a16z Martin Casado 对谈 MakerMaker.AI CTO：多数 RSI 项目只是在卖蒸汽机时代的老概念

a16z's Martin Casado with https://t.co/URbd8O8R0Z CTO Owen He on why a lot of RSI pitches are sellin...

a16z's Martin Casado with MakerMaker.AI CTO Owen He on why a lot of RSI pitches are selling something as old as the steam engine: Martin: "I see a lot of pitches come in, and a lot of them use the term RSI. And I'll be like, 'Oh, so what do you do?' And they'll say, 'We use AI to create AI.' 'That sounds very interesting. So what do you do?' 'We use AI to create GPU kernels.'" Owen: "RSI is definitely an overloaded term today... The important thing when you are differentiating them is to ask the question, what is the optimizer and what is the optimizee? When you're self-improving, who is improving?" "There are people who are saying, 'Okay, we're doing RSI. We have humans using AI to develop better AI.' But that to us is just autocatalysis... the ideas and the improvement come from human brains." Martin: "Most technical epochs have had autocatalytic effects. There's no way you can create a computer chip without a computer... You could build better steam engines because you could get coal faster, etc. So this notion of using the thing to build the thing faster is not new." Owen: "That human brain was never the optimizee. Now, for the first time, we could swap the optimizer as the optimizee... The brain of the agents, which is the LLM, the model, can be the thing that you're optimizing, and this leads to this reinvestment of cognitive improvement into itself." @makermaker_ai @martin_casado Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z 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: youtu.be/3Q1mdn5ZGvM @makermaker_ai @_dval_ @martin_casado Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 2 🔄 3 ❤️ 19 👀 7131 📊 3 ⚡