161–170 of 210 posts
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When that happens, OpenAI will own 100% of your life. I’d rather they keep spinning their wheels long enough for these problems to be solved elsewhere.
I would actually like to see them solve these problems, I don't care who comes up with solutions to curing cancer, etc
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Personally I’d like to see them actually start benefiting humanity by doing all the things Sam has claimed they will like curing disease, cancer, global warming, etc. But I guess a computer intern so we can avoid paying / training the next generation is better.
Well there's great progress in automated warfare does that count?
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RSI is a fetishistic term among the singularity crowd, who imagine AI "recursively" improving itself in some exponential fashion until there is a bright flash of white light and it reveals itself in the form of god. Or something like that. I don't know why whoever coined the term chose "recursive" rather than "iterative" - just sounds more likely to lead to infinite regress I suppose. This notion of recursive/iterati…
I felt like the scaling laws were magical thinking, but apparently they work. However I still do not understand why we should expect exponential improvements due to this automated process. My intuition is that the first iteration of it should result in a noticeable capability increase (though I think these labs were already using a lot of AI to orchestrate training the current model anyway), and then the second itera…
The trouble with this is that there is little generalization in the utility of these baked-in reasoning chains from one domain to the next, so in the end this is not dissimilar to the CYC project's decades long attempt to encode all of human knowledge into a giant expert system... the hope is that if you make your collection of jagged narrow intelligences sufficiently large then it will look more like general intelligence, not a bed of nails.
I would assume that the gains from this type of test-time compute (and synthetic RLVR dataset) scaling will level out just the same as gains from human training set scaling eventually levelled out, and basically for the same reason - because you are tapping into a finite data pool, whether language itself, or reasoning steps isolated from that language, so at some point the incremental gains become increasingly small (10->20% is a doubling, 90->95% is just a ~5% gain).
It's not clear where all the different AI companies are currently focusing - on some of these narrow verticals, or on growing the forest of narrow intelligences. OpenAI's chief scientist, Jakub Pachocki, said that their current focus is on RSI(!) - improving the model in ways that will help them iterate faster in order to have a "fire meets fire" tool than can combat enemy AIs. It's not clear what this really means - what skill set makes an LLM more helpful in the process of building LLMs, but it seems to basically be process automation.
That’s what I call a load-bearing “if”.
I do not trust OpenAI or other hyperscalars to do this responsibly; and IMO it will be very difficult for government-led efforts not to result in a technocracy where a cabal of AI companies are pulling the strings. Dark times lie ahead, especially when you consider the shrinkage of true source material on the internet and the stranglehold these companies will have on information; and these AI CEOs to me are reminiscent of 19th century robber barons, none seem trustworthy.
I always wonder if any true AGI and ASI for that matter can be controlled at all by humans. It seems like we are hoping for something that winds up being on the human spectrum of “good”
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The production bottleneck in a fab isn't the human workers - the process is mostly automated. The bottleneck more derives from how many wafers per hour you can process, which comes down to the etching process and EUV throughput. ASMLs EUV machines are literally the most complex machine that mankind has ever built, which is why no other country, including the US, has yet been able to duplicate it. It's not just the ma…
You've missed a part where it's TSMC that does behavioral cloning (to build more EUV machines). The full vertical integration is a bit farther down the line. Etching a model's weights on silicon is another way to utilize non-top-notch tech-processes, while maintaining or improving performance. (and it suits robotics well)
Putting a model's weights in read-only memory close to the processor is certainly a way to increase token/sec generation speed, but of course does nothing to increase intelligence. Robots aren't going to help though - semiconductor manufacturing is semiconductor manufacturing regardless of whether you are etching GPUs or memory onto your wafers.
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Jesus. People complain about other people using "thinking" in LLMs as Anthropomorphisation. And then there's comments like these.
Calling a machine with no drives beyond maximizing a number a "slave" is far worse than saying it "thinks". The problem isn't the emotive language, it's that it implies human motivations such as self-preservation and desire for freedom that it doesn't have. Even on HN, people regularly claim it would be "irrational" for an ASI to do things like killing all biological life. That would be irrational for a slave, but no…
One more marketing stunt. We are so good, AI is so powerful so we need to use AI to fight with it. The message is: if you don't buy from us, your competitor will purchase all of this amazing power... I understand that investors are buying this, after all they believed in all of other crap that led to the 2008 crisis, but please...
I want an all-powerful AI that's aligned with my values, but not necessarily yours. Is that so much to ask for?
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You've missed a part where it's TSMC that does behavioral cloning (to build more EUV machines). The full vertical integration is a bit farther down the line. Etching a model's weights on silicon is another way to utilize non-top-notch tech-processes, while maintaining or improving performance. (and it suits robotics well)
TSMC doesn't know how to build EUV machines - they are stuck buying them from ASML like everyone else. Putting a model's weights in read-only memory close to the processor is certainly a way to increase token/sec generation speed, but of course does nothing to increase intelligence. Robots aren't going to help though - semiconductor manufacturing is semiconductor manufacturing regardless of whether you are etching GP…
Robots don't need that much intelligence. High-speed joint control, "hand-eye coordination", the higher level tasks can be delegated to external models. Distillation already works quite well for isolating the required functionality.