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Category: robotics/AI – Page 103

Zuckerberg Trying to Simulate Human Biology at the Cellular Level

Mark Zuckerberg is following a path paved by fellow billionaires Bill Gates and Warren Buffet: laundering his untold billions through a health research prestige project.

Called the Chan Zuckerberg Biohub — his wife Priscilla Chan, a pediatrician, is also involved — the foundation’s stated long-term mission is to “cure and prevent all disease through AI-powered biology, frontier research, and state-of-the-art technology.”

True to those enormous goals, the Biohub recently announced a $500 million investment into AI models of human cells, specifically, in order to “accelerate the cure and prevention of all diseases,” Euronews reported.

Temporal superposition and feature geometry of RNNs under memory demands

Abstract: Understanding how populations of neurons represent information is a central challenge across machine learning and neuroscience. Recent work in both fields has begun to characterize the representational geometry and functionality underlying complex distributed activity. For example, artificial neural networks trained on data with more features than neurons compress data by representing features non-orthogonally in so-called *superposition*. However, the effect of time (or memory), an additional capacity-constraining pressure, on underlying representational geometry in recurrent models is not well understood. Here, we study how memory demands affect representational geometry in recurrent neural networks (RNNs), introducing the concept of temporal superposition. We develop a theoretical framework in RNNs with linear recurrence trained on a delayed serial recall task to better understand how properties of the data, task demands, and network dimensionality lead to different representational strategies, and show that these insights generalize to nonlinear RNNs. Through this, we identify an effectively linear, dense regime and a sparse regime where RNNs utilize an interference-free space, characterized by a phase transition in the angular distribution of features and decrease in spectral radius. Finally, we analyze the interaction of spatial and temporal superposition to observe how RNNs mediate different representational tradeoffs. Overall, our work offers a mechanistic, geometric explanation of representational strategies RNNs learn, how they depend on capacity and task demands, and why.

Supplementary Material: zip

Primary Area: interpretability and explainable AI.

Artificial intelligence accelerates discovery of next-generation disinfectants

Chemists and computer scientists tapped AI to find new disinfectants to combat the growing threat of dangerous “superbugs.”

The Journal of Chemical Information and Modeling published their computational-experimental framework for developing quaternary ammonium compounds, or QACs, to kill bacteria.

The method yielded 11 new QACs that show activity against antimicrobial-resistant bacteria.

A Token of Our Imagination: The Invisible Economy Powering GenAI

Ever wonder what actually happens inside the AI after you hit “Enter”?

You type a prompt into your favorite generative AI, and within seconds, your screen fills with exactly what you asked for—whether it’s a quarterly report or a cinematic image of a cyberpunk golden retriever. It feels like absolute magic.

But behind that seamless curtain lies a bustling, microscopic economy running entirely on a digital currency you’ve probably heard of but might not fully understand: the token.

Most of us only ever see the input and the output. We don’t see the internal cash register ringing, the mathematical gymnastics, or the sprawling “assembly line” churning through billions of calculations.

What actually happens between the moment you hit send and the moment your final masterpiece appears? In my newest blog post, I peel back the curtain to trace the fascinating journey of an AI token.

I break down this invisible economy—from the “toll booth” of the input phase to the heavy lifting of the output phase—and show you exactly how the machine balances the books.

Continue reading “A Token of Our Imagination: The Invisible Economy Powering GenAI” | >

AI Is Now Improving Itself

In 1965, a mathematician who worked alongside Alan Turing wrote a single
paragraph that has haunted AI research ever since. He predicted that one
day, a machine would learn to improve itself, and that everything after
that point would change.

Sixty years later, that loop is starting to close.

In this video, we trace how AI got here: from I.J. Good’s 1965 prediction.
to AlphaGo Zero teaching itself Go in 72 hours, to AlphaEvolve cracking a
math problem that had stood unbeaten for 56 years, and then quietly
speeding up the training of the very model that runs it. We look at the
data behind the trend (autonomous AI task length is doubling every 7
months), the walls AI keeps running into (compute, data, energy), and what
the people building this technology are actually saying about how close
we are.

This video is an honest look at what \.

AI lets chemists design molecules by simply describing them

Creating complex molecules usually requires years of experience and countless decisions, but a new AI system is changing that. Synthegy lets chemists guide synthesis and reaction planning using simple language, while powerful algorithms generate and evaluate possible solutions. The AI doesn’t just compute—it reasons, scoring pathways and explaining which ones make the most sense.

How controlling light inside a tiny resonator could speed AI chips and secure communications

A new technology allows light to be “designed” into desired forms, potentially making AI and communication technologies faster and more accurate. A KAIST research team has developed an “integrated photonic resonator”—a core component of next-generation optical integrated circuits that process data using light. Interestingly, the research was led by an undergraduate student. This technology is expected to serve as a key foundation for next-generation security technologies such as highspeed data processing and quantum communication.

The resonator developed by the research team of Professor Sangsik Kim from the School of Electrical Engineering, in collaboration with Professor Jae Woong Yoon’s team from the Department of Physics at Hanyang University, is capable of freely controlling optical signals by utilizing light interference (the phenomenon where two light waves meet and influence each other). Their paper is published in Laser & Photonics Reviews.

Photonic Integrated Circuits (PICs) process data at ultra-high speeds and with low power consumption using light. They are garnering significant attention as a fundamental platform technology for next-generation fields such as AI, data centers, and quantum information processing.

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