Co-founder and CEO @lanyon_ai, making the universe computable. Part-time math/physics research @Princeton Previously @Cambridge_Uni @WolframResearch
Princeton, NJ
Joined November 2012
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Jonathan Gorard retweeted
In January, Jonathan Gorard (@getjonwithit) was confident AI couldn’t produce a machine-checked Lean proof of Maryna Viazovska’s Fields Medal sphere-packing result before late 2027. By March, one existed. He says the real story turned out to be complicated.
Full episode: youtu.be/oQypVVv1u1o
Where computational irreducibility makes particle kinetics infeasible, it gifts us fluid mechanics to compensate.
Where it makes neurophysiology infeasible, it gifts us psychology to compensate.
Mechanistic interpretability is infeasible. What have we been gifted to compensate?
Some of y’all have not studied the implications of computational irreducibility, and it shows…
Local determinism wrt nontrivially interacting rule sets does not help predict the “long term” behavior of a computationally irreducible system, and when it comes to AI the iterative frequency is such that “long term” is not actually that long from the human reference frame.
This is as true of our current neural nets as it is of the initial cellular automata in which computational irreducibility was first discovered and studied.
It’s also what Anthropic was (re)discovering wrt their escape cone diagrams wrt agentic unpredictability over long-horizon work—especially work with somewhat ambiguous constraints and success metrics.
Except that they got the cone geometry wrong, as the region of an agent’s potential state space trajectory looks more like an exponential horn.
Jonathan Gorard retweeted
A must-watch interview by @TOEwithCurt with @getjonwithit on the state of AI, math, and science. youtube.com/watch?v=oQypVVv1…
A beautiful post from our Chief Scientist @JunoRavin about the physics of nuclear fusion.
Using @lanyon_ai, we can autonomously explore the complex interplay between fusion reactor design, plasma physics timescales, and fundamental MHD instabilities (such as interchange/flute instabilities in mirror configurations, and sausage modes in Z-pinch configurations) that must be mitigated for stable fusion.
Lanyon's neurosymbolic architecture is ideally suited for automating such analyses, allowing one to combine formally verified numerical simulations for 3D MHD (using Lanyon's symbolic layer) with highly flexible AI-driven physics analysis (using Lanyon's neural layer).
The era of formally verified autonomous fusion science is upon us!
Why is fusion hard? In the first in our series of “physics deep dives”, Chief Scientist @JunoRavin shows how Lanyon can be used to autonomously rediscover and analyze some of the fundamental physics difficulties underlying magnetic confinement fusion: the sausage instability that killed the Z-pinch device, and the interchange/flute instabilities that continue to plague magnetic mirror devices.
This deep dive shows the true power of Lanyon’s neurosymbolic architecture for applied physics research, combining formally verified 3D MHD simulations produced by Lanyon’s symbolic layer, with highly flexible AI-driven physics analyses enabled by Lanyon’s neural layer.
Stay tuned for further additions to this series, where we will dive deeper into field-reversed configurations, tokamaks, stellarators, and all of the new physics difficulties that emerge at these new ranges of timescales. Post below 👇
Jonathan Gorard retweeted
Why is fusion hard? In the first in our series of “physics deep dives”, Chief Scientist @JunoRavin shows how Lanyon can be used to autonomously rediscover and analyze some of the fundamental physics difficulties underlying magnetic confinement fusion: the sausage instability that killed the Z-pinch device, and the interchange/flute instabilities that continue to plague magnetic mirror devices.
This deep dive shows the true power of Lanyon’s neurosymbolic architecture for applied physics research, combining formally verified 3D MHD simulations produced by Lanyon’s symbolic layer, with highly flexible AI-driven physics analyses enabled by Lanyon’s neural layer.
Stay tuned for further additions to this series, where we will dive deeper into field-reversed configurations, tokamaks, stellarators, and all of the new physics difficulties that emerge at these new ranges of timescales. Post below 👇
I don't think this is true: it will just select for a very particular kind of mathematician: the G. H. Hardy.
If you read C. P. Snow's foreword to the Apology, it becomes clear that Hardy's most substantive mathematical contributions came about from taking the highly creative and technically impressive (but otherwise utterly inexplicable) accomplishments of Littlewood and Ramanujan, and figuring out how to explain, interpret, and communicate them in a manner that was simultaneously rigorous, precise, and beautiful.
It's ironic given his derogatory comments about mathematical exposition in the Apology, but Hardy's real skill lay in the beauty and clarity of his prose and his explanation. He was always the one who wrote up the final manuscript for publication, but he was almost never the one who provided the underlying results or ideas. (Again, Snow's foreword makes clear that the Apology is, amongst other things, a work of immense self-hatred.)
In a world where Littlewoods and Ramanujans are commodified, be a Hardy.
The problem being identified here has nothing to do with mathematics, clinical trials, or human understanding.
The problem is that we have mechanisms for assigning (and enforcing) legal, social, and moral responsibility in humans, but not in AIs. This problem is solvable.
Last night was the first night I genuinely couldn't sleep because I was so excited to get back to reading an AI-generated proof (of the Grothendieck homotopy hypothesis).
Hard not to feel shellshocked, of course, but the feeling of "touching the infinite" is pretty palpable.
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
Always a great pleasure to chat with my friend @TOEwithCurt. Over the course of 2+ hours, we discuss the Hilbert-Gandy program, existential worries, the limitations of language and cognition, how to connect AI to the physical world, and why we left Princeton to build @lanyon_ai.
Math has a proof checker. Can physics get one? Jonathan Gorard @getjonwithit, an applied mathematician and the cofounder and CEO of Lanyon AI, joins to explain why AI took off in math and coding but not yet in physics. We discuss an AI finishing the formal check of a Fields Medal proof and why it scared him, why he thinks “I detected a particle” rests on 20 to 50 levels of theory, and what Alan Turing’s PhD student Robin Gandy tried to build for physics. The conversation also covers his push to make physics executable, why he thinks mysteries are artifacts of description languages, and whether there is still a place for the physicist.
youtu.be/oQypVVv1u1o
Jonathan Gorard retweeted
Math has a proof checker. Can physics get one? Jonathan Gorard @getjonwithit, an applied mathematician and the cofounder and CEO of Lanyon AI, joins to explain why AI took off in math and coding but not yet in physics. We discuss an AI finishing the formal check of a Fields Medal proof and why it scared him, why he thinks “I detected a particle” rests on 20 to 50 levels of theory, and what Alan Turing’s PhD student Robin Gandy tried to build for physics. The conversation also covers his push to make physics executable, why he thinks mysteries are artifacts of description languages, and whether there is still a place for the physicist.
youtu.be/oQypVVv1u1o
FFS. Just be explicit about whether you mean:
1: "Computer" in the sense of an abstract machine that computes general recursive functions;
2: "Computer" in the sense of a finite physical approximation to (1);
3: "Computer" in the sense of the von Neumann implementation of (2).
A lot of people should read this aeon.co/essays/your-brain-do…
Jonathan Gorard retweeted
Using Lanyon to autonomously discover and verify new models and algorithms for continuum mechanics.
Simulating high-energy impact of a copper projectile on an elastoplastic plate, plus the resulting damage profile. The discovered model matches the experimental value for the effective plastic strain to within 5%.
Model discovery, formal verification, software implementation, and numerical simulation all completed in less than 5 minutes, from a single prompt.
No. A concatenation of many matrix multiplications is… just a single matrix multiplication (that’s what linearity means).
Rather, a neural network is a concatenation of matrix multiplications *and* nonlinearities, i.e. a concatenation of arbitrary linear and nonlinear operations, i.e. an arbitrary operation.
So the relevant question is rather: can consciousness be instantiated by an arbitrary abstract operation?
I’d say it’s pretty clear which way round the burden of proof goes.
Jonathan Gorard retweeted
Math has a proof checker. Can physics get one? Physicist Jonathan Gorard @getjonwithit, cofounder of the Wolfram Physics Project and now CEO of Lanyon AI, thinks so: “We want to make physics executable in the same way that software is executable and that now increasingly mathematics is executable.” He also says, “I’m in a continual process of grief.” What does it mean to grieve a way of doing physics? And is there still a place for the physicist?
Very proud to be sponsoring this fantastic event, and we’ll be providing early access to the @lanyon_ai API and CLI (plus unlimited compute) to all participants.
Should be great fun - see you there!
We’re organizing Vibecheck, a formal methods hackathon with some of the best folks in the space - @maxvonhippel, @qd_forall, @nolanlwin, @jessemhan, @emiyazono, and @workersio.
You’ll get a weekend to build real production software, and formally verify it.
We’ll have people who really know their stuff around to help, and you can come with a team, find one there, or just hack on something yourself.
We are glad to have a phenomenal group of sponsors, including @harmonicmath, @mathematics_inc, @theoremlabs, @thegp, @omnicom, @primeintellect, @astriolabs, @buildwithparty, @wearerandomlabs, and @lanyon_ai, as well as some more we will announce in the coming days!
If you’re a hacker, a formal methods person, or just someone who thinks “how do we know it works?” is an interesting question, we’d love to have you.
Signup Now: fmxai.org/vibecheck/