First, this model is yet to be released. This is a momentum "announcement". When the O1 was "announced", it was announced as a "breakthrough" but I use Claude/O1 daily and 80% of the time Claude beats it. I also see it as a highly fine-tuned/targeted GPT-4 rather than something that has complex understanding.
So we'll find out if this model is real or not by 2-3 months. My guess is that it'll turn out to be another flop like O1. They needed to release something big because they are momentum based and their ability to raise funding is contingent on their AGI claims.
Intelligence has not been LLM's major limiting factor since GPT4. The original GPT4 reports in late-2022 & 2023 already established that it's well beyond an average human in professional fields: https://www.microsoft.com/en-us/research/publication/sparks-.... They failed to outright replaced humans at work not because of lacking intelligence.
We may have progressed from a 99%-accurate chatbot to one that's 99.9%-accurate, and you'd have a hard time telling them apart in normal real world (dumb) applications. A paradigm shift is needed from the current chatbot interface to a long-lived stream of consciousness model (e.g. a brain that constantly reads input and produces thoughts at 10ms refresh rate; remembers events for years and keep the context window from exploding; paired with a cerebellum to drive robot motors, at even higher refresh rates.)
As long as we're stuck at chatbots, LLM's impact on the real world will be very limited, regardless of how intelligent they become.
O3 is multiple orders of magnitude more expensive to realize a marginal performance gain. You could hire 50 full time PhDs for the cost of using O3. You're witnessing the blowoff top of the scaling hype bubble.
Like they've been making it all this time? Cheaper and cheaper? Less data, less compute, fewer parameters, but the same, or improved performance? Not what we can observe.
>> Tell me, what has this industry been good at since its birth? Driving down the cost of compute and making things more efficient.
No, actually the cheaper compute gets the more of it they need to use or their progress stalls.
Do you understand the difference between training and inference?
Yes, it costs a lot to train a model. Those costs go up. But once you trained it, it’s done. At that point inference — the actual execution/usage of the model — is the cost you worry about.
Inference cost drops rapidly after a model is released as new optimizations and more efficient compute comes online.
> What they’ve proven here is that it can be done.
No they haven't, these results do not generalize, as mentioned in the article:
"Furthermore, early data points suggest that the upcoming ARC-AGI-2 benchmark will still pose a significant challenge to o3, potentially reducing its score to under 30% even at high compute"
Meaning, they haven't solved AGI, and the task itself do not represent programming well, these model do not perform that well on engineering benchmarks.
Just to be clear — your position is that the cost of inference for o3 will not go down over time (which would be the first time that has happened for any of these models).
Yes, that's exactly what I'm implying, otherwise they would have done it a long time ago, given that the fundamental transformer architecture hasn't changed since 2017. This bubble is like watching first year CS students trying to brute force homework problems.
That's a very static view of the affairs. Once you have a master AI, at a minimum you can use it to train cheaper slightly less capable AIs. At the other end the master AI can train to become even smarter.
The high efficiency version got 75% at just $20/task. When you count the time to fill in the squares, that doesn't sound far off from what a skilled human would charge
> how is this a plateau since gpt-4? this is significantly better
Significantly better at what? A benchmark? That isn't necessarily progress. Many report preferring gpt-4 to the newer o1 models with hidden text. Hidden text makes the model more reliable, but more reliable is bad if it is reliably wrong at something since then you can't ask it over and over to find what you want.
I don't feel it is significantly smarter, it is more like having the same dumb person spend more thinking than the model getting smarter.
So we'll find out if this model is real or not by 2-3 months. My guess is that it'll turn out to be another flop like O1. They needed to release something big because they are momentum based and their ability to raise funding is contingent on their AGI claims.