michael_nielsen

Thank you for sharing. And I'm very sorry for your loss.

A few comments have been kind enough to recommend the textbook I wrote with Ike Chuang. I'm glad they find it useful!

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.

Timothy Chow has a wonderful phrase for this - he describes one of his papers (on forcing) as solving an "open expository problem": https://timothychow.net/forcing.pdf

Agreed, & flagged the submission. The actual article is of interest, but I really don't want to reward inflammatory titles.

I've been wondering at what point AI assistants are going to reduce that to a manageable level? It's unfortunately not obvious what the main bottlenecks are, though Chris and Shan might have a good sense.

I see virtually no politics. Maybe don't follow accounts which post about politics?

A good brief overview here from Tim Gowers (a Fields Medallist, who participated in the effort), explaining and contextualizing some of the main caveats: https://x.com/wtgowers/status/1816509803407040909

You have not understood the statement.

Just so you know: you've completely misunderstood the statement.

There's (at least) two meanings for AI: (1) Software systems based on extensions of the LLM paradigm; and (2) Software systems capable of all human cognitive tasks, and then some.

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.

There goes 50+% of my use.

"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.

It was the records of $100 billion dollars in retirement assets that was deleted by Google. Not a small deal, and far more than many small banks.

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

Stoll has written a lovely 2010 mea culpa, originally a (now-vanished) comment at: http://boingboing.net/2010/02/26/curmudgeony-essay-on.html,

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 flagged this - it seems too clearly flame bait. If it was an honest mistake, my apologies. Disney had three movies in 2023 which took more than $200 million at the US box office

I mostly agree with this. Certainly the last line!

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'm not sure why they're so often so bad. I wonder if it's the Upton Sinclair effect; to paraphrase slightly: "It is difficult to get a person to understand something, when their hoped-for future wealth depends on not understanding it."

So are the rules of the road.

We have extremely detailed and well-tested models of climate. It's worth reading the IPCC report - it's extremely interesting, and quite accessible. I was somewhat skeptical of climate work before I began reading, but I spent hundreds of hours understanding it, and was quite impressed by the depth of the work. By contrast, our models of future AI are very weak. Something like the scaling laws paper or the Chinchilla paper are far less convincing than the best climate work. And arguments like those in Nick Bostrom or Stuart Russell's books are much more conjectural and qualitative (& less well-tested) than the climate argument

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.

I co-authored that book (written in the late 1990s). I don't know when quantum computing will happen - I only follow developments very, very loosely now - but think you're being much too pessimistic. It is a very (very!) challenging problem, and it's still early days, but there's also been steady and impressive progress for many years.

Quantum computers won't search unordered databases with fewer than ~ sqrt(N) queries. Proved in: https://arxiv.org/abs/quant-ph/9701001

The title is "These scientists aren't using ChatGPT - here's why", which is meaningfully different, and better

Fair enough. I suppose the useful bit is that lots of ignorant people have gone nuts ("OMG this is AGI!") without any details. That's just hype. But, yes, to your point, there is also some substantive and interesting speculation from more knowledgeable people.

LeCun at least knows a lot about AI. Most of the Q* stuff is coming from people who know almost nothing.

"What's an instance where you gave up power for principle?"

I imagine the journalist at some point said: "Just how far do you now walk", and he replied "Oh, I often go for a 6km walk on Sunday." It's a newspaper article, not a technical spec.

I expect to see one every few minutes in San Francisco. When I counted in August, 4 of 100 vehicles going past my house were self-driving (https://twitter.com/michael_nielsen/status/16873429948135628... ). I don't recall the exact time period, but believe it was just under half an hour.

It's likely somehow a reference to the Emperor. Purple cloth was extremely rare and expensive, and it was the colour worn by the Emperors. Indeed, it eventually became a capital crime for people outside the Emperor's family to wear it. I don't know if that was yet true at the time of Vesuvius, although Wikipedia claims Caligula may have had someone killed for wearing purple.

There's a great video of a frog (and several other items) floating in a strong magnetic field, from 1997: https://www.youtube.com/watch?v=KlJsVqc0ywM&ab_channel=APArc...

Lots of objects are diamagnetic, including human beings, and can potentially be made to float in a strong magnetic field.