MakerMaker.AI CEO Dhaval Adjodah 与 CTO Owen He 在 a16z 播客中介绍公司如何构建自我改进的 AI 体系。他们认为优秀的工程师和研究员难以被智能体替代，因此把整个公司视为一个多智能体系统，人类也是其中一种智能体。公司设有专门破坏实验结果的“方差总督”岗位，通过“bleeder board”追踪实验开销，衡量性能相对成本的提升。
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http://MakerMaker.AI CEO Dhaval Adjodah and CTO Owen He on building a self-improving AI company where humans and agents work as one system:
"One big limit of [RSI] is the human element. I think the way that a lot of AI companies are thinking is, let us use AI to replace humans."
"But what we found is that there's so much that good researchers and good engineers can bring to the table that's gonna be very hard to replace by agents."
"And so that's why from the very beginning, we've tried to think of our basically entire company as a multi-agent system, where humans are a kind of agent that's cohabitating with other AI agents."
"We have one of our engineers that we named the Viceroy of Variance. That's a real thing. His job is to go and destroy the results we have and find what's wrong."
"So far we have not found a replacement for what we call quality time. Literally running hundreds of experiments, looking at it, developing an intuition, trying to figure out what is wrong, what is not wrong... what is just variance, what is not."
@makermaker_ai @_dval_
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在 X 查看被引用的帖子
来源：a16z · x.com