151–160 of 328 posts
I (don't) want to see an agent learn to rowhammer the memory address storing its reward quantity.
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To what extent is this simply working around the weirdness of x86? Do these improvements apply to something like MIPS, ARM64, or RISC-V that have inherently simpler ISAs?
In this particular case they were universal but in paper it's said the optimizations were done on x86. One of the ideas was to use LLVM IR but intuition for optimizer over optimizer was unlikely to work properly.
Wut?
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This is DeepMind's modus operandi. Every press release is just utterly hyperbolic nonsense that doesn't withstand the slightest scrutiny. AlphaGo, AlphaFold, AlphaDev... they've done literally nothing to improve the human condition, and may have actually made things worse. Go players have DECREASED their Elo after playing AlphaGo (or just quit the game altogether). I would be embarrassed to be associated with DeepMin…
I think this is a bit far in the other direction. Deepmind's stuff is often deeply impressive, they just have a tendency to exaggerate on top of that.
There must be a "demonstrate (1) DeepMind #Win per " requirement somewhere that gets the once-over from the marketing dept. to meet some MBOs.
If that can be done say OS wide I could see that having an actual impact on global energy usage
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for example, matrix multiplication has all sorts of algorithms with exponent significantly less than 3, but they are nearly all curiosities. In practice, the straightforward N^3 algo is used nearly everywhere
I had a long discussion with Rob Pike once that ended with me saying "you know, I don't think people actually use Strassen on supercomputers" (we were discussing how somebody had managed to show an N*2.37 algorithm or something like that and "it was a new world's record").
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any sort that only uses comparisons is provably not better than n log n. Hence whatever Thorup is, it doesn't work for arbitrary input, and must assume something further, something like "all elements under 1000 or something"
I think the parent's argument is that, since they only evaluated their algorithms on sorting arrays of 32-bit or 64-bit integers, it is fair game to compare against integer sorting algorithms for which we have better complexities than O(nlgn).
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The Gleason bound is n log(n) and says that no sorting algorithm based on comparing pairs of keys can be faster. Heap sort meets the Gleason bound so is the fastest possible in this context. Actually the usual versions of quick sort are slower. If the keys are not too long, radix sort is O(n) and faster. All this has been well known for decades. I explained a little more in another post in this thread.
I think you are getting your information mixed up. Here is a comparison that shows quicksort running in 1/10th the time as heapsort. https://johnysswlab.com/why-is-quicksort-faster-than-heapsor...
> AlphaDev uncovered new sorting algorithms that led to improvements in the LLVM libc++ sorting library that were up to 70% faster for shorter sequences and about 1.7% faster for sequences exceeding 250,000 elements. As someone that knows a thing or two about sorting... bullshit. No new algorithms were uncovered, and the work here did not lead to the claimed improvements. They found a sequence of assembly that saves.…
This is DeepMind's modus operandi. Every press release is just utterly hyperbolic nonsense that doesn't withstand the slightest scrutiny. AlphaGo, AlphaFold, AlphaDev... they've done literally nothing to improve the human condition, and may have actually made things worse. Go players have DECREASED their Elo after playing AlphaGo (or just quit the game altogether). I would be embarrassed to be associated with DeepMin…
Don't get me wrong, Sutskyver (et al) has done incredibly good work previously, but when it comes to the products, they're much more engineering and marketing polish than scientific endeavours.
Botvinick's work on metaRL for example is an interesting direction that Deepmind has shown that few other companies that are only interested in engineering would venture towards.