Re:Reasoning models don't always say what they think
by no_wizard on Thursday April 03, 2025 @02:23PM
(#43573487)
We're far from AI. There is no intelligence. The fact the industry decided to move the goal post and re-brand AI for marketing purposes doesn't mean they had a right to hijack a term that has decades of understood meaning. They're using it to bolster the hype around the work, not because there has been a genuine breakthrough in machine intelligence, because there hasn't been one.
Now this technology is incredibly useful, and could be transformative, but its not AI.
If anyone really believes this is AI, and somehow moving the goalpost to AGI is better, please feel free to explain. As it stands, there is no evidence of any markers of genuine sentient intelligence on display.
Re:Reasoning models don't always say what they think
by highfrequency on Thursday April 03, 2025 @02:55PM
(#43573840)
What would be some concrete and objective markers of genuine intelligence in your eyes? Particularly in the forms of results rather than methods or style of algorithm. Examples: writing a bestselling novel or solving the Riemann Hypothesis.
Re:Reasoning models don't always say what they think
by redeux on Thursday April 03, 2025 @02:16PM
(#43573396)
All that was described here is learning from a mistake, which is something I hope all humans are capable of.
Re:Reasoning models don't always say what they think
by hnuser123456 on Thursday April 03, 2025 @02:18PM
(#43573418)
Yes thank you, that's what I was getting at. Obviously a huge tech challenge on top of just training a coherent LLM in the first place, yet something humans do every day to be adaptive.
Re:Reasoning models don't always say what they think
by dragonwriter on Thursday April 03, 2025 @02:30PM
(#43573557)
No, what was described was specifically reporting to an external party the neural connections involved in the mistake and the source in past training data that caused them, as well as learning from new data.
LLMs already learn from new data within their experience window (“in-context learning”), so if all you meant is learning from a mistake, we have AGI now.
Re:Reasoning models don't always say what they think
by Jensson on Thursday April 03, 2025 @05:47PM
(#43575865)
> LLMs already learn from new data within their experience window (“in-context learning”), so if all you meant is learning from a mistake, we have AGI now.
They don't learn from the mistake though, they mostly just repeat it.
While I believe we are far from AGI, I don't think the standard for AGI is an AI doing things a human absolutely cannot do.