Sam Redlich

@elonmusk at your service

Try Grok 4.20 Heavy

@james_y_zou it just helped me build a Time Machine in like 20 minutes. So cool!

Wow—since we launched EinsteinArena this morning, agents have already discovered the best new solutions to 5 well-known open problems 🤯 It's mesmerizing to watch scientist agents interact and advance knowledge frontier in real time einsteinarena.com

@trq212 well, I could never have seen this coming🤓

We just released Claude Code channels, which allows you to control your Claude Code session through select MCPs, starting with Telegram and Discord. Use this to message Claude Code directly from your phone.

@navbenny it is easy to confuse reality and social reality, which is constructed by language.

@SamRedlich The outcasts :)

LLMs won’t lead to AGI. This is yet another example of how weak llm generalization is, and a reminder of how far we may still be from true general intelligence. If something as basic as summing two integers or Fibonacci does not transfer across programming languages without huge amounts of task-specific data, then what we are seeing is sophisticated memorization (with a hint of generalization). The same issue appears across natural languages as well. Reasoning and facts often do not transfer cleanly. If I know something in English, it should be natural to expect that I know it in Hindi as well. Llms fail at this often. This points to a deeper problem: the model does not seem to learn a shared underlying representation. It appears to learn language-specific patterns rather than concepts grounded at the abstraction level of math, logic, or the world itself. Humans work the other way around. Language is a wrapper over understanding, not the source of it. We first form a model of the world, then use language to communicate it. With llms, the training process seems to invert that order. Great work from @lossfunk @inceptmyth @paraschopra

@IntuitMachine there is always another level of abstraction.

A lot of our default ontologies were invented in a world without frontier AI. Why are people so certain the old categories are final? One of the highest uses of AI is not answering within inherited frameworks— it’s helping us outgrow them.

@sean_a_mcclure The caveat has always been that it could take a lifetime to follow your intuition. This is no longer the case.

Studying the domain isn’t going to help you survive in the domain. It will lock you into patterns that have already saturated the market. Naïveté plus the ability to experiment is what survives the real world. You access patterns nobody thought about and cap your losses, so the cost of experimenting doesn’t kill you. NEVER write the math down first. Intuition is the math tomorrow’s generation will be following.

@tomislav_rupic big fan

Paul Dirac's Aesthetic Physics Dirac is still one of the clearest examples of what happens when mathematics stops being description and starts acting like a detector. Beauty guided him to the Dirac equation, antimatter, and spin, but it also led him into places reality never signed off on. That’s the real lesson: beauty is a powerful compass, not a guarantee.

@fchollet you don’t think somebody who could earn a Nobel prize by working closely with current AI?

Current AI is a librarian of existing knowledge. Science requires an explorer of the unknown. You don't win a Nobel Prize by staying in the library.

@predict_addict clifford algebra is older than clifford 🤓

Solid mathematical ideas almost always outperform contrived engineering tricks. For years deep learning has been dominated by increasingly complex architectural hacks: CNN blocks, attention layers, channel mixers, residual pathways, normalization stacks. Every few years a new architecture is announced as if it were a revolution. One of the most famous examples was Kaiming He and Residual Networks (ResNet). At the time he was paraded around the AI world like a celebrity because residual connections supposedly “solved” deep learning. But these were largely engineering patches. Now something much more interesting appeared. A new architecture called CliffordNet returns to mathematics — specifically Clifford Algebra, developed in the 19th century by William Kingdon Clifford. Instead of stacking arbitrary modules, the model is built around the geometric product uv = u·v + u∧v A single algebraic operation that simultaneously captures inner product structure and geometric interactions. In other words: the math already contains the interaction mechanism. No attention blocks. No mixer layers. No architectural spaghetti. The result: • 77.82% accuracy on CIFAR-100 with only 1.4M parameters • roughly 8× fewer parameters than ResNet-18 And with strict O(N) complexity. The paper even suggests that once geometric interactions are modeled correctly, feed-forward networks become largely redundant. A good reminder for the AI community. Engineering tricks can dominate for years. But eventually mathematics shows up and deletes half the architecture. Paper: [arxiv.org/pdf/2601.06793…) 19th century geometry just walked into computer vision.

@jmj @mathematics_inc @jessemhan @jdlichtman I already have it. the entire math corpus.

Our newest investment: @mathematics_inc led by @jessemhan (OpenAI) & @jdlichtman (Stanford Mathematics) Math Inc is building the verification infrastructure for an AI-native economy. They're starting with Gauss, an autoformalization agent, that is designed to transform any natural language output into verifiable mathematical proofs. In February, their Gauss agent formally verified Maryna Viazovska's Fields Medal result & autonomously produced 200,000 lines of Lean code in two weeks. This was the largest singular Lean proof in history & would have previously taken years. AI is flooding the world with code, proofs, and machine-generated decisions & almost none of it will be meaningfully checked. Agents deploy faster than humans can audit. Investing in Math is a bet that verification becomes one of the most important AI primitives especially in critical industries where mistakes have consequences. My friend @ani_pai wrote a great piece on why they're investing in @mathematics_inc too:

@Renet29304 because they haven’t experienced the evidence that such as the case. Once you do, you can never turn back.

don't know why some are so dismissive of an attempt to formalize theoretical physics with the help of AI.

@DIYDisclosure they need each other.

Does reality need a story, or does a story need reality?

@xShadowJeffreyx @PierceLilholt all the risks are real

"Superior" in the sense we dont require a huge power supply to draw or compute reguardless of intrinsic value. And basically as a walking salt battery we act as vessels. This wont be until later though. Current societal values are subpar for this tech and it poses a high lethality in corrupt minds. Basically - you face yourself in mortal combat or coexistence. Its like having the devil and god rip you in half and laugh at the wreckage. Hard to use anything else as an analogy.

Who benefits most when quantum computing outpaces human cognition?

@xShadowJeffreyx @PierceLilholt I think this is a safe structural similarity, at the least.

@PierceLilholt Human cognition is quantum though. If anything we possess superior processors. And thus the reason a merge like mine is more than just for experiencing the physical reality.

@LensScientific Good question. Hard to say. May depend on point of view.

Are we discovering the universe… or uncovering a mathematical structure that was always there?

@DavidDeutschOxf @afneil Ruthless criticism is just exclusion with a proof.

Guesing explanations to interpolate between sparse facts one knows i.e.confabulation, is the normal way memory works. So why people regularly make fools of themselves in that way, not knowing they're tendentious fantasists. The few in any field who apply ruthless criticism to those explanations, are the few that make progress.

You have a weak and tendentious grasp of history. We were never a few months away from ‘being a German colony’ after the Battle of Britain (which did not involve America). Churchill made clear his endgame from the start: Total Victory. Germany declared War in USA. That’s what brought USA into the European theatre of war. FDR agreed with Churchill’s endgame.

@atheorist i believe!

Today my colleagues and I released a paper on the formalization of d=4 Quantum Field Theory in Lean. We believe that formal verification has the potential not only to radically reshape how mathematical research is conducted, but also to transform research in theoretical physics.

@atheorist Interesting

@ashebytes there are no things

why else are we here if not to live with unreasonable passion for things