. Didn't AMD have (or fund) something like this and then decide not to pursue it further recently? It was called HIP. There's also ZLUDA https://www.blopig.com/blog/2024/03/an-open-source-cuda-for-...
Very interesting.
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#35Calling beam search 'AI' is doing a lot of heavy lifting here. This is just superoptimization with a very expensive heuristic function.
That's correct - however as other commenters have noted. Doing this by hand is extremely challenging for human engineers working on tensor kernels.
The expense calculation might be
expense of improvement = (time taken per optimization step * cost of unit time ) / ( speedup - 1)
The expensive heuristic function is saving wall time well also being cheaper in cost of unit time. And as the paper shows the speed up provided for each unit time multiplied by unit cost of time is large.
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#36So, Trainium is an architecture that requires brute force to write software for.
Maybe if we invest $100 trillion in data centers, we can rewrite the Linux Kernel in Malbolge.
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#38Chris Latner of Apple's Swift and Tesla fame is running a company entirely predicated on this, but at the deterministic language design level rather than the inference level.
If a beam search, initiative plan and execute phase is more effective than having better tooling in a deterministic programming language then this will clearly take the lead.
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#39AI has told me that Biden was preparing for his upcoming debate with Trump. It told me that in May 2025. AI has told me its not raining in my city and that in fact there was 0% chance of it that day. As I was looking out my open front door watching a heavy downpour.
that is an indictment of the implementations, not the fundamental limits of the architecture; most commercial LLMs now have web-searching available by default and can do both of those things, but couldn't when they were confined to the user's prompt and their training data (which was often not quite contemporary, until recently)
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#40Optimization work sounds like it might be a really good fit for coding agents. If you can provide a robust test which "proves" the implementation works the actual work of increasing its performance is the kind of thing a coding agent could run in a loop, testing each optimization to see if the tests still pass and it runs faster.
But we might end up with "work on my infrastructure" optimization that would be hard to reproduce.
Like that research that evolved an FPGA where some unconnected parts where crucial for the the expected behaviour.
https://www.eetimes.com/whatever-happened-to-evolvable-hardw...