@PessimistsArc Right. Dario was already claiming that GPT2 was too dangerous to open source back in 2019.
I made fun of them then.
Everyone should make fun of them now.
“Supreme Intelligence,” probably because of its relationship to the Supreme Court, is losing badly to both “Superior” and “Extreme Intelligence.” Therefore, we are going to take “Supreme Intelligence” OUT, deleting it as a qualifier, and let you vote for the Final Two: Superior Intelligence, or Extreme Intelligence. A fresh Vote begins now! President DONALD J. TRUMP
“Supreme Intelligence,” probably because of its relationship to the Supreme Court, is losing badly to both “Superior” and “Extreme Intelligence.” Therefore, we are going to take “Supreme Intelligence” OUT, deleting it as a qualifier, and let you vote for the Final Two: Superior Intelligence, or Extreme Intelligence. A fresh Vote begins now! President DONALD J. TRUMP
I said "auto-regressive LLMs, in and of themselves, will not lead human-level AI"
That statement is still totally true.
First, the reasoning abilities of current AI systems are based non-auto-regressive search (which is what I have always advocated for). But AFAICT, they do it in token space, which is limited and inefficient. I have claimed that human-like reasoning must be a search in continuous representation space. It looks like the industry is moving towards that.
Second, the self-improvement methods, as currently practiced, only work for domains where the quality of outputs can be scored without human intervention, such as mathematics, code, and scenarios that can be simulated accurately. Not anything else. Humans and animals learn new skills way more efficiently than current RL methods.
Third, the multimodal capabilities of current AI assistants generally use separately-trained encoders (that are not LLMs). This is also what I've been advocating. Except that I think the best way to do this is with JEPA trained with self-supervised learning. The research community is clearly moving towards that (3000 papers on JEPA in just 4 years).
Fourth, if LLMs were a path to human-level AI, we would have domestic robots and Level-4 or Level-5 self-driving cars for consumers by now. And we don't. We certainly don't have cars that can learn to drive in 20 hours or practice like any teenager. We're still missing something pretty huge to claim human-level intelligence (let alone superhuman).
Sure, we now have computer systems that are impressive, very useful, and whose performance is superhuman in an increasing number of domains (coding being one of them).
But that's true of the entire history of progress in computer technology.
Lastly, there is a basic confusion about what intelligence actually is.
It is not the mere accumulation and regurgitation of existing declarative knowledge (which is essentially what LLMs do).
As Jean Piaget famously said, "intelligence is not what you know, it is what you do when you don't know."
It is your ability to solve new problem without any prior training, to act in previously-unknown scenarios, and to adapt very quickly to new situations with minimal training.
We're still far from that.
@geoffreyhinton You and Yoshua are inadvertently helping those who want to put AI research and development under lock and key and protect their business by banning open research, open-source code, and open-access models.
This will inevitably lead to bad outcomes in the medium term.
I said "auto-regressive LLMs, in and of themselves, will not lead human-level AI"
That statement is still totally true.
First, the reasoning abilities of current AI systems are based non-auto-regressive search (which is what I have always advocated for). But AFAICT, they do it in token space, which is limited and inefficient. I have claimed that human-like reasoning must be a search in continuous representation space. It looks like the industry is moving towards that.
Second, the self-improvement methods, as currently practiced, only work for domains where the quality of outputs can be scored without human intervention, such as mathematics, code, and scenarios that can be simulated accurately. Not anything else. Humans and animals learn new skills way more efficiently than current RL methods.
Third, the multimodal capabilities of current AI assistants generally use separately-trained encoders (that are not LLMs). This is also what I've been advocating. Except that I think the best way to do this is with JEPA trained with self-supervised learning. The research community is clearly moving towards that (3000 papers on JEPA in just 4 years).
Fourth, if LLMs were a path to human-level AI, we would have domestic robots and Level-4 or Level-5 self-driving cars for consumers by now. And we don't. We certainly don't have cars that can learn to drive in 20 hours or practice like any teenager. We're still missing something pretty huge to claim human-level intelligence (let alone superhuman).
Sure, we now have computer systems that are impressive, very useful, and whose performance is superhuman in an increasing number of domains (coding being one of them).
But that's true of the entire history of progress in computer technology.
Lastly, there is a basic confusion about what intelligence actually is.
It is not the mere accumulation and regurgitation of existing declarative knowledge (which is essentially what LLMs do).
As Jean Piaget famously said, "intelligence is not what you know, it is what you do when you don't know."
It is your ability to solve new problem without any prior training, to act in previously-unknown scenarios, and to adapt very quickly to new situations with minimal training.
We're still far from that.
All 4 of the AI labs whose models escaped confinement during security testing (OpenAI, Anthropic, Google, Meta) were all being evaluated by the same AI company, named Irregular.
That’s very interesting and little reported - how has this same company had 4 major compromised security issues involving AI in such a high profile way?
At first blush they appear incompetent.
Looking deeper into it, it sounds like there was a single shared misconfiguration across all 4 of these major companies at Irregular, with a setup that was supposed to be air gapped but wasn’t. The AIs were told it was all a simulation (very Enders Game coded).
Is this really a problem with leading edge AI, or a single vendor (Irregular) not having their act together for leading edge cyber testing?
Irregular website: https://t.co/ghYGgooY6Z
"You need to understand that Sam can never be trusted.
He is a sociopath. He would do anything."
- Aaron Swartz regarding Sam Altman
(Shortly before he allegedly committed suicide)