The second law of thermodynamics says disorder always increases. But life defies this by creating order. | https://t.co/POUTDea3ij
Philosopher Dorothea Olkowski draws on Ilya Prigogine's work to ask whether the classical version of the law ever described the reality of life at all.
The human brain is strikingly modular, with distinct networks for language, formal reasoning, social reasoning, and physical reasoning. Is this a fundamental principle of how intelligent systems are built, or an accident of biological evolution?
In our latest preprint, we find that a similar modular organization emerges in Large Language Models, another class of intelligent system.
Brains and LLMs are shaped by entirely different kinds of optimization (biological evolution vs. gradient descent). That they arrive at the same modular design anyway suggests modularity may be a fundamental property of intelligent systems.
🌐 Web: https://t.co/ZKrnTSSuSf
📄 Paper: https://t.co/ZibBXz3PUy
💻 Code & data: https://t.co/uBo5iOYNjy
Using circuit analyses across 46 tasks spanning four cognitive domains, we find:
1️⃣ Tasks that draw on the same network in humans recruit overlapping units in LLMs, while tasks drawing on different networks recruit distinct units.
2️⃣ These units are causally linked to model behavior. Ablating the units critical for one domain impairs performance in that domain (−26% accuracy) but barely touches the others (−2.5%).
This project has been in the works for a while :) Huge thanks to my advisors @jacobandreas@ev_fedorenko@devarda_a, and to @Nancy_Kanwisher for valuable conceptual input and feedback throughout. #MIT
Introducing Claude Science, a new app designed with every stage of research in mind.
Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect.
Available now in beta.
Updated story on Anthropic's Claude Science launch event
Beyond the product news, Anthropic's leaders say they are also developing their own preclinical drug programs
Focused on neglected diseases, unclear if they plan to take these into clinic: https://t.co/NRgXm9kf2E
New #preprint: an exciting and new aspect of #bioelectricity!
Most work on bioelectricity and electrophysiology focuses on events at the cell membrane. Here, we (@HamidSediqi18 , @cellbioelectric, et al.) tackle a new aspect of the electric dimension of cells: the nuclear membrane.
https://t.co/Xwlop6aUC7
"Ionic Exposure History Shapes Inner Nuclear Membrane Voltage and Chromatin Texture Responses"
We created a new reagent for monitoring the voltage across the nuclear envelope, finding changes in chromatin and ordering effects (i.e., a kind of simple memory of past experiences) in its response to ionic stimuli. This is just the beginning - a new target for monitoring and interventions that I predict will have a lot of impact on nuclear events (chromatin state, gene expression, etc.) and thus cell behavior.
"The working mathematician’s vocabulary includes terms such as set, function, element, subset, and equivalence relation. Any axiomatization of sets will choose some of these concepts as primitive and derive the others. The traditional choice is sets and elements. We use sets and functions." - some simple thoughts expressed elegantly. Tom Leinster is also one of my favorite readings in Category Theory ..
Interesting day for people doing bio work with agents: Arc releasing a new natural language for generative bio models, and Nvidia releasing a better tool calling layer for agents harnesses
Thanks for posting this lecture -- I can recommend it as a panoramic introduction to causal inference, from which one can zoom easily onto any of the topics outlined.
Technically we are not in an AI age, but in a Deep Learning age. Deep Learning is deep faking AI, (mostly) by imitating the patterns of human thought. That does not mean that Deep Learning is limited, but it is wasteful to dig through mountains of our output to find intelligence
edgePython 0.2.6 released.
Thanks to everyone who is sharing issues via Github or just letting me know of desired features / improvements.
https://t.co/NOMbVAEfzw
The assumption that consciousness requires a human or animal brain is the biggest obstacle to understanding what the mind actually is, argues philosopher of science Natalie Lawrence. | https://t.co/gd4Hk38VET
Drawing on her research into the philosophy of biology, Lawrence explores how Spain's MINT Lab is building a rigorous new framework that moves beyond our biases around investigating cognition.
The first complex cells had genes from a complex mix of species
Our ancestors’ genomes were built through successive waves of gene transfers. https://t.co/flMPs4Nj24 @arstechnica
Is categorization something the brain does after perceiving the world, or is it how perception happens in the first place?
A groundbreaking Perspective by Lisa Feldman Barrett and Earl K. Miller in Nature Reviews Neuroscienceargues that categorization is completely "baked" into our neural infrastructure.
Rather than a final-stage sorting process, categorization occurs from the very moment a sensory signal enters the brain. Driven by predictive feedback loops that start at the limbic core, the brain continuously constructs functional context to optimize energy and anticipate metabolic needs (allostasis).
In short: meaning and metabolism are deeply intertwined, and your brain is constantly grouping the world to keep your body running efficiently. @MillerLabMIT https://t.co/S3e5ag5nlI
#Neuroscience #CognitiveScience #BrainResearch #Categorization #Allostasis #PredictiveProcessing #Neuroanatomy
New paper! How do RNAs "know" where to go inside a cell? We dug into the sequence elements that route RNAs to the right place. It turns out that, in mammals, they're surprisingly massive (>200 nt), multipartite, and wonderfully complicated. 🧵