"AI is getting better rapidly" is the current state of affairs. Arguing "AI is about to stop getting better" is the argument that requires strong evidence.

Telling us all to remember that there's potential for bias isn't so bad. It's a hot button issue.

There was no grand announcement of passing the Turing test or not. Instead the whole idea has faded in importance.

As the models get better, it wouldn't be shocking to me we get to a point that no one cares if the models are considered "AGI" or not.

We will be chasing some new vaguely defined concept.

"Modern" definitions that include non-intelligence related stuff like agency sound like goalpost moving, so it's unclear why would you want them.

Whether there is hype or not, the laws of money remain the same. If you invest and don’t get expected returns, you will be eventually concerned and will do something about it.

I believe that’s Eliezer Yudkowsky’s definition.

They really aren't even great at forming models of language. They are a single model of language. They don't build models, much less use those models. See, for example, ARC-AGI 1 and 2. They only performed ARC 1 decently [0] with additional training, and are failing miserably on ARC 2. That's not even getting to ARC 3.

[0] https://arcprize.org/blog/oai-o3-pub-breakthrough

> Note on "tuned": OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details. We have not yet tested the ARC-untrained model to understand how much of the performance is due to ARC-AGI data.

... Clearly not able to reason about the problems without additional training. And no indication that the additional training didn't include some feature extraction, scaffolding, RLHF, etc created by human intelligence. Impressive that fine tuning can get >85%, but it's still additional human directed training and not self contained intelligence at the level of performance reported. The blog was very generous making the undefined "fine tuning" a footnote and praising the results as if they were directly from the model that would have cost > $65,000 to run.

Edit: to be clear, I understand LLMs are a huge leap forward in AI research and possibly the first models that can provide useful results across multiple domains without being retrained. But they're still not creating their own models, even of language.

>It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model.

my point is more people can try different models and algorithms rather than having to stick to LLMs.

Depends if you’re asking about real world models or synthetic AI world models.

One of them only exists in species with a long evolutionary history of survivorship (and death) over generations living in the world being modeled.

There’s a sense of “what it’s like to be” a thing. That’s still a big question mark in my mind, whether AI will ever have any sense of what it’s like to be human, any more than humans know what it’s like to be a bat or a dolphin.

You know what it’s like for the cool breeze to blow across your face on a nice day. You could try explaining that to a dolphin, assuming we can communicate one day, but they won’t know what it’s like from any amount of words. That seems like something in the area of neuralink or similar.