Comment by pbrum
13 hours ago
Don't know if you're referring to the headline or the body (which is paywalled). The current headline reads "OpenAI says it has overtaken Anthropic with its latest AI model". Which makes me wonder whether FT itself changed a headline along the lines of what you wrote in the past few minutes?
Apparently Greg Brockman said that as far as he's concerned it may be AGI, or something along those lines.
OTOH OpenAI have their own meaningless definition of AGI as "able to do most commercially valuable work" or somesuch, which I'm sure is not true, and is also not what I'd call AGI.
If it can't learn for itself, then it's certainly not AGI.
The biggest significance of anyone at OpenAI calling it AGI is their contract with Microsoft, giving MSFT access to all their IP, is based around them having achieved AGI, but presumably they can't just declare that unilaterally.
Maybe they see getting out of the Microsoft contract as necessary to IPO.
>OTOH OpenAI have their own meaningless definition of AGI as "able to do most commercially valuable work" or somesuch, which I'm sure is not true, and is also not what I'd call AGI.
It's not meaningless. Specifically it is - "highly autonomous systems that outperform humans at most economically valuable work". In fact, it's one of the most meaningful definitions out there.
>If it can't learn for itself, then it's certainly not AGI.
Now this is meaningless.
>The biggest significance of anyone at OpenAI calling it AGI is their contract with Microsoft, giving MSFT access to all their IP, is based around them having achieved AGI, but presumably they can't just declare that unilaterally.
They can't declare it unilaterally, and those deals have changed significantly. Microsoft’s license to OpenAI models and products runs through 2032, and explicitly includes post-AGI models.
https://openai.com/index/next-chapter-of-microsoft-openai-pa...
> It's not meaningless. In fact, it's one of the more meaningful definitions out there.
Well, it's commercially meaningful, but it's not meaningful it terms of how close we are to achieving human intelligence.
But which one you care about is up to you. If you are all about the money and don't care about human intelligence then for sure go with OpenAI's definition.
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> The biggest significance of anyone at OpenAI calling it AGI is their contract with Microsoft, giving MSFT access to all their IP, is based around them having achieved AGI, but presumably they can't just declare that unilaterally. > >Maybe they see getting out of the Microsoft contract as necessary to IPO.
I don’t have a link handy, but they already loosened up that contract significantly earlier this year, surely because they planned to start claiming AGI to pump their IPO and didn’t want to be beholden to all of those commitments.
Anyone can call anything AGI to be honest, which is why I don't take any large AI company's word for this, cause they just don't define AGI
>If it can't learn for itself, then it's certainly not AGI.
What does this even mean, exactly? For example if it can filter new information to put into the next version of itself does that qualify? If not, explain exactly why that's the case.
Ask 100 people what their definition of AGI is and you'll probably get 100 different answers.
My definition is closer to DeepMind's "can do any [computer-based] task that a human can do", and certainly a human intern can learn on the job and do better on day #2 than day #1.
Learning is basically THE hallmark of intelligence. Being able to learn from experience and use that to do better in the future.
If you could somehow take everything the LLM learnt today and "put it into" an update for tomorrow that would be better than nothing, but the intern on the job is probably learning dozens of things per day that stack upon each other, so that's going to be a S-L-O-W learning AI intern !
Leaving aside the technical issue how you could store a days learning, or what that would even mean, you've also got the privacy and merge issues if this were to be done in the cloud without every customer having their own personalized model.
For real continual learning we need to get past gradient descent-based batch training and develop a new learning algorithm so the model itself learns incrementally as it runs (animal-like predict, observe, learn cycle), rather than being reliant on some external alien to come in, equipped with it's entire learning curriculum, and program its weights.
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