Mudassir Farooq retweeted
If I did not believe in the intrinsic and underrated usefulness of mathematics, I would be somewhat sad about what AI is doing to it.
I think Jevons paradox will strike again: much cheaper and deeper mathematics will become even more of the fundamental infrastructure of the future than it is today.
Math will no longer be centered on conjectures posed by humans. It will be continually postulated, solved, processed, and utilized by AI systems as they work on the hard scientific, engineering, and computing problems of the future.
We’re essentially charbroiled
Mudassir Farooq retweeted
Terry Tao is probably the most measured, pro-AI mathematician on the planet - which makes this quote especially concerning to read. 😟
If sharing your hunches means getting scooped ~immediately without acknowledgement, then we're going to see not just math but all other science / engineering disciplines go dark.
Mudassir Farooq retweeted
If you are a lab researcher, I urge you to consider what the next few years will actually feel like. Do you want to kick off a superintelligent RL run without a rigorous understanding of its mind? Should you put your head down because “it’s happening anyway” - or take this moment to call for different conditions?
Mudassir Farooq retweeted
I left Google DeepMind in June. Jacob is right: many researchers believe they are building something that could kill everyone on the planet. It was literally my day job to think about how to stop that.
Replying to @hilbertspaess
The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.
Mudassir Farooq retweeted
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
Mudassir Farooq retweeted
go fuck yourself @sama
claiming that you solved navier stokes because 10000 agents ran in circles for 88hours on a multimillion dollar gpu cluster to formalize in lean a blowup case under controlled external forcing is pure scientific vulgarity the clay mathematics institute millennium prize does not ask m whether you can artificially force a singularity in a fluid by injecting an ad hoc smooth external forcing term f(x,t) to twist the vortex until it breaks the real problem questions the fundamental stability and global smooth existence for 3dimensional incompressible euler & navier stokes equations under natural conservation laws and viscous dissipation alone using a mathematical loophole on forced equations to parade a century old victory is a major conceptual scam Altman
technically & epistemologically what you present as an agi breakthrough is nothing more than bruteforce combinatorial autoformalization the ai did not understand fluid mechanics it simply navigated a continuous search space previously mapped out and constrained by the monumental work of human mathematicians like tristan buckmaster/ levent alpöge / diego córdoba or tarek elgindi coordinating 10000 agents to check the logical consistency of a 100 page proof via lean is a software engineering feat and computational parallelization triumph not an intrinsic scientific discovery it is the victory of the compute bulldozer over abstract human intuition repackaged for the public as a higher mathematical consciousness
to this theoretical imposture you add a disgusting ethical and industrial cynicism taking advantage of private codex sessions and informal preprints from academic researchers to siphon their research leads and then trying to redact or erase the contribution of levent alpöge under the pretext that he works at rival anthropic is intellectual serfdom openai behaves like a feudal lord of silicon appropriating the cognitive subsistence of independent scholars threatening their careers behind closed doors if they protest and turning community academic labor into a privatized pressrelease
this entire staged event serves a desperate financial agenda in a pre ipo panic facing the slowdown of scaling laws and growing investor skepticism over the profitability of foundational models openai needs to manufacture an artificial sputnik moment claiming to solve a millennium prize without immediately submitting the proof to traditional peer review means using the prestige of fundamental mathematics as cheap marketing fuel to inflate a delusional valuation!!!
real science is not a clout chase on social media or a compute spike spent to rob the clay mathematics institute it is a quest for elegance physical truth and universal rigor to decode reality true artificial intelligence will not emerge from hostile corporate takeover of academic work hidden behind computational bruteforce but from architectures capable of generating new conceptual paradigms by masquerading constrained formalization as the collapse of physics greatest mysteries you did not solve navier stokes you only proved how far silicon valley will go to prostitute scientific integrity for capitalist spectacle
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Mudassir Farooq retweeted
Thanks for posting this thought-provoking article. Readers should read it with an eye towards the three-level ladder of causal understanding -- parallels may pop-up and, when they do, we have the beginning of mathematizing intuition.
Google Deepmind argues that LLMs can never make real scientific discoveries.
They published a paper breaking down Albert Einstein’s private view of scientific discovery.
In a famous letter to his friend Maurice Solovine, Einstein drew a diagram of how science actually happens.
It is a cyclical loop.
First, you experience raw sensory data. Then, through a mysterious, non-logical act of intuition, you make an intuitive "jump" to abstract axioms. Finally, you use strict logical deduction to derive consequences from those axioms.
Generative AI has completely mastered two-thirds of this loop.
• Induction: Statistical pattern matching across billions of tokens.
• Deduction: Formal proof generation, like AlphaProof solving complex math Olympiads.
AI can crunch data and it can prove theorems.
But it cannot make the jump.
The paper argues that AI completely lacks Abduction, the generation of novel explanatory hypotheses when observational data is scarce.
The prevailing tech myth says that "creativity is just data compression." That if you feed an LLM enough text, scientific breakthroughs will naturally pop out.
Einstein’s formulation of General Relativity proves that is a delusion.
When Einstein formulated relativity, the observational data didn't demand a new physics framework; classical mechanics was still massively successful. The breakthrough required a conceptual rupture. An intuitive leap from physical reality to a brand-new set of foundational axioms.
An LLM can execute the math once the axioms are given. But it is structurally incapable of formulating those premises on its own.
It can interpolate inside existing human thought, but it cannot transcend it.
The translation of physical reality into formal axioms remains the absolute, hard bottleneck of artificial scientific invention.
We can build models with trillions of parameters. We can scale compute into the stratosphere.
We can make the calculator infinitely fast.
But until we solve grounding, the machine can process all the data in the universe.
It still can't make the jump.
This is what happens when the landscape rewards for good performance and not necessarily good learning 🤷♂️
Replying to @anpaure
thanks for bringing this up. it's always a bit annoying when someone reads a handful of papers and starts talking about "taste" as if they've developed deep research intuition and then immediately begins quoting feynman and hamming.
Question I keep returning to: Is it even wise to read Goodreads/Reddit reviews — or ask for an AI structural blueprint — before skimming a book’s preface, contents, and intro?
🧵 1/4
Does treating the text as an object to be mechanistically mapped before direct encounter conflict with good Curiosity Design? Or am I being puritanical? [ 3/4 ]
Mudassir Farooq retweeted
I disagree with this decision and I don't like it.
But also...
HOW DID ANTHROPIC NOT SEE THIS COMING‽
It is *the* obvious response to "this is too dangerous for anyone except us to use", since that relies on a premise ("we are uniquely good") that almost no-one agrees with.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: anthropic.com/news/fable-myt…
Mudassir Farooq retweeted
Dario (48 hours ago): “US gov should be able to block model deployment”
USG: *export controls models*
Dario: “not like that”
Mudassir Farooq retweeted
One interesting pattern with Fable 5 is that it will often say things that are gibberish when I use it for coding. Things like "The morning's slim-scan fix cured the scan hang", "this is a latent-drift API-shape wrinkle", etc.
When I ask why it does this, Fable explains that it invents codenames while reasoning about the problem, then fails to realize they're meaningless to me. Its neuralese is blending into its output because of a theory-of-mind failure about what's in its head vs. mine.
Mudassir Farooq retweeted
The core part of this Anthropic Fable release saga is that there are many overlapping issues at once. Some of which operate on different timelines of the AI arc, and some have easier fixes. In my critiques, I asked for specific changes to some things, understanding that some things don't have an easy fix.
The simplest issue was an uneven application of safety domains in a way that was misleading to users. This was an implementation issue that overlaps with a values-based decision of what their customers should be doing. Many people including myself pointed out how it was insane to list core safety areas and then have one of them launch with a different safety mechanism, one which actively mislead users. Doing this from the guise of safety was a major misstep and in my opinion Anthropic got very justifiably raked over the coals for it. Don't release the model if you can't hit your safety targets.
A subissue here is the idea of silent manipulation. This again is a horrible precedent, and quite odd for a company that has done extensive, leading technical AI safety research on ideas like CoT monitoring and other emergent misalignment issues. Silent manipulation of users is baking in a misalignment to the system at its face level. This comes with a permanent degradation in user trust, which begets a less safe environment for AI. Users who don't have clear information on how AI works will not develop safe working patterns with it.
The more complex issues are with how Anthropic handles broader scientific engagement with their models. The safety classifiers launched with these models obviously have accuracy issues to start. I have priced in that there will be more false positives to start, that's life. It's Anthropic's business to degrade their products at release time, or make the trade off of user satisfaction versus revenue. Still, it is a very real sign of concentration of power that businesses can make such obviously user-harmful behaviors and still lead in the market. This concentration of power is only starting to set in and we could see even weirder signs of it in the coming years.
It is now simple enough for me to test Claude Fable in my workflows and know if I'm restricted. This is obviously a suboptimal equilibrium – i want the best intelligence I can get, without restrictions – but it is easy enough for me to make sense of and work with.
The specific issue of restricting access to AI research in particular was a bubbling and hard to fix issue with Anthropic specifically, and the frontier labs generally. There is a common view that the frontier labs will be the mediators of all major scientific innovations in the future, as the places with the best models and the compute for inference to solve major problems. This is a categorical error in how science works, which is a community evolution of accepted ideas, and the the evaluation of your ideas by (hopefully numerous) independent, other practitioners. You cannot have science advance only within a monolith.
As an AI researcher I'm very sad to have the latest models restricted, but I would expect Anthropic to do this eventually. I lost more trust over the silent manipulation than I would with a restriction in access. Anthropic has made it pretty clear that they only trust themselves as the mediators of cutting-edge AI research.
If I had a say, Anthropic should've proactively made a program to make sure researchers get access in the broader AI community without the safeguards. Academics, nonprofit workers myself, etc. have no reason to not get access. The only valid argument here is that they want to control frontier AI, which is a know your customer part of serving these models.
This worldview of science has personally motivated me greatly over the last year, and increasingly so this week, to make the open science of AI continue to be viable. Olmo was a wonderful success here. Still, building research infrastructure is different from working for access to the tools needed to do the trade.


























