1/12 How do animals build an internal map of the world? In our new paper, we tracked thousands of neurons in mouse CA1 over days/weeks as they learned a VR navigation task. @nspruston @HHMIJanelia, w/ co-1st author @JohanWinn Video summary: youtube.com/watch?v=yw_4uVur… Paper: nature.com/articles/s41586-0…

We show face patches implement the following code through recurrent dynamics: Detect face If (face found) Discriminate face else Continue to detect face IMHO, our paper conclusively resolves a debate that has raged since I was a graduate student, about whether face patches are specialized for processing faces or not. It turns out domain-general folks were right early on, domain-specific folks were right later in the response. So proud of @Yuelin_Shi and the entire team!

Great opportunity in an amazing team. Highly recommended! @E11BIO @JohanWinn

I just made my own Star Wars clip using AI (text-to-video)!

(1/n) How do we generalize knowledge across similar experiences? In our new preprint, we introduce S-HAI: a hierarchical active inference model that captures "schemas" used by humans and animals to generalize task abstractions. arxiv.org/abs/2601.18946 🧵

Janelia is hiring Group Leaders! Work alongside superstars like Luke Lavis, @JiefuBiol, @mengwang939, @JLS_Lab, @ERSchreiter. If you have ideas that are perhaps too ambitious or crazy for a traditional academic setting, that is exactly who they are looking for. Tool-builders at all career stages with original, transformative ideas for experimental approaches in imaging, molecular engineering, protein chemistry, mass spectrometry, and methods that don’t yet exist: Apply by Feb. 3, 2026: janelia.org/groupleader

"Before Transformers, RNNs were the thing. These were a big breakthrough. Suddenly, everyone started to work on improving RNNs. But the results were always these slight modifications on the same architecture, like putting the gate in a different spot, with improvements to 1.26, 1.25 bits per character on language modeling." "After the Transformer, when we applied very deep decoder-only Transformers to the same task, we immediately got 1.1 bits per character. So all that research on RNNs suddenly seemed a waste of time". "We're currently in the same situation where a lot of papers are taking the same architecture (Transformer) and making these endless tweaks, in a local minimum, and we might be wasting time in exactly the same way." - Llion Jones, co-author of the Transformer on @MLStreetTalk

If no one builds it, you're never born. ..not building AGI is a risky thing. Wilbur Wright, inventor of airplanes, died in 1912 at the age of 45 from typhoid fever because antibiotics didn’t exist then. Just imagine how our world would be without the medical and technological advancements of the past century! Actually, you wouldn’t have to imagine, because you wouldn’t exist! Inventing technology and advancing science is how we overcame our challenges and managed to support 8 billion human souls on this planet, escaping the Malthusian trap of famines, diseases, and conflicts. Automation of knowledge acquisition and thought is the next step, and the best tool humanity can build. The risk of not building AGI is that we won’t be prepared for the challenges the world throws at us, some of which would be challenges that our own existence creates. A(G)I safety is important, and here are my thoughts about it. 1. Scaling up current techniques is not going to lead to AGI. It will lead to powerful AI systems, but these will be supported by a lot of engineered scaffolding. In these cases, making AI work usefully is almost exactly the same as making AI safe. Since scaling has already proven to be useful, we are naturally on the path to exploiting it to the maximum, and we should. 2. We will eventually figure out how to build and scale AI that uses principles of human intelligence. These systems will learn causal structure and reliable world models that can be used for counterfactual thinking. This will lead to much more capable AI systems and AGI. But for these kinds of systems, increasing capability can also come with increasing controllability. Where powerful AI and AGI is going to help us Earthquakes, wildfires, hurricanes, floods: Despite all the technological advances, we are still at the mercy of nature when it comes to these disasters. Where are the army of robots helping to dig out people from collapsed buildings? Where are the ones to manage fires and help people? Having AGI means we will have robots that will help us in these cases to save lives and to recover faster. Health: Antibiotic resistance, pandemics, …, we don’t know what challenges we will face in the future and it would be great to have powerful tools. In general, having much better understanding of how our bodies and minds work, and curing of diseases. Flora and Fauna: Instead of conforming to the requirements of dumb machines, we might finally be able to do more organic multi-crop agriculture, reduce the amount of pesticides we use, and abolish factory farming. Intelligent machines will free us from the economic necessity of these. Climate change, Energy, Materials, Education, Transportation, Space …. examples like these abound in each of those areas. Things that we accomplished crudely with dumb machinery will be done with more finesse with intelligent machines, and that will be important for humanity to thrive at scale. Balancing the risks… Of course the title is a play on the Yudkowsky and Soares book “If anyone builds it, everyone dies”. While I disagree with many things that the book asserts, their work has brought attention to the important problem of AI safety. Smart people working on AI safety is a good thing. It is important to continue that work, even if the specific x-risk scenarios in the book can be taken apart. In the midst of all the talk about the risks of AGI it is important to realize that not building AGI has risks as well.

Wouldn't it be great if neural networks knew a spiral... as a spiral? 🍥

The US should do something equally big in brain circuit mapping science.org/content/article/…

Incredibly proud to announce that today @E11BIO is releasing our first preprint together with accompanying open data and methods🚀 Here we show how our PRISM technology addresses the biggest bottlenecks in connectomics: tracing and sample fragility.

@E11BIO is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

Veo is a more general reasoner than you might think. Check out this super cool paper on "Video models are zero-shot learners and reasoners" from my colleagues at @GoogleDeepMind.

Why does AI sometimes fail to generalize, and what might help? In a new paper, we highlight the latent learning gap — which unifies findings from language model weaknesses to agent navigation — and suggest that episodic memory complements parametric learning to bridge it. Thread:

Our thoughts on symbols in mental representation, given today's advanced neural nets, w/ @cocosci_lab @RTomMcCoy @Brown_NLP @TaylorWWebb Major open Q: Can neural nets learn symbol-related abilities *without* training on massive, unrealistic data generated from symbolic systems?

The world renowned Cornell Lab of Ornithology is seeking to hire a new director academicjobsonline.org/ajo/j…