Mathew Tizard

We won a Webby Award for our Changdeok ARirang project! A collaboration between Google PI, Nexus Studios and SKT. nexusstudios.com/insight/we-won… #webbys @thewebbyawards @nexusstories @sktelecom @GoogleARCore @BuiltwithARCore #arcore @GoogleARVR @joshto

"The model didn’t 'invent' any 'new mathematics', [but] merely being able to know deeply all the results in a scientific field, and being able to use all known arguments expertly and with just the right choice of parameters, that alone can lead to a ton of breakthroughs, and this is not just limited to mathematics, this type of (extremely) solid expert execution is the bread and butter of many many scientific advances."

RIP Michael Keating. He was brilliant as Vila.

Kokichi Sugihara’s mind-bending optical illusions. 🧐

Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946. For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids. An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better. This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.

The Demis Hassabis HUGE* Conversation (in full) 00:00 What is the hardest problem AI has already solved? 12:30 What is the cutting edge of drug discovery with AI? 21:53 Why did Demis say he “would have left AI in the lab longer”? 43:09 How should militaries use AI? 50:13 What can humans do that AI won't? 58:17 What does Demis Hassabis want his legacy to be? (And 1:04:40 Can I beat Demis at Jenga?) Recorded March 5, 2026 in London.

Beauty and Mystery of Mathematics. A Blaschke-Quotient Flow on the Poincaré Disk. "Roots and Poles of a Complex Rational Field." By @mathelirium, instagram.com/p/DP2einzDIq0/, Used with permission. [math, maths]

Brain cells making friends with each other on a grid:

The results of the research happening in my team @GoogleDeepMind have convinced me that the next era of scientific discovery will be aided by AI agents acting as force multipliers for human ingenuity. That’s why I’m proud to introduce Gemini for Science - a collection of experimental science tools designed to support researchers at every stage of the research process. The tools include: 1️⃣ Literature Insights, built with Google NotebookLM, searches millions of scientific papers to synthesize findings and generate artifacts including data tables, slides, reports, and more. 2️⃣ Hypothesis Generation, built with Co-Scientist, simulates the scientific method via a multi-agent "idea tournament" to generate, debate, and rigorously evaluate research hypotheses. 3️⃣Computational Discovery, built with AlphaEvolve and ERA, is an agentic engine that generates and scores thousands of code variations in parallel, allowing researchers to test modeling approaches in fields like epidemiology in a fraction of the usual time. Read more: blog.google/innovation-and… Register for access here: labs.google/science

@HazelAppleyard No biggie. There's probably a setting to flip the image

Wait for it… Whatever you think is going to happen… it’s worse 😂

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

This isn't AI, it's the Huajiang Canyon Bridge in Guizhou, China, the world's highest bridge at 625 meters above the river beneath it. It features a man-made waterfall created by diverting karst spring water discovered during tunnel construction.

Hue repel v2. It’s interesting how different sets hue make different pattern. #creativecoding

Ai has never been bigger.

The Aizawa Attractor. A simple equation, but beautiful chaotic pattern. 🎥: abakcus

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."

Watching Galinstan react in a copper ion acid solution felt strangely philosophical. I couldn’t believe how the liquid metal suddenly spread and moved almost like it was alive, as if matter itself only needs the right conditions to reveal hidden behavior.

All GPUs in Europe seem to be busy, as I wanted to spin up an Nvidia L40S to train my next splat! 🫠 So in the meantime have some juicy details of a strawberry. #3dgs

nanomachine precursors

It's still baffling to me that you can make a length 2 zig-zag look arbitrarily close to a length sqrt(2) line without changing its own length at all I know we can say "arc length isn't continuous wrt pointwise convergence", but that only renames the fact, it doesn't debaffle it