161–170 of 309 posts
While I feel silly to take seriously something printed in The Economist, I would like to mention that people tend to overestimate the short-term impact of any technology and underestimate its long-term impacts. Maybe AI will follow the same route?
Ah yes, disgraced tabloid The Economist, no one should ever take their writing seriously!
At least within tech, there seem to have been explosive changes and development of new products. While many of these fail, things like agents and other approaches for handling foundation models are only expanding in use cases. Agents themselves are hardly a year old as part of common discourse on AI, though technologists have been building POCs for longer. I've been very impressed with the wave of tools along the lin…
Its probably cliche but I think it's both overhyped and under hyped, and for the same reason. They hype comes from "leadership" types that don't understand what LLMs actually do and so imagine all sorts of nonsense (replacing vast swaths of jobs or autonomously writing code) but don't understand how valuable a productivity enhancer and automation tool to can be. Eventually hype and reality will converge, but unlike e…
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Agents are going to change everything. Once we've got a solid programmatic system driving interface and people get better about exposing non-ui handles for agents to work with programs, agents will make apps obsolete. You're going to have a device that sits by your desk and listens to you, watches your movements and tracks your eyes, and dispatches agents to do everything you ask it to do, using all the information i…
So basically, the "ideal" state of a human is to be 100% active driving agents to vibe code whatever you need, based on every movement, every thought? Can our brains even handle having every thought being intentional and interpreted as such without collapsing (nervous breakdown)? I guess I've always been more of a "work to live" type.
I think the "calculator for words" analogy is a good one. It's imperfect since words are inherently ambiguous but then again so is certain forms of digital numbers (floating point anyone?). Through this lens it's way more normal
Floating point numbers aren't ambiguous in the least. They behave by perfectly deterministic and reliable rules and follow a careful specification.
They are deterministic, and they follow clear rules, but they can't represent every number with full precision. I think that's a pretty good analogy for LLMs - they can't always represent or manipulate ideas with the same precision that a human can.
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All articles published by the Economist are reviewed by its editorial team. Also, the Economist publishes all articles anonymously so the individual author isn't known. As far as I know, they do this so we take all articles and opinions as the perspective of the Economist publication itself.
I’m pretty sure the “what if” in that article was meant in earnest. That article was playing out a scenario, in a nod to the ai maximalists. I don’t think it was making any sort of prediction or actually agreeing with those maximalists.
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[citation needed] People constantly assert that LLMs don't think in some magic way that humans do think, when we don't even have any idea how that works.
It's not some "magical way"--the ways in which a human thinks that an LLM doesn't are pretty obvious, and I dare say self-evidently part of what we think constitutes human intelligence: - We have a sense of time (ie, ask an LLM to follow up in 2 minutes) - We can follow negative instructions ("don't hallucinate, if you don't know the answer, say so")
The general notion of passage of time (i.e. time arrow) is the only thing that appears to be intrinsic, but it is also intrinsic for LLMs in a sense that there are "earlier" and "later" tokens in its input.
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LLMs are absolutely not "fully understood". We understand how the math of the architectures work because we designed that. How the hundreds of gigabytes of automatically trained weights work, we have no idea. By that logic we understand how human brains work because we've studied individual neurons. And here's some more goalpost-shifting. Most humans aren't capable of novel mathematical thought either, but that doesn…
We don't understand individual neurons either. There is no level on which we understand the brain in the way we very much do understand LLMs. And as much as people like to handwave about how mysterious the weights are we actually perfectly understand both how the weights arise and how they result in the model's outputs. As I mentioned in [1] what we can't do is "explain" individual behaviours with simple stories that…
If we knew that, we wouldn't need LLMs; we could just hardcode the same logic that is encoded in those neural nets directly and far more efficiently.
But we don't actually know what the weights do beyond very broad strokes.
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I think it's fine to keep the name, we just have to realise it's like magic. real magic can't be done. magic that can be done is just tricks. AI that works is just tricks.
I didn't realize that magic was the goal. I'm just trying to process unstructured data. Who's here looking for magic?
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This comment reminds me of the forever present HN comments that take a form like "HN is so hypocritical. In this thread commenters are saying they love X, when just last week in a thready about Y, commenters were saying that they hated X."
All articles published by the Economist are reviewed by its editorial team. Also, the Economist publishes all articles anonymously so the individual author isn't known. As far as I know, they do this so we take all articles and opinions as the perspective of the Economist publication itself.
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We have a name: Large Language Models, or "Generative" AI. It doesn't think, it doesn't reason, and it doesn't listen to instructions, but it does generate pretty good text!
[citation needed] People constantly assert that LLMs don't think in some magic way that humans do think, when we don't even have any idea how that works.