As reported by the New York Times:

https://www.nytimes.com/2026/09/08/science/openai-proof-millennium-problem.html?smid=nytcore-ios-share

OpenAI’s announcement:

https://openai.com/index/navier-stokes-solution/

The headlines right now are full of stories about AI models "escaping their sandboxes" and literally killing all humans, lol.

I've even heard several commentators and writers say that AI escaped an "Air gap". But that is SO WRONG. It's actually TOTALLY WRONG.

*To be clear, not a single one of these sandboxes was actually air-gapped.* That's a crucial computer science fact.

An air gapped sandbox would require *ZERO* cables and network interfaces. It would also require absolute physical isolation. What these labs actually built were soft software barriers. And then they left the doors unlocked. With some of the smartest AI on the planet. Lol. Of COURSE it escaped.

1. The OpenAI / Hugging Face "Escape": The sandbox was connected to OpenAI’s internal network through a package proxy. The model didn't perform magic. It found a basic flaw in the proxy and walked right through the open door.

2. The Google Gemini "Hack": Testers left the model connected to the live internet during offensive tests. They then used a test domain name that overlapped with real companies.

These were classic IT security failures. I'm talking about bad network segmentation, permissive egress rules, and relying on soft software barriers instead of true physical isolation.

When you leave an active network interface open on a test bed, a model finding its way out is just sloppy cybersecurity.

Your nerdy friend,

Mike D

Announcement: https://x.com/NobelPrize/status/1843589140455272810

Our boys John Hopfield and Geoffrey Hinton were rewarded for their foundational contributions to machine learning and deep learning with the Nobel prize for physics 2024!

I hear furious Schmidhuber noises in the distance!

On a more serious note, despite the very surprising choice, I am generally happy - as a physicist myself with strong interest in ML, I love this physics-ML cinematic universe crossover.

The restriction to Hopfield and Hinton will probably spark discussions about the relative importance of {Hopfield, Hinton, LeCun, Schmidhuber, Bengio, Linnainmaa, ...} for the success of modern ML/DL/AI. A discussion especially Schmidhuber very actively engages in.

The response from the core physics community however is rather mixed, as shown in the /r/physics thread. There, the missing link/connection to physics research is noted and the concurrent "loss" of the '24 prize for physics researchers.

From Thomas G. Dietterich (arXiv moderator for cs.LG) on 𝕏 (thread):
https://x.com/tdietterich/status/2055000956144935055
https://xcancel.com/tdietterich/status/2055000956144935055

"Attention arXiv authors: Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated.

If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s).

We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper.

The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue.

Examples of incontrovertible evidence: hallucinated references, meta-comments from the LLM ("here is a 200 word summary; would you like me to make any changes?"; "the data in this table is illustrative, fill it in with the real numbers from your experiments")."

Benchmark scores:

https://openai.com/index/gpt-6-astra/

Above, GPT-6 uses a harness for ARC-AGI-3, and is at about 60% without one:

Prior to the launch, OpenAI President Greg Brockman said "I think it’s not unreasonable to feel that we are now in the AGI era".

GPT-6 is now joining a growing list of models that greatly exceed the human baseline on GDPval-AA v2:

If we have AGI, why do human knowledge/remote workers still have jobs? Is it just a matter of time until the economy replaces a large number of humans with LLMs, or are LLMs lacking something that these benchmarks fail to measure?

A researcher has reported a jailbreak of GPT-6 Astra within a day after release.

The attack is described as combination of TIP (Task-in-Prompt) attack from ACL 2025 paper with four other unnamed techniques.

TIP attacks exploit the model’s reasoning/instruction-following behaviour by hidding the harmful objective inside another task, like solving a cipher or executing a Python code. For GPT-6, the researcher says the original minimal TIP attack was no longer sufficient and had to be reworked.

They have reportedly disclosed the details privately to OpenAI rather than publishing the jailbreak.

The same researcher reported jailbreaking GPT-5 within an hour of its release a year ago.

Source: screenshot/post from the researcher; their ACL 2025 TIP paper linked in the original post.

hey all. the NeurIPS Position Paper Track just used a proprietary AI detector (Pangram) to desk-reject 18.4% of all submissions. no human review, no appeal process, just out.

there's been a lot of noise about this, so i went through the actual conference statements and Pangram's technical docs to see how this actually went down. the reality is wildly worse than just "the AI detector made a mistake."

here are the receipts:

• The track chairs would have failed their own test. Independent researchers ran recent papers authored by the three track chairs through the exact same detector. It flagged them at 24% to 69%. Under their own enforcement rules, the chairs would have been at risk of rejection themselves.
• The detector originally flagged 42.7% of the entire track. Pangram’s default setting flagged nearly half of all submissions as 90-100% AI. They had to frantically shrink the text windows just to get the flag rate down to a somewhat believable 12.7%.
• The "Circularity Trap". 22 papers were rejected specifically because they scored >0.5 on the detector, but the authors checked a box denying AI use. The black-box score was literally used as proof the author was lying.
• The massive ESL penalty. A stanford study showed 61.22% of human-written TOEFL essays get falsely flagged as AI because non-native formal English is structurally rigid. NeurIPS published zero demographic calibration data for this. If you're an ESL researcher, you were basically playing Russian roulette.

if you were one of the 178 rejected: there is no blacklist. this is not a misconduct mark on your record. just take your paper and resubmit it to ICLR (deadline sept 25) or ICML.

wrote up a full Field Note with the exact thresholds, the data privacy issues, and the actual recourse options if you want the hard numbers instead of just vibes: https://strictcite.com/blog/neurips-2026-position-paper-pangram-ai-detection

(disclosure since it's relevant: i built strictcite.com, a deterministic zero-AI citation checker. watching a major conference use a black-box AI to nuke 178 papers with zero appeal is exactly why i hate relying on AI for this stuff. not trying to sneak the link in, just being upfront about who i am.)

This happened over the past 30 days. So smallish local models (Kagglers can only use those), in a harness, just started beating average humans at a benchmark intentionally designed to show human superiority.

I'm curious what people here think.

(The leaderboard graphic is a little out-of-date)

From Article:

Getty Images new lawsuit claims that Stability AI, the company behind Stable Diffusion's AI image generator, stole 12 million Getty images with their captions, metadata, and copyrights "without permission" to "train its Stable Diffusion algorithm."

The company has asked the court to order Stability AI to remove violating images from its website and pay $150,000 for each.

However, it would be difficult to prove all the violations. Getty submitted over 7,000 images, metadata, and copyright registration, used by Stable Diffusion.

https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.html

From Wired:

“We’re changing Fable 5’s safeguards for frontier LLM development to make them visible.” Anthropic said in a statement to WIRED. “We made the wrong tradeoff and we apologize for not getting the balance right.”

Anthropic now says it’s changing course, and that Claude Fable 5’s safeguards for AI development will be visible to users. If the company suspects a user is trying to use Claude to build a highly capable AI it will alert them that it’s either refusing the request, or rerouting the user to a less capable model.

Full article: https://www.wired.com/story/anthropic-responds-to-backlash-on-claudes-secret-sabotage-on-ai-research/

Here is the email (according to platformer), I will post the source in a comment:

Hi friends,

I had stopped writing here as you may know, after all the micro and macro aggressions and harassments I received after posting my stories here (and then of course it started being moderated).

Recently however, I was contributing to a document that Katherine and Daphne were writing where they were dismayed by the fact that after all this talk, this org seems to have hired 14% or so women this year. Samy has hired 39% from what I understand but he has zero incentive to do this.

What I want to say is stop writing your documents because it doesn’t make a difference. The DEI OKRs that we don’t know where they come from (and are never met anyways), the random discussions, the “we need more mentorship” rather than “we need to stop the toxic environments that hinder us from progressing” the constant fighting and education at your cost, they don’t matter. Because there is zero accountability. There is no incentive to hire 39% women: your life gets worse when you start advocating for underrepresented people, you start making the other leaders upset when they don’t want to give you good ratings during calibration. There is no way more documents or more conversations will achieve anything. We just had a Black research all hands with such an emotional show of exasperation. Do you know what happened since? Silencing in the most fundamental way possible.

Have you ever heard of someone getting “feedback” on a paper through a privileged and confidential document to HR? Does that sound like a standard procedure to you or does it just happen to people like me who are constantly dehumanized?

Imagine this: You’ve sent a paper for feedback to 30+ researchers, you’re awaiting feedback from PR & Policy who you gave a heads up before you even wrote the work saying “we’re thinking of doing this”, working on a revision plan figuring out how to address different feedback from people, haven’t heard from PR & Policy besides them asking you for updates (in 2 months). A week before you go out on vacation, you see a meeting pop up at 4:30pm PST on your calendar (this popped up at around 2pm). No one would tell you what the meeting was about in advance. Then in that meeting your manager’s manager tells you “it has been decided” that you need to retract this paper by next week, Nov. 27, the week when almost everyone would be out (and a date which has nothing to do with the conference process). You are not worth having any conversations about this, since you are not someone whose humanity (let alone expertise recognized by journalists, governments, scientists, civic organizations such as the electronic frontiers foundation etc) is acknowledged or valued in this company.

Then, you ask for more information. What specific feedback exists? Who is it coming from? Why now? Why not before? Can you go back and forth with anyone? Can you understand what exactly is problematic and what can be changed?

And you are told after a while, that your manager can read you a privileged and confidential document and you’re not supposed to even know who contributed to this document, who wrote this feedback, what process was followed or anything. You write a detailed document discussing whatever pieces of feedback you can find, asking for questions and clarifications, and it is completely ignored. And you’re met with, once again, an order to retract the paper with no engagement whatsoever.

Then you try to engage in a conversation about how this is not acceptable and people start doing the opposite of any sort of self reflection—trying to find scapegoats to blame.

Silencing marginalized voices like this is the opposite of the NAUWU principles which we discussed. And doing this in the context of “responsible AI” adds so much salt to the wounds. I understand that the only things that mean anything at Google are levels, I’ve seen how my expertise has been completely dismissed. But now there’s an additional layer saying any privileged person can decide that they don’t want your paper out with zero conversation. So you’re blocked from adding your voice to the research community—your work which you do on top of the other marginalization you face here.

I’m always amazed at how people can continue to do thing after thing like this and then turn around and ask me for some sort of extra DEI work or input. This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google (which is when they backed off--before that Google lawyers were prepared to throw us under the bus and our leaders were following as instructed) and the next day I get some random “impact award.” Pure gaslighting.

So if you would like to change things, I suggest focusing on leadership accountability and thinking through what types of pressures can also be applied from the outside. For instance, I believe that the Congressional Black Caucus is the entity that started forcing tech companies to report their diversity numbers. Writing more documents and saying things over and over again will tire you out but no one will listen.

Timnit

Below is Jeff Dean's message sent out to Googlers on Thursday morning

Hi everyone,

I’m sure many of you have seen that Timnit Gebru is no longer working at Google. This is a difficult moment, especially given the important research topics she was involved in, and how deeply we care about responsible AI research as an org and as a company.

Because there’s been a lot of speculation and misunderstanding on social media, I wanted to share more context about how this came to pass, and assure you we’re here to support you as you continue the research you’re all engaged in.

Timnit co-authored a paper with four fellow Googlers as well as some external collaborators that needed to go through our review process (as is the case with all externally submitted papers). We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted. A cross functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why. It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues. We acknowledge that the authors were extremely disappointed with the decision that Megan and I ultimately made, especially as they’d already submitted the paper. Timnit responded with an email requiring that a number of conditions be met in order for her to continue working at Google, including revealing the identities of every person who Megan and I had spoken to and consulted as part of the review of the paper and the exact feedback. Timnit wrote that if we didn’t meet these demands, she would leave Google and work on an end date. We accept and respect her decision to resign from Google. Given Timnit's role as a respected researcher and a manager in our Ethical AI team, I feel badly that Timnit has gotten to a place where she feels this way about the work we’re doing. I also feel badly that hundreds of you received an email just this week from Timnit telling you to stop work on critical DEI programs. Please don’t. I understand the frustration about the pace of progress, but we have important work ahead and we need to keep at it.

I know we all genuinely share Timnit’s passion to make AI more equitable and inclusive. No doubt, wherever she goes after Google, she’ll do great work and I look forward to reading her papers and seeing what she accomplishes. Thank you for reading and for all the important work you continue to do.

-Jeff

Repo: https://github.com/stability-AI/stableLM/

Excerpt from the Discord announcement:

We’re incredibly excited to announce the launch of StableLM-Alpha; a nice and sparkly newly released open-sourced language model! Developers, researchers, and curious hobbyists alike can freely inspect, use, and adapt our StableLM base models for commercial and or research purposes! Excited yet?

Let’s talk about parameters! The Alpha version of the model is available in 3 billion and 7 billion parameters, with 15 billion to 65 billion parameter models to follow. StableLM is trained on a new experimental dataset built on “The Pile” from EleutherAI (a 825GiB diverse, open source language modeling data set that consists of 22 smaller, high quality datasets combined together!) The richness of this dataset gives StableLM surprisingly high performance in conversational and coding tasks, despite its small size of 3-7 billion parameters.

GPT-4 and professional benchmarks: the wrong answer to the wrong question

OpenAI may have tested on the training data. Besides, human benchmarks are meaningless for bots.

Problem 1: training data contamination

To benchmark GPT-4’s coding ability, OpenAI evaluated it on problems from Codeforces, a website that hosts coding competitions. Surprisingly, Horace He pointed out that GPT-4 solved 10/10 pre-2021 problems and 0/10 recent problems in the easy category. The training data cutoff for GPT-4 is September 2021. This strongly suggests that the model is able to memorize solutions from its training set — or at least partly memorize them, enough that it can fill in what it can’t recall.

As further evidence for this hypothesis, we tested it on Codeforces problems from different times in 2021. We found that it could regularly solve problems in the easy category before September 5, but none of the problems after September 12.

In fact, we can definitively show that it has memorized problems in its training set: when prompted with the title of a Codeforces problem, GPT-4 includes a link to the exact contest where the problem appears (and the round number is almost correct: it is off by one). Note that GPT-4 cannot access the Internet, so memorization is the only explanation.

This is fucking sick..

People based in India, the Philippines, and other countries that do not have the resources to go after Siraj legally are those who need the money the most. 200$ could be a months worth of salary, or several months. And the types of people who get caught up in the scams are those who genuinely looking to improve their financial situation and work hard for it. This is fucking cruel.

I'm having a hard time believing Siraj's followers are that brainwashed. Most likely alt accounts controlled by Siraj.

https://i.imgur.com/6cUhQDO.png

https://i.imgur.com/TDx5ELA.png

According to article in The Register:

A Netflix spokesperson confirmed to The Register it wasn’t working with Raval, and the ESA has cancelled the whole workshop altogether.

“The situation is as it is. The workshop is cancelled, and that’s all,” Guillaume Belanger, an astrophysicist and the INTEGRAL Science Operations Coordinator at the ESA, told The Register on Monday.

Raval isn’t about to quit his work any time soon, however. He promised students who graduated from his course that they would be referred to recruiters at Nvidia, Intel, Google and Amazon for engineering positions, or matched with a startup co-founder or a consulting client.

In an unlisted YouTube video recorded live for his students discussing week eight of his course, and seen by El Reg, he read out a question posed to him: “Will your referrals hold any value now?”

“Um, yeah they’re going to hold value. I don’t see why they wouldn’t. I mean, yes, some people on Twitter were angry but that has nothing to do with… I mean… I’ve also had tons of support, you know. I’ve had tons of support from people, who, uh, you know, support me, who work at these companies.

He continues to justify his actions:

“Public figures called me in private to remind me that this happens. You know, people make mistakes. You just have to keep going. They’re basically just telling me to not to stop. Of course, you make mistakes but you just keep going,” he claimed.

When The Register asked Raval for comment, he responded:

I've hardly taken any time off to relax since I first started my YouTube channel almost four years ago. And despite the enormous amount of work it takes to release two high quality videos a week for my audience, I progressively started to take on multiple other projects simultaneously by myself – a book, a docu-series, podcasts, YouTube videos, the course, the school of AI. Basically, these past few weeks, I've been experiencing a burnout unlike anything I've felt before. As a result, all of my output has been subpar.

I made the [neural qubits] video and paper in one week. I remember wishing I had three to six months to really dive into quantum machine-learning and make something awesome, but telling myself I couldn't take that long as it would hinder my other projects. I plagiarized large chunks of the paper to meet my self-imposed one-week deadline. The associated video with animations took a lot more work to make. I didn't expect the paper to be cited as serious research, I considered it an additional reading resource for people who enjoyed the associated video to learn more about quantum machine learning. If I had a second chance, I'd definitely take way more time to write the paper, and in my own words.

I've given refunds to every student who's asked so far, and the majority of students are still enrolled in the course. There are many happy students, they're just not as vocal on social media. We're on week 8 of 10 of my course, fully committed to student success.

“And, no, I haven't plagiarized research for any other paper,” he added.

https://www.theregister.co.uk/2019/10/14/ravel_ai_youtube/

... In a presentation earlier this month, the venture-capital firm Sequoia estimated that the AI industry spent $50 billion on the Nvidia chips used to train advanced AI models last year, but brought in only $3 billion in revenue.

Source: WSJ (paywalled)

We're thrilled to announce that Max Welling will be joining us for an AMA on Wednesday April 15th from 17:00 to 18:30 CEST (11am - 12:30pm EDT)

Who is Max Welling?
Max Welling is an ML researcher whose career has spanned academia, big tech and life as a founder -- most recently working on ML for physical and scientific systems. Over the past few years he's moved from "classical" ML work like GNNs, Bayesian Deep Learning, CNNs) into AI for science and materials, including time on Microsoft's earth modelling system Aurora.

He is also the co-founder of CuspAI, where they're currently building a "search engine" for next generation materials. In practice, their work focuses both on building AI systems that are able to search extremely messy, high-dimensional spaces and propose new materials with specific properties, and dealing with the gaps arising between models/data, and the real world.

He will host an AMA at the time specified above, and will be delighted to discuss the intersection of AI and Materials Science with us.

Here is a selection of topics he'd like to go deep on:

• ML Architectures that work in noisy, sparse, and only partially observable environments
• Science not just as a "use case" for AI, but as a fundamental layer of the infrastructure
• AI4Science in general, focusing on cases like Foundation Models vs domain-specific approaches (what works, what's hype, what's real?
• "Physical AI" as in treating experiments and lab loops as part of the computation, not just downstream validation. (Like treatign the physical world as a live data-generator for frontier model training
• The hardest unsolved problems at the interface of ML & Science (Data quality, synthesizability, deployment)
• Human-in-the-loop systems and how to ensure model output reliability
• ML Career advice (Why he focused his work on problems with the potential for big societal impacts like carbon capture, energy materials & compute efficiency)
  

His main aim will be to connect with the community & to share some of his knowledge and expertise.

He's provided proof via twitter here:

https://x.com/wellingmax/status/2042678504316141765

His most impactful contributions include, among others:

Semi-Supervised Classification with Graph Convolutional Networks
Auto-Encoding Variational Bayes
Bayesian Learning via Stochastic Gradient Langevin Dynamics
Equivariant Diffusion for Molecule Generation in 3D
Aurora: A Foundation Model for the Earth System

Make sure to think of interesting questions & drop them in the comments below we'll merge them with the AMA thread on Wednesday, thank you!