"From AGI to ASI" This paper from Google DeepMind defines how AGI is one human-level general system, and ASI is a system or collective that beats large expert human organizations across almost everything. They argue that the jump may come from scaling, new paradigms, recursive self-improvement, or huge multi-agent AI collectives. With the key idea that digital minds can copy, speed up, share memory, and run in parallel, so superintelligence may look less like one breakthrough and more like accelerating AI civilization.

Literally the perfect juxtaposition. Algo delivered 🤣

MIT Press published a robotics textbook. Then put it on GitHub for FREE. 📌 "Introduction to Autonomous Robots" covers everything: kinematics, sensors, actuators, motion planning, localization, computer vision, and neural networks... from mechanisms all the way to algorithms. It's written for undergraduates. Which means it's actually readable. Most robotics textbooks assume you're already deep in the field. This one builds everything from the ground up, step by step, with real examples. Stanford's Mac Schwager called it "much-needed" (because it genuinely is). Four professors at the University of Colorado Boulder spent years building it from lecture notes. MIT Press published it. Then they open-sourced the whole thing under Creative Commons. PDF. Free. GitHub. If you're trying to understand how autonomous robots actually work (not just the frontier research, but the foundations), this is where to start. 📌 [github.com/Introduction-to-A…] Share this with your fellow roboticist! —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com

Yann Lecun published the most heretical AI paper of the year. He opens by arguing Magnus Carlsen isn't good at chess and only gets more unhinged from there. The Turing Award winner and his co-authors dropped a paper demanding the AI industry abandon its biggest obsession, AGI. Right now, everyone from Silicon Valley CEOs to politicians assumes AGI is the ultimate goal. A machine that can do everything a human can do. LeCun argues that this entire concept is a biological illusion. Humans do not possess "general" intelligence. We are highly specialized biological machines, tuned by evolution simply to survive in the physical world. We only think our intelligence is "general" because we are completely blind to the millions of cognitive tasks we are incapable of comprehending. Which brings us to the chess argument. Magnus Carlsen is the greatest human chess player in history. But compared to a modern computer? He is fundamentally terrible. Our belief that Carlsen is "good" at chess is pure human-centric bias. He isn't objectively good. He's just better than the rest of us, who are biologically awful at it. LeCun says we need to stop building AI to mimic human generality. Instead, he proposes a new North Star: SAI. Superhuman Adaptable Intelligence. Instead of trying to build a machine that mimics our flawed, biologically-limited brains, we need to embrace extreme specialization. SAI is about the speed of adaptation. It is an intelligence that can learn to exceed humans at any specific, economically important task. More importantly, it is designed to fill the vast skill gaps where humans are fundamentally incapable. Things like managing global energy grids in real-time. Or predicting complex molecular structures. The entire AI industry is obsessed with building a digital reflection in our own image. LeCun's paper is a brutal wake-up call.

Trained the first ever Makiina arms to assemble a raspberry Pi into its case

Our mission is to make it easy for anyone to deploy a robot to help them in the real world We wrote an intuitive guide to understanding modern robotics, catered toward an audience that understands technology but not AI robotics We hope that this short blog post embeds in you the core principles that will bring further curiosity.

Meet Axol: a dual-arm robot designed for teams working with physical AI. Made in America. Axol is for builders who believe robots should work, in the real world, not just staged environments, and that the future of physical AI should be open not closed.