161–170 of 328 posts
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They still have to publish something in major journals and have presence in major conferences.
a.k.a. - they still care enough to have some semblance of shame. There's the rub.
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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 thought the same thing - it smacks of desperation at the moment, any tiny win is exaggerated . It’s not hard to see why, with the emergence (ha) of OpenAI, Midjourney and all of this generative modelling, what has DeepMind done? I imagine the execs at Google are asking them some very probing questions on their mediocre performance over the last 5 years.
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"may have actually made things worse. Go players..." Go players are using AI to get better at Go.
That claim is often made, and never substantiated.
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that's what LLMs can with rl from human (or ai) readability feedback & instruction tuning + prompting. we will 100% see this if gpt-4 doesn't already do this.
I wouldn't classify any of the output I've seen so far as "optimally readable/understandable". Some if it looks pretty ok, especially where it overlaps with well established approaches.
This is why these systems are helpful in programming: they allow developers to think more about the design paradigms, and algorithmic solutions, rather than the fine grained code syntax and typing.
My hope (not prediction unfortunately, but *hope*) is that these systems will make people "better* programmers. This could happen by alleviating the requirement of typing out the code in a particular way, and allowing more time to really try out or think carefully about the correct solution for how to make their programs (i.e. multiprocessed, producer-consumers, distribute data with ipfs, faster algorithms, etc)
Not general or universal. Only for pre-trained data and with abysmal worst cases.
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I thought the same thing - it smacks of desperation at the moment, any tiny win is exaggerated . It’s not hard to see why, with the emergence (ha) of OpenAI, Midjourney and all of this generative modelling, what has DeepMind done? I imagine the execs at Google are asking them some very probing questions on their mediocre performance over the last 5 years.
Deepmind has done quite an enormous amount actually, but it's been in academia not in the commercial product sphere. Just because something is not on a little web page available to average Joe's does not mean there isn't value in it. For example, Deepmind's work towards estimating quantum properties of materials via density functional theory may not be the best toy for your grandma to play around with, but it certain…
Deep minds work on Density functional theory was complete rubbish, and everyone in computational chemistry knows it. They simply modelled static geometry and overfit their data, we wanted this methodology to work, computing DFT is expensive, and we did multiple months of rigorous work and the reality of the situation is that a bunch of machine learning engineers with a glancing amount of chemistry knowledge, made approximations that were way too naive, and announced it as a huge success in their typical fashion.
What they then count on is people not having enough knowledge of DFT / Quantum property prediction to query their work and make claims like “it certainly does move academia way further ahead” - which is total rubbish. In what way? Why aren’t these models being used in ab initio simulators now? The answer to that is simple: they are not revolutionary, in fact they are not even useful.
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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"
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.
More precisely, if the key length is w, then radix sort is O(w n) operations. In particular, if the n elements are distinct integers for example, w is greater than log(n).
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I thought the same thing - it smacks of desperation at the moment, any tiny win is exaggerated . It’s not hard to see why, with the emergence (ha) of OpenAI, Midjourney and all of this generative modelling, what has DeepMind done? I imagine the execs at Google are asking them some very probing questions on their mediocre performance over the last 5 years.
Deepmind has done quite an enormous amount actually, but it's been in academia not in the commercial product sphere. Just because something is not on a little web page available to average Joe's does not mean there isn't value in it. For example, Deepmind's work towards estimating quantum properties of materials via density functional theory may not be the best toy for your grandma to play around with, but it certain…
The most interesting part of this paper to me is that they let the agent guess how efficient it’s own solutions were and only had the model experimentally verify it’s guesses in 0.002% of cases. This allowed the model to search much faster than another program that didn’t guess and had to run every program.
This is the same sort of "more guesses faster beats smarter guesses slower" that made afl-fuzz by far the best at exploring large search spaces in program fuzzing Fast search often beats accurate search. Sometimes adding clever heuristics or more complex scoring "works" but slows down the search enough that it's an overall loss. Another kind of a bitter lesson, perhaps