I suspect stage 1 is to get current models to assist with making AI breakthroughs.
They probably have ideas and a research direction, but it's probably going too far to call whatever they have a plan to get to AGI, since no one knows how to get there. At best you could probably say they have a plan to search for a path to AGI.
> I suspect stage 1 is to get current models to assist with making AI breakthroughs.
That's sounds like a sci-fi trope.
OpenAI barely has any moat, I Seriously wonder why no one is talking about hoq much open weighted models are getting to the closes source ones, they need invesotr cash to build something that would let them stabd out, otherwise no one would bither paying for OpenAI API when you can just run your own model.
It can do this to a limited degree. I did mean "help with" in a very practical, limited way.
Artificial.
Good enough.
Intelligence.
Besides, most people i know that use AI say they would get much more out of it with smaller more specifically trained models than the all knowing one we have now, but that depends.
https://www.noemamag.com/artificial-general-intelligence-is-...
What most people mean when they say AGI is ASI.
How do you know what most people mean?
Update: I agree with the article that a reasonable amount of generality exists with chatGPT. I dispute the intelligence part of it. It cannot evaluate the truth or falsity of what it spouts out. This appears to be a fundamental limitation of LLMs.
> Have they shifted the definition that much, or is there something they’re not telling? My impression is that, based on traditional definitions, no one has the foggiest notion of where to head to achieve it. And if he’s suggesting that “creative use of existing models” is a path to AGI… wow, that’s gotta be a useless definition of AGI.
It's likely they're taking cues from Musk's "Full Self-Driving" fiasco: make big promises, then when progress stalls, redefine the promises to match what you've built.
Then again, we as engineers are always the annoying nerds who get stuck in the details.
A lot of traditional definitions imply some degree of consciousness or sentience.
But AGI will ultimately be defined by capabilities, not similarity to humans.
If a GPT-style LLM meets or exceeds humans at a broad category of tasks, it doesn't matter that it doesn't "understand" the tokens it operates on. Same with self-driving systems or autonomous robots. It doesn't matter if the CNN doesn't have a conception of a "person" - if it can feed the identification vector into the control network, and the control network swerves to avoid the identified object at a rate equal to humans, then that's good enough.
Capability is what matters, nothing else. And right now, we have ALL the parts to do this, we just have to scale them up, and train them better, and connect them.
If you'd showed one of these models to an AI researcher in the 90s or 2000s they would have been jumping up and down shouting "that's AGI!"
As we've been able to play with these models, we as humans can tell that there's something missing, a je ne sais quoi of human intelligence. They can think, they can reason, sometimes on very technical subjects, but the reasoning is shallow and fragile.
But to say that we are nowhere near AGI is definitely an exaggeration. We are on the precipice.
Have they shifted the definition that much, or is there something they’re not telling? My impression is that, based on traditional definitions, no one has the foggiest notion of where to head to achieve it. And if he’s suggesting that “creative use of existing models” is a path to AGI… wow, that’s gotta be a useless definition of AGI.