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Decision making was the bottleneck all along. Productivity is the rate at which you make open-ended decisions, the rate at which you reduce future paths.
Every Pixar movie hits way harder when you're an unc than when you're a kid
Most documented psychological biases are not irrational, they are highly optimized, energy-efficient shortcuts meant for a biological substrate operating under strict real-time physical constraints and a limited caloric budget
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The JAX package is now around the same level, 20M monthly downloads. Which is incredibly fast growth, because 5 years ago I recall it being below 2M or so. It went from niche to mainstream in the past couple of years. Well deserved success.
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The Keras package recently crossed 21M monthly downloads on PyPI, an all-time high (the daily ATH is around 900k). I still remember when it first crossed 10M monthly downloads about 5 years ago and I thought it couldn't possibly go any higher...
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This reminds me of computerization. The amount of "work" people could execute on computers increased by a huge factor, but their productivity did not. The amount of work "needed" to arrive at the same high-level outputs exploded.
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The quantity of code that devs ship has roughly 10xed. But net developer productivity (value created by unit of time) is only up by a bit, if at all. Part of it is that the additional code is solving more incremental problems. A bigger part is that the new code is creating problems of its own.
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网友在微博爆料，一位英伟达北京分公司员工晒出了工资条，总薪酬1688万，个税687万……
月薪11.43万，基础年薪100万，剩下全是股票分红。
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Symbolic learning is not a replacement for coding agents, it's a replacement for gradient descent & NNs: a low-level, completely general, extremely scalable new learning substrate.