Comment by chr15m
16 hours ago
Every 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.
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?
Transference from code to reasoning is not surprising, programming languages are formal logical systems and written code is essentially a concretized form of reasoning about a domain and operations in it. Training people in formal logic also improves their general reasoning capabilities.
Yes it will feel very different.
Yes, capabilities are correlated but an open question is how correlated, and how much negative transfer (where the model regresses on other benchmarks) will come into play.
Why are frontier models not chess geniuses (amateur club level elos), despite knowing the rules deeply, having a great deal of chess data in the training set, and having general reasoning training in other domains? Similarly, a beginner can beat a frontier model at Magic The Gathering. Why?
It is because chess and MTG are not priority targets in the (expensive) training pipeline. Chess is already solved by lesser neural networks so there is no wow factor, and nobody cares if they suck at MTG.
Mathematics on the other hand, has a huge wow factor, and a large and highly verifiable dataset.
Related: why did OpenAI give up on video?
I agree it's disorienting and scary but I'd phrase it like this: "the moment we come up with a systematic, objective way to verify a task, *and we can generate or find large datasets, and somebody has a reason to pay for the expensive training*, we become obsolete at it."
The economics side of it is an important constraint, currently ignored because the big labs are swimming in money (debt).
"Becoming obsolete" due to increased efficiency could lead to sigificant economic and political unrest, but there's no fundamental reason it must.
How much do you want to bet that an LLM with the ability to search the web and write python scripts would smoke the best humans at chess and MTG?
How comforting is it really when it’s not the bare LLM beating you, but the LLM using tools it created?
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IMO I think a partial root of why AI writing is so bad is because it's written like code. Like if you really dive into how it constructs things after iterations and ask it to analyze it , without it saying it's like code or without you saying anything about code, you'll notice coding like iterations.
How many models have you tried? They write very differently. I found Gemini Argon 4 to be really good at writing like a human. Simpler models like Haiku seem better than more powerful ones like Opus, though even Opus has improved considerably with 5.5. DeepSeek is much more roundabout which can mirror certain people’s styles and some people prefer that. So in summary don’t just say LLMs are bad at writing, you need to be more specific: which model, which style, which context, what instructions were given? Claiming they are bad under all combinations is just ignorant.
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> I think a partial root of why AI writing is so bad
I hard disagree with that statement. At shorter outputs, with proper prompting AI produce very decent writing.
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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