News

Good morning, robotics enthusiasts. Agility Robotics wants its new humanoid out of the safety cage and onto the warehouse floor, working alongside people. Digit 5 is bigger, stronger, and faster to recharge than Digit 4 — but the more interesting upgrade is what it does when a human gets too close.

PLUS: A brain implant that decodes your body language Good morning, tech enthusiasts. The U.S. military confirmed for the first time that it has weapons in orbit.

Companies are giving AI specific jobs: checking security cameras, helping farmers understand their records, searching satellite images, and preparing sales quotes. This edition explains how those tools work, who can use them, and what companies have reported so far.

Global conflicts, shifting tariffs, trade disruptions and regulatory uncertainty are forcing companies worldwide to rethink how they manage supply chains. For many, the challenge is not only moving goods from one place to another but understanding, in real time, how a disruption in one part of the world could ripple through factories, suppliers, inventory, transportation costs and customer demand.

We’re introducing ZGateway, the proxy we are using to unify traffic through ZippyDB, Meta’s most widely-used key value store.

Two years ago, Kier Group plc decided to embrace AI – with a little help from work friends and peers. Executives at the UK construction and infrastructure firm were excited by AI’s promise to boost productivity and enhance safety practices.

The Information reports that Nvidia is discussing an equity investment in Perplexity at a valuation above $30 billion.

I recently posted a Substack note about Claude’s new watermarking process and implementation.

Artificial intelligence is already helping clinicians spot patterns in diagnostic images and detect signs of disease. But many of today’s pathology AI systems are built for a single task and must be rebuilt for each new application.

Much of what AI knows about the world comes from data most people never see and from technical processes that determine what’s included or left out. As AI becomes increasingly influential in daily life, a new platform aims to help communities participate in that foundation being built behind the scenes to ensure they’re portrayed accurately and fairly, especially in imagery.

AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater specialization across the stack. To meet this need, Microsoft continues to evolve Azure’s infrastructure, including expanding its AI fleet with AMD’s most advanced AI and high-performance computing (HPC) solutions.

The Theodore Roosevelt Presidential Library opened to the public on July 4. He led the Rough Riders, became the youngest U.S. president and inspired the teddy bear — but Theodore Roosevelt probably never imagined anything like this.

Cancer treatment has gotten more precise over time, as doctors first classified the disease by where it began in the body and, more recently, by the mutations found inside cancer cells to help find the right drugs to treat it. But why can two people with seemingly similar cancers respond so differently to the same medication?

AI has arrived in the enterprise, and the shift is happening all at once. Every function, every role, every workflow is being reshaped. At the same time, a new class of organizations is emerging, one that will look fundamentally different from the companies that defined the last era of business.

In 1928, London was in the middle of a terrible health crisis, devastated by bacterial diseases like pneumonia, tuberculosis, and meningitis.

A Gentle Introduction to Graph Neural Networks A Gentle Introduction to Graph Neural Networks A Gentle Introduction to Graph Neural Networks Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them.

Understanding the building blocks and design choices of graph neural networks. This article is one of two Distill publications about graph neural networks. A Gentle Introduction to Graph Neural Networks for a companion view on many things graph and neural network related.

Over the past five years, Distill has supported authors in publishing artifacts that push beyond the traditional expectations of scientific papers. From Gabriel Goh’s interactive exposition of momentum , to an ongoing collaboration exploring self-organizing systems , to a community discussion of a highly debated paper , Distill has been a venue for authors to experiment in scientific communication.

Adversarial Reprogramming of Neural Cellular Automata Adversarial Reprogramming of Neural Cellular Automata Allen Discovery Center at Tufts University Differentiable Self-organizing Systems Thread , an experimental format collecting invited short articles delving into differentiable self-organizing systems, interspersed with critical commentary from several experts in adjacent fields. This article makes strong use of colors in figures and demos.

This article is part of the Circuits thread , an experimental format collecting invited short articles and critical commentary delving into the inner workings of neural networks.

Multimodal Neurons in Artificial Neural Networks Multimodal Neurons in Artificial Neural Networks We are deeply grateful to Sandhini Agarwal, Daniela Amodei, Dario Amodei, Tom Brown, Jeff Clune, Steve Dowling, Gretchen Krueger, Brice Menard, Reiichiro Nakano, Aditya Ramesh, Pranav Shyam, Ilya Sutskever and Martin Gabriel Goh: Research lead.

Neural Cellular Automata Model of Pattern Formation Differentiable Self-organizing Systems Thread , an experimental format collecting invited short articles delving into differentiable self-organizing systems, interspersed with critical commentary from several experts in adjacent fields. Adversarial Reprogramming of Neural Cellular Automata Neural Cellular Automata (NCA We use NCA to refer to both Neural Cellular Automata and Neural Cellular Automaton .

This article is part of the Circuits thread , an experimental format collecting invited short articles and critical commentary delving into the inner workings of neural networks. The problem of understanding a neural network is a little bit like reverse engineering a large compiled binary of a computer program.

We reverse engineer a non-trivial learned algorithm from the weights of a neural network and use its core ideas to craft an artificial artificial neural network from scratch that reimplements We can compare the curve detectors in a neural network we hand-crafted with the curve detectors in InceptionV1 by measuring how they activate to synthetic curve stimuli.