It's worth noting: the book assumes a fair bit of mathematical background, especially in linear algebra. If you don't have the equivalent of an undergrad CS/math/physics degree (with some linear algebra), it may be better to start with gentler sources.

One such gentler source is the free online text I wrote with Andy Matuschak -- https://quantum.country. I'm sure there are others which are very good, but perhaps that's helpful!

Both books focus on foundations of the field, and don't cover recent innovations -- the book with Ike Chuang is 26 years old! Still, many of the foundations have remained quite stable.

It's not yet clear what (1) has to do with (2). Maybe it turns out that LLMs or similar can do (2). And maybe not.

I can understand being skeptical about the economic value of (1). But the economic value of (2) seems obviously enormous, almost certainly far more than all value created by humanity to date.

"LLMs are no good for [use case X]" often means "I aren't very good at using LLMs for [use case X]".

With many powerful tools - violins, say, or carpentry tools - we know that it takes a long time and a lot of learning to achieve competent performance, much less virtuoso performance. Someone who spent ten hours learning the violin and concluded "Violins sound terrible" wouldn't have diagnosed a problem with violins, but with their own mastery. I certainly think current LLMs have some big intrinsic weaknesses, but also that what they are is quite subtle.

I am, unfortunately, a customer of the retirement firm, UniSuper.

I saved the comment. Quoting:

"Of my many mistakes, flubs, and howlers, few have been as public as my 1995 howler.

Wrong? Yep.

At the time, I was trying to speak against the tide of futuristic commentary on how The Internet Will Solve Our Problems.

Gives me pause. Most of my screwups have had limited publicity: Forgetting my lines in my 4th grade play. Misidentifying a Gilbert and Sullivan song while suddenly drafted to fill in as announcer on a classical radio station. Wasting a week hunting for planets interior to Mercury’s orbit using an infrared system with a noise level so high that it couldn’t possibly detect ‘em. Heck – trying to dry my sneakers in a microwave oven (a quarter century later, there’s still a smudge on the kitchen ceiling)

And, as I’ve laughed at others’ foibles, I think back to some of my own cringeworthy contributions.

Now, whenever I think I know what’s happening, I temper my thoughts: Might be wrong, Cliff…

Warm cheers to all,

-Cliff Stoll on a rainy Friday afternoon in Oakland"

I've been reflecting on Jeremy's comments, though, and agree on many things with him. It's unfortunately hard to tease apart the hard corporate push for open source AI (most notably from Meta, but also many other companies) from more principled thinking about it, which he is doing. I agree with many of his conclusions, and disagree with some, but appreciate that he's thinking carefully, and that, of course, he may well be right, and I may be wrong.

I say this as someone who written several pieces about xrisk from AI, and who is concerned. The models and reasoning are simply not nearly as detailed or well-tested as in the case of climate.

Comments URL: https://news.ycombinator.com/item?id=38894707

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