#大佬观点

AI is getting better at solving hard math problems, especially given recent drops. But what does that mean for the cryptography that protects everything from private messages to healthcare to banking and crypto wallets? Stanford cryptographer Dan Boneh, with @eddylazzarin, @SuccinctJT, @liz_harkavy, and @smc90, makes sense of the warnings sounded recently. They cover: - what’s fact vs. assumption - what’s new and what risks cryptographers have long prepared for - where quantum computing doesn't (and does) fit - opportunities for builders Highlights: 00:37 – What sparked the most recent debate about AI and cryptography 01:35 – Signal vs. noise: What the math does and doesn’t tell us 05:29 – Why cryptography depends on open problems 09:25 – Why the stakes go beyond blockchains 10:43 – How AI could strengthen confidence in cryptography 13:50 – Considerations for users and institutions 20:10 – Where AI and quantum threats overlap 22:32 – Building systems that can change cryptographic methods 23:59 – Hash-based versus lattice-based cryptography 33:07 – Opportunities for builders, and better security tools 38:14 – What comes next if there were a cryptographic break?

META THINKS THE NEXT BIG AI BREAKTHROUGH IS MEMORY. NOT JUST SMARTER MODELS TIL: Mark Zuckerberg and Meta's chief AI officer Alexandr Wang believe one of the biggest problems holding back personal AI agents is surprisingly simple. They keep forgetting things about you. Meta's new Muse Wonder model introduces something called Active Memory, designed to remember important details from conversations weeks or even months ago and bring them back when they're actually useful. And it's not just about giving AI a bigger context window. Wang says the model is being trained to manage its own memories, decide what information matters, and retrieve it when needed. Imagine an AI that remembers your family's preferences, what you discussed last month, and what you've been planning, then uses that information to handle things like shopping or organizing activities without making you explain everything again. But there's a much bigger reason Meta is spending so much effort on memory. Zuckerberg thinks personal agents could eventually become the main way people interact with the internet. And an AI that remembers your life could have a very different relationship with you than today's chatbots... [Read the rest of the story on Antfeed] #Meta #MarkZuckerberg #AlexandrWang #MuseWonder #Muse #ActiveMemory #AIAgents #ArtificialIntelligence #PersonalAI #AIModels #Superintelligence #AIInfrastructure #SiliconValley #인공지능 #메타 #마크저커버그 #알렉산더왕 #AI에이전트 #AI메모리 #장기기억 #초지능 $META $NVDA $MSFT $GOOGL

Nvidia 576 GPUs Rubin Ultra slide!

http://MakerMaker.AI co-founders Dhaval Adjodah and Owen He with a16z's Martin Casado on recursive self-improvement, and how to tell when it's real: A lot of RSI pitches are really just people using AI to build better AI. Tech has always worked that way. You needed computers to build chips. Steam engines got better once you could get coal faster. Dhaval and Owen's bar is higher. The model has to improve itself: its weights, its harness, even the prompt that tells it what to work on next. And the gains have to hold up when you count what they cost. They track everyone's experiment spend on a "bleeder board," and one engineer, the Viceroy of Variance, has the job of breaking their results. Over the past few months, they've started seeing early signs of their system getting better and cheaper at the same time. 0:55 Why most RSI pitches miss 2:25 Optimizer vs. optimizee 5:10 The RSI equation: performance over cost 6:50 Why a closed loop does not mean takeoff 8:40 Humans are agents inside the system too 9:25 The denominator trap: progress divided by capital raised 11:40 Training GPT-2 for $100 13:40 Most RSI papers fail when replicated 16:40 Why you have to co-optimize 17:40 Why inference is the first problem 20:15 The agent rewrites its own prompt 22:20 "It's not a belief. It's happening." 25:00 Why they haven't published 27:50 Intelligence is a vector and Amdahl's law 29:45 The garden hose theory of RSI YouTube: https://youtu.be/3Q1mdn5ZGvM @makermaker_ai @_dval_ @martin_casado

NVIDIA and @Cisco introduce pluggable optics to scale AI factories. USFP is the first module to support 400Gb/s per lane, delivering 4x the bandwidth density of today's optics. AI builders can connect more compute to the same switch — no extra network tiers, less power per bit moved. Learn more: https://nvda.ws/4B6Ch7e

http://MakerMaker.AI co-founders Dhaval Adjodah and Owen He with a16z's Martin Casado on recursive self-improvement, and how to tell when it's real: A lot of RSI pitches are really just people using AI to build better AI. Tech has always worked that way. You needed computers to build chips. Steam engines got better once you could get coal faster. Dhaval and Owen's bar is higher. The model has to improve itself: its weights, its harness, even the prompt that tells it what to work on next. And the gains have to hold up when you count what they cost. They track everyone's experiment spend on a "bleeder board," and one engineer, the Viceroy of Variance, has the job of breaking their results. Over the past few months, they've started seeing early signs of their system getting better and cheaper at the same time. 0:55 Why most RSI pitches miss 2:25 Optimizer vs. optimizee 5:10 The RSI equation: performance over cost 6:50 Why a closed loop does not mean takeoff 8:40 Humans are agents inside the system too 9:25 The denominator trap: progress divided by capital raised 11:40 Training GPT-2 for $100 13:40 Most RSI papers fail when replicated 16:40 Why you have to co-optimize 17:40 Why inference is the first problem 20:15 The agent rewrites its own prompt 22:20 "It's not a belief. It's happening." 25:00 Why they haven't published 27:50 Intelligence is a vector and Amdahl's law 29:45 The garden hose theory of RSI YouTube: https://youtu.be/3Q1mdn5ZGvM @makermaker_ai @_dval_ @martin_casado

"Energy Capacity = Economic Capacity" Former Intel CEO Pat Gelsinger on why America's power grid is the ceiling on growth: "We went through 10 to 15 years where essentially I was taking coal offline at the rate I was adding renewables, and essentially the nation was flatlined in terms of overall energy capacity." "So essentially our economic capacity as a nation, flat for fifteen years." "Even with that, we've gone to maybe increasing our national energy capacity 4% per year. Going from essentially zero to one." "Nuclear as base load is a fabulous thing that we want to go build more of. Unfortunately all of our renewables have deep dependencies in China." "Energy capacity somewhat dampens how euphoric we can get on AI. Why build a new data center and buy the million GPUs if I can't power them?" @PGelsinger @RaghuRaghuram @appenz