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Just curious. How do you square the rise in AI stocks taking so long? Many people here thought it was obvious since 2022 and made a ton of money.
Keep in mind the current administration is replacing incompetent bureaucracies with self assembling corporations. The organization is still there, just more competent and under a different name. A government project could just look like telling the labs to create 1 data center, throwing money at them, and cutting red tape for building gas plants.
Seems increasingly likely to me that there will be some kind of national AI project before AGI is achieved as the government is waking up to its potential pretty fast. Unsure what the odds are, but last year, I would have said <10%. Now I think it's between 30% and 60%.
Has anyone done a write up on what the government-led AI project(s) scenario would look like?
It might just be a perception problem. LLMs don't really seem to have a good understanding of a letter being next to another one yet or what a diagonal is. If you look at arc-agi with o3, you see it doing worse as the grid gets larger with humans not having the same drawback.
EDIT: Tried on o1 pro right now. Doesn't seem like a perception problem, but it still could be. I wonder if it's related to being a succcesful agent. It might not model a sequence of actions on the state of a world properly yet. It's strange that this isn't unlocked with reasoning.
Ah so there could actually be a large compute overhang as it stands?
Does deepseek v3 imply current models are not trained as efficiently as they could be? Seems like they used a very small fraction of previous models resources and is only slightly worse than the best LLM.
They did this on the far easier training set though?
An alternative story is they trained until a model was found that could beat the training set but many other benchmarks too, implying that there may be some general intelligence factor there. Maybe this is still goodharting on benchmarks but there’s probably truly something there.
No, I believe there is a human in the loop for the above if that’s not clear.
You’ve said it in another comment. But this is probably an “architecture search”.
I guess the training loop for o3 is similar but it would be on the easier training set instead of the far harder test set.
I think there is a third explanation here. The Kaggle model (probably) does well because you can brute force it with a bag of heuristics and gradually iterate by discarding ones that don't work and keeping the ones that do.
I have ~15% probability humanity will invent artificial superintelligence (ASI) by 2030.
The recent announcement of the o3 model has updated me to 95%, with most of the 5% being regulatory slow downs involving unprecedented global cooperation.
I think a lot of this is wishful thinking from safetyists who want AI development to stop. This may be reductionist but almost every pause historically can be explained economics.
Nuclear - war usage is wholly owned by the state and developed to its saturation point (i.e. once you have nukes that can kill all your enemies, there is little reason to develop them more). Energy-wise, supposedly, it was hamstrung by regulation, but in countries like China where development went unfettered, they are still not dominant. This tells me a lot it not being developed is it not being economical.
For bio related things, Eroom's law reigns supreme. It is just economically unviable to discover drugs in the way we do. Despite this, it's clear that bioweapons are regularly researched by government labs. The USG being so eager to fund gof research despite its bad optics should tell you as much.
Or maybe they will accidentally ban AI too due to being a dysfunctional autocracy -
I remember many essays from people all over this site on how China wouldn't be able to get to X-1 nm (or the crucial step for it) for decades, and China would always figure a way to get to that nm or step within a few months. They surpassed our chip lithography expectations for them. They are very competent. They are run by probably the most competent government bureaucracy in the world. I don't know what it is, but people keep underestimating China's progress. When they aim their efforts on a target, they almost always achieve it.
Rapid progress is a powerful attractor state that requires a global hegemon to stop. China is very keen on the possibilities of AI which is why they stop at nothing to get their hands on Nvidia GPUs. They also have literally no reason to develop a centralized project they are fully in control of. We have superhuman AI that seem quite easy to control already. What is stopping this centralized project on their end. No one is buying that even o3, which is nearly superhuman in math and coding, and probably lots of scientific research, is going to attempt world takeover.
We still somehow got the steam engine, electricity, cars, etc.
There is an element of international competition to it. If we slack here, China will probably raise armies of robots with unlimited firepower and take over the world. (They constantly show aggression)
The longshoreman strike is only allowed (I think) because the west coast did automate and somehow are less efficient than the east coast for example.
Oh I guess I was assuming automation of coding would result in a step change in research in every other domain. I know that coding is actually one of the biggest blockers in much of AI research and automation in general.
It might soon become cost effective to write bespoke solutions for a lot of labor jobs for example.
Why would that be the likely case? Are you sure it's likely or are you just catastrophizing?
catastrophic job loss that destroys the global economy?
I expect the US or Chinese government to take control of these systems sooner than later to maintain sovereignty. I also expect there will be some force to counteract the rapid nominal deflation that would happen if there was mass job loss. Every ultra rich person now relies on billions of people buying their products to give their companies the valuation they have.
I don't think people want nominal deflation even if it's real economic growth. This will result in massive printing from the fed that probably lands in poeple's pockets (Iike covid checks).
While I'm not surprised by the pessimism here, I am surprised at how much of it is focused on personal job loss. I thought there would be more existential dread.
It’s better at questions but subjectively there doesn’t feel like there’s much transfer. It still gets some basic questions wrong.
O1’s release has made me think Yann Lecun’s AGI timelines are probably more correct than shorter ones
Why is the built-in assumption for almost every single post on this site that alignment is impossible and we need a 100 year international ban to survive? This does not seem particularly intellectually honest to me. It is very possible no international agreement is needed. Alignment may turn out to be quite tractable.
I guess in the real world the rules aren’t harder per se but just less clear and not written down. I think both the rules and tools needed to solve contest math questions at least feel harder than the vast majority of rules and tools human minds deal with. Someone like Terrence Tao, who is a master of these, excelled in every subject when he was a kid (iirc).
I think LLMs have a pretty good model of human behavior, so for anything related to human judgement, in theory this isn’t why it’s not doing well.
And where rules are unwritten/unknown (say biology), are the rules not at least captured by current methods? The next steps are probably like baking the intuitions of something like alphafold into something like o1. Whatever that means. R&D is what’s important and there is generally vast sums of data there.
O1 probably scales to superhuman reasoning:
O1 given maximal compute solves most AIME questions. (One of the hardest benchmarks in existence). If this isn’t gamed by having the solution somewhere in the corpus then:
-you can make the base model more efficient at thinking
-you can implement the base model more efficiently on hardware
-you can simply wait for hardware to get better
-you can create custom inference chips
Anything wrong with this view? I think agents are unlocked shortly along with or after this too.
Where are all the successful rationalists?
https://x.com/JDVance/status/1854925621425533043
Is it too soon to say a rationalist is running the White House?
https://x.com/arcprize/status/1849225898391933148?s=46&t=lZJAHzXMXI1MgQuyBgEhgA
My read of the events. Anthropic is trying to raise money and rushed out a half baked model.
3.5 opus has not yet had the desired results. 3.5 sonnet, being easier to iterate on, was tuned to beat OpenAI’s model on some arbitrary benchmarks in an effort to wow investors.
With the failed run of Opus, they presumably tried to get o1 like reasoning results or some agentic breakthrough. The previous 3.5s was also particularly good because of a fluke of the training run rng (same as gpt4-0314), which makes it harder for iterations to beat it.
They are probably now rushing to scale inference time compute. I wonder if they tried doing something with steering vectors initially for 3.5 opus.
A while ago I predicted that I think there's a more likely than not chance Anthropic would run out of money trying to compete with OpenAI, Meta, and Deepmind (60%). At the time and now, it seems they still have no image video or voice generation unlike the others, and do not process image as well in inputs either.
OpenAI's costs are reportedly at 8.5 billion. Despite being flush in cash from a recent funding round, they were allegedly at the brink of bankruptcy and required a new, even larger, funding round. Anthropic does not have the same deep pockets as the other players. Big tech like apple who are not deeply invested in AI seem to be wary of investing in OpenAI. It stands to reason, Amazon may be as well. It is looking more likely that Anthropic will be left in the dust (80%),
The only winning path I see is a new more compute efficient architecture emerges, they are first, and they manage to kick of RSI before more funded competitors rush in to copy them. Since this seems unlikely I think they are not going to fare well.
Really? He seems pretty bullish. He thinks it will co author math papers pretty soon. I think he just doesn’t think or at least state his thoughts on implications outside of math.
Except billionaires give out plenty of money for philanthropy. If the AI has a slight preference to keeping humans alive, things probably work out well. Billionaires have a slight preference to things they care about instead of random charities. I don’t see how preferences don’t apply here.
This is a vibes based argument using math incorrectly. A randomly chosen preference from a distribution of preferences is unlikely to involve humans, but that’s not necessarily what we’re looking at here is it.
The chip export controls are largely irrelevant. Westerners badly underestimate the Chinese and they have caught up to 7nm at scale. They also caught up to 5nm, but not at scale. The original chip ban was meant to stop China from going sub 14nm. Instead now we may have just bifurcated advanced chip capabilities.
The general argument before was "In 10 years, when the Chinese catch up to TSMC, TSMC will be 10 years ahead." Now the only missing link in the piece for China is EUV. And now the common argument is that same line with ASML subbed in for TSMC. Somehow, I doubt this will be a long term blocker.
Best case for the Chinese chip industry, they just clone EUV. Worst case, they find an alternative. Monopolies and first movers don't often have the most efficient solution.
Talk through the grapevine:
Safety is implemented in a highly idiotic way in non frontier but well-funded labs (and possibly in frontier ones too?).
Think raising a firestorm over a 10th leading mini LLM being potentially jailbroken.
The effect is employees get mildly disillusioned with saftey-ism, and it gets seen as unserious. There should have been a hard distinction between existential risks and standard corporate censorship. "Notkilleveryoneism" is simply too ridiculous sounding to spread. But maybe memetic selection pressures make it impossible for the irrelevant version of safety to not dominate.
Talk is cheap. It's hard to say how they will react as both risks and upsides remain speculative. From the actual plenum, it's hard to tell if Xi is talking about existential risks.
Red-teaming is being done in a way that doesn't reduce existential risk at all but instead makes models less useful for users.
https://x.com/shaunralston/status/1821828407195525431
In other contexts, it seems it's quite common for a disgruntled employee to go to a journalist and blow up a minor problem. Why can't this similarly be abused if the bar isn't high?
Feels like Test Time Training will eat the world. People thought it was search, but make alphaproof 100x efficient (3 days to 40 minutes) and you probably have something superhuman.
This part seems to just be to not allow an LLM translation to get the problem slightly wrong and mess up the score as a result.
It would be a shame for your once a year attempt to have even a 2% chance of being messed up by an LLM hallucination.
https://x.com/wtgowers/status/1816839783034843630
It wasn't told what to prove. To get round that difficulty, it generated several hundred guesses (many of which were equivalent to each other). Then it ruled out lots of them by finding simple counterexamples, before ending up with a small shortlist that it then worked on.
That comment doesn’t seem to be correct.
I think a lot of it is simply just eating away at the margins of companies and product that might become larger in the future. Even if they are not direct competitors, it's still tech investment money going away from their VR bets into AI. Also big companies fully controlling important tech products has proven to be a nuisance to Meta in the past.
I'm guessing many people assumed an IMO solver would be AGI. However this is actually a narrow math solver. But it's probably useful on the road to AGI nonetheless.
I predict the move to Texas will be largely fake and just whining to get CA politicians to listen to his policy suggestions. They will still have a large office in California.
This is a reconstruction of Roman GDP per capita. Source of image. There is ~200 years of quick growth followed by a long and slow decline. I think it's clear to me we could be in the year 26, extrapolating past trends without looking at the 2nd derivative. I can't find a source of fertility rates, but child mortality rates were much higher then so the bar for fertility rates was also much higher.
For posterity, I'll add Japan's gdp per capita. Similar graphs exist for many of the other countries I mention. I think this is a better and more direct example anyways.
It is plausible that technological and political progress might get it to fulfilling all Sustainable Development Goal
This seems highly implausible to me. The technological progress and economic growth trend is really an illusion. We are already slowly trending in the wrong direction. The U.S. is an exception and all countries are headed towards Japan or Europe. Many of those countries have declined since 2010 or so.
If you plotted trends from the Roman Empire but ignored the same population decline/institutional decay from them we should have reached technological goals a long time ago.
It’s always hard to say whether this is an alignment or capabilities problem. It’s also too contrived too offer much signal.
The overall vibe is these LLMs grasp most of our values pretty well. They give common sense answers to most moral questions. You can see them grasp Chinese values pretty well too, so n=2. It’s hard to characterize this as mostly “terrible”.
This shouldn’t be too surprising in retrospect. Our values are simple for LLMs to learn. It’s not going to disassemble cows for atoms to end racism.There are edge cases where it’s too woke, but these got quickly fixed. I don’t expect them to ever pop up again.
much of what they say on matters of human values is actually pretty terrible
Really? I’m not aware of any examples of this.
TSMC has multiple fabs outside of Taiwan. It would be a setback but 10+ years seems to be misinformed. Also there would likely be more effort to restore the semi supply chain than post covid. (I could see the military try being mobilized to help or the Defense Production Act being used)
If OpenAI didn’t get the 30m from any other donor, they’d probably just turn into a capped profit earlier and raise money that way.
Also I never said Elon would have been the one to donate. They had 1B pledged, so they could have conceivably gotten that money from any other donors.
By the backing of Elon Musk, I mean the startup is associated with his brand. I’d imagine this would make raising funding easier.
It’s a lot for AI safety but for OpenAI at the time, with the backing of Elon Musk and the most respected AI researchers in the country, they could have raised a similar amount for series A funding at the time. (I’m unsure if they were a capped profit yet). Likewise, 1B was pledged to them at their founding, but it’s hard to tell how much was actually distributed out by 2017.
Agree with 2, but Safety research also seems hard to fund.
Shorting nvidia might be tricky. I’d short nvidia and long TSM or an index fund to be safe at some point. Maybe now? Typically the highest market cap stock has poor performance after it claims that spot.
I see Elon throwing money into this. He originally recruited Sutskever and he’s probably(?) smart enough to diversify his AGI bets.
Oh yeah I agree. Misread that. Still, maybe not so confident. Market leaders often don’t last. Competition always catches up.
OpenAI is closed
StabilityAI is unstable
SafeSI is ...
I think a more nuanced take is there is a subset of generated outputs that are hard to verify. This subset is split into two camps, one where you are unsure of the outputs correctness (and thus can reject/ask for an explanation). This isn’t too risky. The other camp is ones where you are sure but in reality overlook something. That’s the risky one.
However at least my priors tell me that the latter is rare with a good reviewer. In a code review, if something is too hard to parse, a good reviewer will ask for an explanation or simplification. But bugs still slip by so it’s imperfect.
The next question is whether the bugs that slip by in the output will be catastrophic. I don’t think it dooms the generation + verification pipeline if the system is designed to be error tolerant.