A child prodigy who finished his Harvard degree at 14 and his PhD at 17 sat down in 1948 and wrote a single book that invented the entire conceptual vocabulary we still use to talk about AI, robotics, self-driving cars, and reinforcement learning. He never got the credit. Most people have never heard his name. His name was Norbert Wiener. The book was called Cybernetics. Every feedback loop running inside every system you interact with today traces back to one problem he was handed during World War II. The problem was this: how do you aim a gun at a fast-moving airplane? By the time your shell arrives, the plane is somewhere else. You cannot aim at where the plane is. You have to aim at where the plane will be. And the plane's pilot, knowing this, is constantly changing course to make that prediction wrong. Wiener spent years on this. What he built to solve it was not a better gun. It was a new science. He noticed something that nobody had formally described before. The gun system and the human nervous system were solving the same problem using the same method. You observe where the target is. You compare it to where you want to hit. You calculate the gap. You correct. You observe again. He called that loop feedback. Not in the casual sense people use it today. In the precise mathematical sense. A signal goes out. The result comes back. The system compares the result to the goal. The gap between them drives the next action. The loop closes. That mechanism, exactly as Wiener described it in 1948, is what runs inside every thermostat, every autopilot, every cruise control system, and every AI training loop on the planet right now. When GPT-4 learned to answer questions better, it was doing feedback. When AlphaGo learned to play Go, it was doing feedback. When a self-driving car adjusts its steering because it drifted two inches toward the curb, it is doing feedback. The word they all use, the concept underneath the word, the mathematics formalizing the concept, all of it came from one book written by a child prodigy in 1948 who was trying to figure out how to shoot down a plane. The deeper insight was what he proved about living systems and machines. Before Wiener, biology and engineering were treated as completely separate domains. Organisms adapted. Machines calculated. The idea that you could describe both using the same mathematical framework was not just unusual. It was considered a category error. Wiener proved it anyway. He showed that a brain correcting a reaching movement and a missile correcting its trajectory were running mathematically identical control loops. The hardware was different. The math was the same. Living systems and engineered systems obeyed the same laws once you understood what those laws actually were. He named the field after the Greek word for steersman. Kubernetes. Cybernetics. The person who holds the rudder, reads the water, and adjusts constantly to hold a course through a current that is always pushing the ship somewhere else. That is the mental image he wanted. Not a machine that executes instructions. A system that responds to its own results. The third thing he did is the part almost nobody connects to modern AI. In 1948, Wiener spent an entire chapter of Cybernetics warning about what would happen when machines that learn from feedback were given control over consequential decisions. He described the displacement of workers not as a distant possibility but as a near-term certainty. He wrote about the ethical risks of building systems that optimize for measurable proxies of human values rather than actual human values. He described in plain language what alignment researchers today call Goodhart's Law without using that name, 25 years before Charles Goodhart published anything. He was a mathematician in 1948 writing about problems that AI safety researchers are still trying to solve in 2026. The book is dense in places. The equations are real and the sections on statistical mechanics require actual attention. But Wiener knew this, which is why in 1950 he published The Human Use of Human Beings, which is the same book with all the math removed. Same ideas. Same warnings. Written for anyone who reads English. That second book has been in print for 75 years and almost nobody in tech has read it. Wiener died in 1964 at a conference in Stockholm. He collapsed mid-conversation between sessions. He was 69. He did not live to see a personal computer. He did not live to see the internet. He never saw reinforcement learning, neural networks, or the AI systems that run almost entirely on the mathematical architecture he designed while trying to solve a World War II gunnery problem. Every AI lab in the world today is building systems that run on his framework. Almost none of the people building those systems know his name. The field he founded, cybernetics, mostly disappeared as a word. The ideas did not disappear. They dissolved into every other field. Control theory. Cognitive science. Computer science. Neuroscience. AI. They each took a piece of what he built and called it their own terminology. The word that survived is the one that proves he invented it. Feedback. You use it every day. You use it in code reviews, in meetings, in conversations about AI performance. Every time you use it in the technical sense, meaning a signal that closes a loop between output and goal, you are using the exact definition Wiener wrote down in 1948. He gave the word its meaning. Most people using it have never heard of him. The Human Use of Human Beings is free on archive. Cybernetics is in print and available anywhere books are sold. His major essays are in academic archives at no cost. The man who built the foundation of modern AI was writing about its dangers before the first commercial computer existed. Most people building AI today have never read a word he wrote.

Binance Alpha will remove the following tokens on 2026-05-29 at 6:00 (UTC): $DIGI, $K, $SKI, $JOJO, $PLAYSOLANA, #恶俗企鹅#, $PAL, $TYCOON, $HIPPO, $LN, $BNBXBT, $BOOM. Withdrawing or selling of these tokens on Binance Alpha will still be allowed after the removal. Users can do so by: - Binance Alpha: Go to the [Asset] tab > [Alpha] > Withdraw > Select the token - Binance Alpha: Go to the [Asset] tab > [Alpha] > Select the token > Instant > Sell - Binance Wallet: Go to the [Market] tab > Search > Trade For more information 👉

Binance Alpha will remove the following tokens on 2026-05-14 at 6:00 (UTC): $PRAI, $COMMON, $PINGPONG, $TAKER, $JANITOR, $GATA, $KLINK, $CORL, $SWTCH, $ARIAIP, $LONG, $ZKWASM, $GORILLA, $ECHO, $LITKEY, $FIR, $GM, $DELABS, $DONKEY, $WHY. Withdrawing or selling of these tokens on Binance Alpha will still be allowed after the removal. Users can do so by: - Binance Alpha: Go to the [Asset] tab > [Alpha] > Withdraw > Select the token - Binance Alpha: Go to the [Asset] tab > [Alpha] > Select the token > Instant > Sell - Binance Wallet: Go to the [Market] tab > Search > Trade For more information 👉

The Linux Foundation Announces $12.5 Million in Grant Funding (via @AlphaOmegaOSS and @OpenSSF) @AnthropicAI , @AmazonWebServices, @GitHub, @Google, @GoogleDeepMind, @Microsoft, @OpenAI to Invest in Sustainable Security Solutions for #OpenSource#

Australian tech entrepreneur Paul Conyngham explains how he used ChatGPT/AlphaFold (spent $3,000 with no biology background) to create a custom MRNA vaccine to treat his dog’s cancer tumors. Unreal.

Still incredible that the DeepMind documentary has footage of exact moment Demis is told that AlphaFold can “easily” predict all known (1-2B) protein sequences “in a month” and he says to do it. Then, it shows the moment AlphaFold is released to the world.

I’ve always believed the No.1 application of AI should be to improve human health. That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease! We are turbocharging that goal with $2.1B in new funding.

If Hantavirus mutated into a global threat, it would unleash AI + biotech unlike anything we've ever seen. > genome sequenced and public in 4 hours > AlphaFold maps every protein target > AI screens 10,000 drugs in 24 hrs > 50 vaccine candidates designed simultaneously > AI designed antibodies in days > risk of death computed instantly > decentralized trials launch globally > enroll from home > 20 countries manufacturing at once > first doses in three weeks > real-time dose characterization > your genome + biomarkers determine your protocol > variant map updates every hour No one would wait for governments.

Agentic Payment的最真实的信息，信息降噪 StableHunterAI日报 ━━━━━━━━━━━━━━━━━━━━━━ 2026-04-10 🔥 #1｜How# agents, digital wallets, and trust are rewriting checkout The post analyzes global checkout trends with a focus on digital wallets and AI agents, which aligns with the AI×Web3×Payment intersection, but it lacks deep technical specifics or major protocol-level changes. → 查看原文 ( 🤖 #2｜AlphaTON# Capital looks to raise $43 million to bolster Telegram’s Cocoon AI infrastructure This item directly addresses a major funding initiative for AI infrastructure within a leading Web3 messaging platform, signaling strategic capital deployment at the intersection of AI, Web3, and Payments via Telegram's ecosystem. → 查看原文 ( 🤖 #3｜CoreWeave’s# $8.5B loan shows how AI is replacing crypto mining finance This item highlights a significant capital shift from crypto mining to AI infrastructure, directly impacting Web3 finance and AI hardware investment trends. → 查看原文 ( 🤖 #4｜Why# AI Agents Hit Snags Onchain It directly addresses AI agents encountering blockchain infrastructure friction, squarely at the intersection of AI and Web3, though it's more of an analysis than a major industry shift. → 查看原文 ( ⛓ #5｜Stablecoin# FX nears ‘institutional-grade’ parity with bank rails in LATAM and East Africa: report This report highlights a significant milestone where stablecoin FX is achieving institutional-grade parity with traditional banking in key emerging markets, directly impacting the Web3 and Payments intersection by demonstrating real-world adoption and competitive infrastructure. → 查看原文 ( ⛓ #6｜Stablecoin# volumes to reach $719T by 2035 as generational wealth shift speeds up crypto adoption It directly addresses the intersection of Web3 (stablecoins, crypto adoption) and Payments (challenging Visa/Mastercard, payment volumes) with a forward-looking industry shift driven by generational wealth transfer. → 查看原文 ( ⛓ #7｜Webinar# Recap: Corporate Treasury Onchain — 24/7 Global Liquidity This content directly addresses the intersection of Web3 and Payments by showcasing how enterprises use Solana and Fireblocks for on-chain treasury management, offering 24/7 liquidity and instant payouts, which signals a tangible shift in corporate financial operations. → 查看原文 ( ⛓ #8｜Webinar# Recap: Payments on Solana - A Production-Ready Ecosystem This webinar recap directly addresses the intersection of Web3 and payments by highlighting major payment players building on Solana for real-world use cases, though it lacks detailed technical or product maturity specifics. → 查看原文 ( ⛓ #9｜From# Zero to a Global Pricing Hub: Binance TradFi’s First 90 Days This item highlights a significant Web3-Payment infrastructure milestone with multi-billion-dollar scaling in TradFi derivatives, but lacks direct AI relevance and deep technical specifics. → 查看原文 (