Rob Wiblin
speaker
1,787 appearances
3 recordings
1 series
first heard Sep 2024
last heard May 2025
Rob Wiblin’s voice in public audio — every appearance, attributed to the second.
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And in fact, um there is there are enough connections between them that you can be the best at all of them simultaneously.
I guess you could imagine in future it might come apart if there's more effort to develop uh you know are really, really good specialized models that uh you know, at the frontier perhaps this isn't the case anymore.
Yeah.
Are there any more uh any any other pros or cons of the generality that are worth flagging?
think you often see this phenomenon where people say this this term is kind of confused.
It's too much of a cluster of different competing things.
But despite all of the criticisms and worries, people just completely keep using it all the time and they always come back to it.
And I think at that point you just have to concede there's actually something super important here that people are desperate to refer to and we just have to clarify this idea rather than give up on it.
On that point, I think actually a slightly uh contrarian uh take that I have is that uh people like I think ML people hate this.
But the the idea that in fact machine learning research, even the cutting edge stuff, like might actually not be that difficult.
That in fact the number the kind of things that if you try to break it down into the specific process by which we're improving these models, uh you've got like theory generation stage, then you've got, well, how do we actually test this?
How do we develop a benchmark?
And then you actually run the thing, very compute intensive, and then you go then you decide.
Whether it was an improvement and then go back to the the regeneration stage.
This might be possible well short general age of a full AGI that has all these different skills, especially if you focused on it.
Like in fact, maybe a lot of this research is like much less difficult in some sense than uh than people might who are involved in it might want to believe.
And they could be relatively easily automated, which would be.
Quite shocking and and quite consequential if true.
So
A reason that this connects is that I think that the the the paper alludes to this idea that as you're approaching AGI, you could start with it being very strong in some areas and relatively weak in other dimensions.
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