I don't understand your analogy. Are you just suggesting that luck plays too large a role in this contest? Clearly there is some "skill" or ability factor because AI's have been scoring higher and higher each year. Also, they make reference to superforecaster humans, who are presumably consistently better at forecasting than their peers.
i'm not looking at the guesses, i'm looking at the distribution of guesser success rates. assuming random distribution, counterintuitively enough, you would expect some guessers to appear much better or much worse than others. i know i'm not the first person to think of this, so i'm asking what's been done to mitigate it because i can't find anything. if you expect x% of guesses to be within two std devs of the mean that means you can expect 100-x% to be outside that, even without any guessers actually being better at guessing than any of the others.
Climate as a pattern of weather over a long period of time. If the climate is increasingly unpredictable, I would think that it wouldn't really effect our ability to make short-term predictions, like a few days out.
But our ability to forecast weather on a longer timeline, like for industrial forecasting, is calibrated on historical weather patterns. But with weather being more erratic and unusual, I don't understand how AI will be forecasting with the models they have now.
The primary goal of all basic sciences is human understanding. "Truth" is no more a goal for mathematicians than the physical laws are a goal to physicists; they simply exist in nature. The goal is rather to develop useful language and conceptual frameworks for reasoning and communicating. That understanding underpins all practical applications.
Sciences don't have goals, people have goals and they differ. Some are fans of pure math as a kind of religious or almost erotic activity in elegance and beauty, others are application minded. Some are in it for the community and outreach and conferences, some are in it to just sit in an office alone and be left alone to do it in a zen like flow state all day and night. Some treat it as a 9-5 to pay the bills with a skill they happen to be fit for but aren't especially passionate about.
If the new model is that good, and is chewing through open problems at an unprecedented rate, the smart move would have been to let the humans have their W on this one and present solutions to those other problems.
Especially if there really is a long list of them.
"Here are a few hundred proofs" is far more convincing than "We really Navier Stokes and coincidentally someone else did too but we don't know the details or anything, who us, definitely not."
It's a PR fiasco, and a cynic might wonder if it's entirely about the IPO.
I'm consistently entertained by how these companies, with the most advanced models on the planet, consistently do the most idiotic things.
It seems a perfectly reasonable possibility that Navier-Stokes is just the most easily solvable of the remaining problems, and that their new model is capable of solving it while not being capable of solving the others.
There's many cases of researchers racing to solve various problems after hearing that others are working on them. I don't think anyone's suggesting that it was a coincidence at all. In fact, OpenAI freely admits that they started working on the problem after hearing rumours that others were close to solving it. To me, that's not evidence of "cheating" in any way.
Is it really such an extraordinary claim to say that they could have solved the problem without copying Buckmaster and Alpoge? It seems very much in the realm of possibilities.
To me, it seems just as extraordinary to claim that they did "cheat". If I were a betting man, I would put the odds around 50/50 from everything I've read on the subject.
But my point is that everyone seems to be presuming guilt.
It is an extraordinary claim, it is a millenium prize problem after all. We don't even know, even if there was no copying, how much human involvement there was in the result.
>For other workers: wages may increase on paper, but not really for actual purchasing power.
This is not necessarily a conclusion you can draw from that statement. It's possible that as the economic pie grows, workers' purchasing power increases compared to the counterfactual (a non-AI world), but their purchasing power does not increase as much as the capital owners' does.
According to data in the site and paper [0] labor income is estimated $20T for all four scenarios (including no AI) in 2030. Which means capital takes ALL of the new pie coming from AI.
Also, I am not an economist but I assume if you are not growing as much as GDP, it basically means you’re shrinking. Relatively shrinking, but since everything is relative in an economy you are actually shrinking. Table 4 in paper gives the numbers.
I think that would only follow if the rest of the world’s purchasing power also grows at the same rate (unlikely), or if you were to cease all international trade (uhh…).
Except, so far, this isn't actually what happens. I listened to Moody's "Inside Economics" podcast with Ramp's Chief Economist, and they observe that companies who adopt AI intensively actually hire 10% more people. Also, this article from The Economist suggests that evidence is mixed-to-positive for long term employment, while short term data center buildout is extremely positive for jobs. Paywalled unfortunately.
Because he works for Anthropic. Supposedly this project was not part of his official capacity as an employee of theirs.
However, if they had published first, it's hard to imagine Anthropic not taking the opportunity to claim "our employee solved this Millennium prize problem using our AI".
> [the Open AI rep] twice asserted that he wanted Levent removed from authorship, and said it would all be simple if only it were not the case that, and it was so annoying that, Levent works at Anthropic.
Later on the author claims that the OpenAI rep threatened to ruin his career if he didn't go along with them.
Worth noting that there were two versions of the problem:
- the proof in the equations with viscosity (which OpenAI claims to have solved), and
- the proof with no viscosity (which Tristian and Levent solved)
What is confusing is that if OpenAI can prove their independence from Levent and Tristian, they could take full credit for proving the viscous version of the problem. Offering to give one author credit for something they proved seems like a strange choice: if nothing else it seems obvious that it would drive a very deep wedge between the two authors of the non-viscous version.
It seems very hard for OpenAI to prove that independence. Since they seem unable to exclude the possibility that their model was trained on transcripts by the two mathematicians.
Even if true, I don't see why this is an issue. Are they not allowed to work on problems others are working on? Did Anthropic get first dibs on this problem? Competition is good. And I don't exactly have tons of sympathy when the other side is just a leading AI lab. It's not like it's some scholar who dedicated his life to this problem.
If we accept that it is a proof then it does improve human knowledge, even if no one can understand how to get there.
If you were navigating a pitch dark cave, wouldn't you find it useful to be able to see the light of the cave opening even if it's not bright enough to illuminate your path to it?
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