alt Hacker NewsEvery dot in these graphs is composed of multiple benchmarks smooshed together.
Benchmarks are by definition a curated set of things that can be measured and evaluated.
Things that can be measured and evaluated can be (expensively) trained on.
The progress we are observing comes from figuring out how to measure and evaluate particular aspects of intelligence, and then expensively training on feedback loops made of those exact measurements, and synthesized data representing further measurements (literally benchmaxing).
What is not shown in these graphs are the set of things that intelligence can do, which are difficult, expensive or impossible to measure and evaluate.
That set of things is much larger than the set of benchmarks represented in the graph.
Therefore the post is misleading in the sense that the "superintelligence" being targeted is "jagged" to use Karpathy's term, and we infact already have jagged superintelligence (calculators, compilers, Bayesian spam classifiers, etc.)
We are witnessing a profound explosion of much more jagged superintelligence, restricted to the exact domains that can be (expensively) RL'ed on.
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But picture the next 10 years, where all kinds of people, organizations, etc train in a ll different ways with diff philosophies and goals - I feel like there will be some good, unusual, interesting ones in there, even if they all have their own complex set of limitations.
Apart from physical world interactions which is obviously lacking, which domains lag behind and do not improve then?
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This isn't really comforting to me for 2 reasons:
1. A key insight of foundation models is that capabilities are correlated. For example, training on code improved general reasoning capabilities: https://arxiv.org/abs/2309.16298
2. A profound capabilities explosion in easily measured domains will still feel very different to today. Like, the moment we come up with a systematic, objective way to verify a task, we become obsolete at it?