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Wake up, babe, new OpenAI frontier model just dropped.
Well, you can’t actually use it yet. But the benchmarks scores are a dramatic leap up.. Perhaps most strikingly, o3 does VERY well on one of the most important and influential benchmarks, the ARC AGI challenge, getting 87% accuracy compared to just 32% from o1. Creator of the challenge François Chollet seems very impressed.
What does all this mean? My view is that this confirms we’re near the end-zone. We shouldn’t expect achieving human-level intelligence to be hard in the first place, given all the additional constraints evolution had to endure in building us (metabolic costs of neurons, infant skull size vs size of the birth canal, etc.). Since we hit the forcing-economy stage with AI sometime in the late 2010s, ever greater amounts of human capital and compute have been dedicated to the problem, so we shouldn’t be surprised. My mood is well captured by this reflection on Twitter from OpenAI researcher Nick Cammarata:
The only real sign we're near the end-zone is when we can ask a model how to make a better model, and get useful feedback which makes a model which can give us more and better advice.
I certainly foresee plenty of disruption when we reach the point of being willing to replace people with AI instances on a mass level, but until the tool allows for iterative improvement, it's not near the scary speculation levels.
The problem of improving AI is a problem which has seen an immense investment of human intelligence over the last decade on all sides.
On the algorithmic side, AI companies pay big bucks to employ the smartest humans they can find to squeeze out any improvement.
On the chip side, the demand for floating point processing has inflated the market cap of Nvidia by a factor of about 300, making it the second most valuable company in the world.
On the chip fab side, companies like TSMC are likewise spending hundreds of billions to reach the next tech level.
Now, AI can do many tasks which previously you would have paid humans perhaps 10$ or 100$ to do. "Write an homework article on Oliver Cromwell." -- "Read through that thesis and mark any grammatical errors."
However, it is not clear that the task of further improving AI can be split into any amount of separate 100$ tasks, or that a human-built version of AI will ever be so good that it can replace a researcher earning a few 100k$ a year.
This is not to say that it won't happen or won't lead to the singularity and/or doom, perhaps the next order of magnitude of neurons will be where the runaway process starts, but then again, it could just fizzle out.
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Altman saying "Maybe Not" to an employee who said they will ask the model to recursively improve itself next year. https://x.com/AISafetyMemes/status/1870490131553194340
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You already can. Chatgpt says:
Increase Model Depth/Width: Add more layers or neurons to increase the capacity of your neural network.
Improve the Dataset
Computational Resources
Use Better Hardware: Train on GPUs or TPUs for faster and more efficient computations.
There really isn't much secret sauce to AI, it is just more data, more neurons.
Presumably this meant "the sort of useful feedback that a smart human could not already give you".
Claude can give useful feedback on how to extend and debug vllm, which is an llm inference tool (and cheaper inference means cheaper training on generated outputs).
The existential question is not whether recursive self improvement is possible (it is), it's what the shape of the curve is. If it takes an exponential increase in input resources to get a linear increase in capabilities, as has so far been the case, we're ... not necessarily fine, misuse is still a thing, but not completely hosed_ in the way Yud's original foom model implies.
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