But the problem is these agents are just naturally pretty sloppy and they're naturally pretty spiky in their capability profiles. And so you wouldn't necessarily even if you noticed a weird error that it made, you wouldn't necessarily jump to the conclusion that it was like because of some sort of malign crazy conspiracy.
I am very much opposed to the view that the AIs are sentient, or might be sentient. I view that as a category error , and the chances of it being true are vanishingly small.
Linux conquered everything else. All the AI infrastructure that everyone runs off, it's all running on Linux. All the systems, all the servers, everything is Linux. So it was kind of a curiosity that the desktop and the personal computers we were using really hadn't been captured, but clearly it was just waiting for this moment. Linux spent the time from '91 to now waiting for agents to fully flourish as an end user operating system.
The supercomputer inside our own skull runs on about 25 watts, which hints that the minimum energy needed for intelligence might be small - and that the giant, controversial AI compute centers we're building now may turn out to be a temporary blip rather than a permanent feature.
This is such a useful book. It makes the case that there's no such thing as "human error" - instead most catastrophes are caused by system issues, misaligned incentives, and unrealistic processes. You are not the custodian of an otherwise safe system that you need to protect from erratic human beings.
It becomes insufferable like as a mathematician because you you would basically be like I'm every single time I see one of these I kind of don't know if it's worth my time even if 99 out of 100 of them are right.
An agent has no such learning ability. At least not out of the box. It will continue making the same errors over and over again. Depending on the training data it might also come up with glorious new interpolations of different errors.
There isn't this cumulative process which is, uh, sort of built up interactively. Um, it it it seems to be a lot more trial and error and just repetition, brute force, um, you know, which can see it scales and it can work amazingly well in in certain contexts, um, but yeah, this this idea this this sort of building up cumulatively from, um, from partial progress is kind of is what's still not quite there yet.
Further, the fact that there is no such thing as a ‘Dark Ages’ and that a term we used for source survival has escaped containment and been rendered meaningless by a bunch of poorly read self-congratulatory dolts is, in fact, what we call ‘settled academic fact’.
If you go to look about how psychologists think about learning, there's nothing like uh imitation. Maybe there are some extreme cases where humans might do that or appear to do that, but there's no basic animal learning process called imitation.
And that's the thing, it's the pool of data, and I think that's what people miss. We as paleontologists get caught up on single superlative specimens and then try and treat them as a silver bullet almost.
will the error rate ever be controllable or manageable will you ever get to a model that knows when it's wrong which to me seems like given a stat statistical system seems like a contradiction in terms
But it seems like you can understand it through passive observation, which is pretty surprising to me. And again, I think hints at something underlying about the nature of reality in my opinion, beyond just the cool videos that it generates.
I think that this is suggesting that there might not be a theory of quantum gravity, that gravity will emerge at a macroscopic level, out of quantum phenomena. Now, we don’t know how to do that yet, but these are all hints.
Humans being awful drivers leads to a massive increase in vehicle costs and weight, makes cars more dangerous to people outside of them, and imposes significant costs on infrastructure like roads.
is this idea that we form these internal models at particular points of high prediction error or points of, I believe also points of uncertainty, points of surprise or motivationally significant periods. And those points are when it’s maximally optimal to encode an episodic memory.
China built half of the world’s ships (by gross tonnage) in 2022, while the US had 0.2 percent of capacity: in practice, this meant that while China builds hundreds of new ships a year, the US builds three to five.
The Outlaws by Javier Cercas - This book, translated from Spanish, covers the years of Spain after the Franco dictatorship but only obliquely hints at them.
“lots of anonymous people just invented through trial and error and adaptation” just doesn’t cut it for me — I’ve never found such stories to be true upon closer investigation.
My view is that the idea that AI will decide to literally kill humanity is a profound category error. AI is not a living being that has been primed by billions of years of evolution to participate in the battle for the survival of the fittest, as animals are, and as we are. It is math – code – computers, built by people, owned by people, used by people, controlled by people.
Another book I wanted to mention in this context is a fairly obvious choice, Antonio Damasio's Descartes' Error, which is a foundational book about the self, in particular because it reconnects emotion with rationality and the body.
Human error is blamed for over 90 percent of industrial and automobile accidents. It is the leading cause of aviation accidents, and medical error is reported to be the third-largest cause of death in the entire United States. Horrifying? Yes, but why do we label it “human error”? It is design error.
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