Latest Notable AI accomplishments

  • Topic: AI 
  • Thread starter Thread starter gleem
  • Start date Start date
  • Tags Tags
    Ai
Join the discussion
Registration is free. Ask a follow-up in this thread, or start your own.
Sign up Log in
132 replies · 34K views
Physics news on Phys.org
722 new papers by OpenAI
Full list
The vast majority of results were obtained with the same procedure using an unreleased internal OpenAI model. On average, each result used three hours of ChatGPT Pro thinking compute with that model. Over the course of the evaluation, the model was posed approximately 4,000 problems. Aggregating the output into result families and manuscripts and requiring an appropriate level of significance led to the catalog outlined above.
 
Reply
  • Like
  • Informative
Likes   Reactions: Demystifier, Frimus, Greg Bernhardt and 1 other person
The next question is how these solutions are to be celebrated, if at all. OpenAI is not a math research foundation. While they may have mathematicians on staff, they have not released their solutions in a currently accepted, peer-reviewed form. Questions about unexplained, unusual methods and plagiarism persist.

Mathematicians are becoming increasingly unhappy with these bulk releases of math solutions.
https://www.wired.com/story/openai-is-pissing-off-a-bunch-of-mathematicians-again/
“Math by tweet and math by press release to me is not the way to nurture the ecosystem that created the fertile ground that they have trained on,” Northwestern University mathematician Bryna Kra

Supposedly, some employees at OpenAI have declared that math is dead. How many grad students' careers have been upended by the corporate exploitation of math problem solutions with their AI models? How many UGs are reevaluating their career? But then again, who needs these "would be" mathematicians? :wink:
 
gleem said:
Questions about unexplained, unusual methods and plagiarism persist.
They persist for the Navier-Stokes solution (and many other suspicious incidents), but not for these 722 new papers. Also, 3 hours of a single agent per result is much more acceptable than 88 hours of 10000 agents for one brute-forced result.

Mathematicians have every right to be pissed-off about the unfair $15 million dollar scoop. But they also accept that OpenAI tried to behave fair this time, and did coordinate with the „representatives“ the mathematicians had quickly assembled.
 
Last edited:
gleem said:
How many grad students' careers have been upended by the corporate exploitation of math problem solutions with their AI models? How many UGs are reevaluating their career?
Since Anthropic is next to go IPO (I guess mid November 2026), it is understandable that OpenAI tries really hard to showcase the strength of its internal models. If Anthropic manages to push-through its targeted $2 trillion market capitalization, OpenAI‘s IPO in 2027 could become a disappointment.

Maybe OpenAI targeted the „low hanging fruits“ for its current internal model, to make it at least a bit more difficult for Anthropic. I am curious what we will see from Anthropic before their IPO.
 
gentzen said:
Also, 3 hours of a single agent per result is much more acceptable than 88 hours of 10000 agents for one brute-forced result.
What's the difference?
 
gmax137 said:
What's the difference?
3 hours of a single agent is affordable, while 88 hours of 10000 agents burning $15 million dollar is not.
https://agmai.org/general-sep29/

3. Ensuring broad access​

[…]
As for publicly available models, unequal access due to economic and other factors risks establishing a multi-tier hierarchy across the mathematical world and greatly exacerbating existing inequalities that arise from institutional wealth and technological privilege.
 
From my understanding, the Riemann Hypothesis critical strip paper would be huge if true. I'm curious how long that one in particular took.
 
An amateur astronmer used Claude and Codex to find an exoplanet candidate. His results were good enough that he was given a slot on the TESS observatory.

 
Borg said:
An amateur astronmer used Claude and Codex to find an exoplanet candidate. His results were good enough that he was given a slot on the TESS observatory.

Using AI as a tool is the way to get ahead, at least for now. The next step in its use is that of an associate. After that, a competitor.

gentzen said:
Mathematicians have every right to be pissed-off about the unfair $15 million dollar scoop. But they also accept that OpenAI tried to behave fair this time, and did coordinate with the „representatives“ the mathematicians had quickly assembled.


It was reported that OpenAI would release its math solutions piecemeal. Dumping hundreds at once on GitHub does not seem well coordinated. I guess one could say that because of the need to verify the results, mathematicians will be busy for a while.

Some believe that AI generated math will require more mathematicians.
https://terrytao.wordpress.com/2026/09/24/were-gonna-need-a-lot-more-mathematicians/
But this author hopes that we will always take AI generated tasks with a grain of salt and verify the validity of its results. The problem is that we are not doing that now to the degree we should, especially in understanding AI itself.
 
AI is significantly improving weather predictions.

OpenAI has developed a model that can forecast cyclone development one more day sooner than before.

https://www.wired.com/story/deepmind-ai-model-can-predict-hurricanes-earlier/
In a paper published on Thursday in Nature, researchers show that the WeatherNext AI model can predict cyclones with unprecedented accuracy. On average, it gives forecasters a day more lead time than existing models; this means its predictions three days out are as accurate as previous models’ predictions two days out. On the ground, that extra day can mean a lot.

A collaboration at MIT has developed an AI system to increase the accuracy of severe weather two to six weeks in advance.

https://news.mit.edu/2026/researche...out-1014?utm_source=substack&utm_medium=email
“Not only does this do better on time averaged forecasts for this range, which is already very useful for farmers and members of the energy industry trying to match supply with demand, but it also improves predictions of extreme weather events, whether it’s flooding or cold spells or something else,” co-author and MIT Research Scientist Judah Cohen says. “It’s really important to predict both, but extreme events might be the most impactful part of this for society.”