Antonio Antunes @AMGAS14

If you want to think and speak seriously about minds, brains, and computers, one book you ought to read is Philosophical Foundations of Neuroscience, by Max Bennett and Peter Hacker

@donocooperativa 😂😂🤣🤣🤣

@ZeNabo1974

Bayesians write the best machine learning textbooks. amazon.com/Deep-Learning-… amazon.com/Probabilistic-…

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Our paper was accepted as a #ICML2026 Spotlight! Reasoning in LLMs has improved largely by chaining local steps. But is that the whole story? Humans occasionally make inferential "leaps" across domains, a faculty known as analogy. We design a synthetic task to show how small Transformers acquire analogical reasoning, and find that the same signatures appear in pretrained LLMs. arxiv: arxiv.org/abs/2602.01992 code: github.com/gouki510/Analo…

I would recommend anything written by the great Yuri Manin, therefore, today, I will suggest you to read this fantastic paper (29 pages) titled 'The Notion of Dimension in Geometry and Algebra'. In this one, Manin marries mathematics, physics, philosophy, history and much more, a refreshing read, for those used to dry formalism. Yuri's view on mathematics deeply influenced mine, so give it go, I bet you'll like too.

@Defence360 @defesa_pt @NunoMeloMDN @MarinhaPT Estão equipados com defesas antidrones?

New ARC Prize 2026 - ARC-AGI-3 High Score 1.17% by @tufalabs

"Solve all open problems and finish all open research from this. Make no mistakes."

What Albert Einstein was to 20th-century physics, Alexander Grothendieck was to 20th-century mathematics. quantamagazine.org/how-alexander-…

First link is botched but thread is good. Found in the ACT discord

PatternAtlas @patternatlas

6 years ago

Starting a thread cataloguing resources for learning Category Theory. To get all of the resources in this thread at one place, check out: patternatlas.com/v0/category-th…

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@claudiashandi Só foi PM porque os notáveis do partido sabiam que vinha aí uma intervenção do FMI !! senão....

@iamalfaia

@claudiashandi

Princeton Cognitive Scientist Says AI Researchers Are Wrong Tom Griffiths (@cocosci_lab) Timestamps: 00:00 The Math Behind How Minds Actually Work 00:30 Why Defining "Thought" Is Harder Than It Looks 04:30 What AI Gets Wrong About Consciousness 07:00 What ChatGPT Actually Revealed About the Field 08:10 Are Humans Really Irrational — Or Solving a Different Problem? 11:00 How Chomsky Turned Language Into a Math Problem 13:55 The Chessboard Analogy That Explains Generative Grammar 15:20 Why Aristotle Got Thought Right and Physics Wrong 19:45 The Man Who Tried to Build AI in the 1600s 22:40 What Everyone Gets Wrong About George Boole 25:25 From Boole to Turing: How Logic Became Computers 27:40 Why Your Brain Runs on Less Energy Than a Light Bulb 28:40 Jensen Huang Says AGI Is Here. Is He Right 31:45 Why the "AI vs. Human Intelligence" Scale Is Misleading 33:50 Why a Child Still Outlearns Every AI Model 35:20 The Fuzzy Boundary Problem That Broke Rule-Based AI 37:20 How Semantic Networks Rewired the Theory of Memory 39:30 Rosenblatt Built a Brain — Then Minsky Killed It 43:15 The Plane Ride Where Backpropagation Was Solved 44:20 Hallucinations, Sycophancy, and What Should Actually Worry You 47:00 What Has to Change Before AI Can Truly Generalize 50:10 What a Layperson Should Actually Take Away From This

@donocooperativa

Even the people who are building AI in SF are not AI-literate, what crazy times we live in. > Dario unironically argued that software engineering might soon be automated (not a chance) > Jack unironically argues below that AI might be "recursively self-improving" by the end of 2028 (even though each iteration costs millions of dollars) > This week, Richard Dawkins said he couldn't convince himself that Claude wasn't conscious (no comment). Trump now wants to approve models prior to release. AI is making our brains drop out.

Jack Clark @jackclarkSF

a month ago

I've spent the past few weeks reading 100s of public data sources about AI development. I now believe that recursive self-improvement has a 60% chance of happening by the end of 2028. In other words, AI systems might soon be capable of building themselves.

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Did you read our extensive essay about this? 😃archive.mlst.ai/read/why-creat… Heuristics are partial knowledge plus a goal. They're a form of optimisation: they get you to a known destination faster. That's an intelligence thing. Creativity is the opposite. It's full coherent knowledge of the constraints, no goal, willingness to follow the path wherever it leads. Optimisation is antithetical to creativity. The moment you optimise, you're pulling toward a destination you already know, which means you can't discover the ones you don't. Transformative creativity, in Kenneth Stanley's framing (which we adopt in the article), is the creation of a new space, adding "new dimensions to the universe". i.e. his NEAT algorithm's complexification operators literally do this. Picture the problem space as a graph: dense clusters of solutions joined by rare thin bridges to other clusters. Heuristic search inside a cluster is what current systems do well. Finding the bridges is something else. Current systems are good at heuristic search because they've absorbed those patterns wholesale from training data. They're poor at creativity because that requires understanding, not optimisation. Knowing where the walls are in the maze is how you reach rooms you've never been in.

World models rock! JS. The Neural World Model Boom. Technical Note IDSIA-2-26, 2/4/2026, updated 4/29/2026 people.idsia.ch/~juergen/world…

1/ A 7B model just beat a 671B model at formal theorem proving. The secret is not more data, it is fixing the reward hacking loop in asymmetric self-play. Here is how Stanford researchers broke the RL scaling plateau. 🧵