One of my neighbors whoâ(TM)s in his eighties wound up switching to a recumbent tricycle because he had balance issues. Which sucked. But the tricycle is awesome and probably would be comfortable for you to sit on.

Nevertheless, you should ride what works and is enjoyable for you. The point is not to punish 80-year-olds by forcing them to ride bikes they arenâ(TM)t safe on. I have an Azore city bike that I really like. Wouldnâ(TM)t mind still being able to ride it at 80.

I live in Europe. It's definitely not a utopia (nor is it a single country!). However, I don't think that being more like the U.S. would make Europe better. That's my only point here. Yes, Europe could definitely be better (even the European country where I live!), but being more like the U.S. would make it worse. Certainly going in the direction of "growth first" would not make it better, although again I'm sure some percentage of those who emigrated to the U.S. would say this would be better.

The country I live in now is becoming more like the U.S. at the moment in the sense of starting to move toward home ownership as a growth asset. In the U.S., this has caused an insane housing shortage, and we're seeing that here too. We should be moving away from such policies, not toward them.

That's because if you get to 75 years old in the U.S. you are more likley to be reasonably well-off—if you aren't you are more likely die before that of preventable causes. And if you are well-off, that helps once you're past 75 as well. Of course plenty of poor people reach 75 in the U.S., but percentage-wise fewer do.

Also, the EU is a big place with lots of different countries. I live in the Netherlands, where you routinely see 80-year-olds riding bicycles. This is less common in other european countries, but that's changing. Your comparison would be more useful it if were by individual country rather than including the whole continent. I don't actually know if it's any different in the Netherlands—we have income inequality issues here too—but it would make sense if it were because people are so much more physically active here throughout life.

At this point a lot of economic growth is just generating garbage, but because we are so addicted to growth, we have to keep generating garbage.

A better question to ask would be, would you prefer to live in Europe or the U.S.? Not work, live. The idea that our lives should be work from graduation to grave doesn't really seem like something we ought to be valorizing—it sucks for the planet, and it sucks for us.

Reality check here: hybrid solar farming actually increases yields, because a lot of crops don't like direct sunlight all day.

I think what we do have evidence for is that you didn't read the paper, but I did, because it was interesting. From the paper:

Godel does no such thing. The incompleteness theorem says that some things can't be proven, and aren't computable, but every example of that *includes humans*. It's not a case that you can't build a computer and program in an axiomatic system that is consistent and can prove every statement with godel numbers, but that a human can prove a statement in that system that that computer can't prove. The human can't either. It's a statement about the limits of axiomatic mathematical systems.

There's no evidence anything in human thought falls under the realm of uncomputability. In fact, given that the brain is made up of neurons that are guaranteed to fire or not fire given specific conditions, electrical and chemical, there's plenty of evidence that it *must* be computable and algorithmic.

That is a terrible system, I'm surprised they do it that way. Banks are usually better about that. The only times I got a call from my bank that required me to prove who I was, it was either a returned call, and they mentioned the subject and that I had called, before they started verifying my identity, so I knew it was legit. Or the fraud alert people, and they could easily verify that they were who they said they were, because they asked about specific purchase attempts with the amount and location before they tried to verify my identity.

I did get one *actual* phishing call decades ago that made me absolutely crack up. The person on the other line said they were from "the bank." They didn't say which bank, just "the bank." Usually I immediately hang up on phishing, but that one made me want to engage a bit: I asked "which bank" and he answered, "your bank." At that point I just burst out laughing and the gig was up, so I hung up.

I had an instance of a work e-mail years ago, that was sent from a third-party contractor, that had so many red flags for very obvious phishing (including coming from outside the organization, wtf).

Where I work, we have a place to forward phishing emails so that IT can review it. I forwarded it there, and apparently so many other people did that a follow-up email had to be sent out that said, "we thank everyone for pointing out this e-mail as phishing, but we can confirm it's actually legit."

I think they learned the lesson from that, because it has not happened since that we got such a terrible email. I think my point is that overtraining may not work, but having a place to report phishing is a great idea. It only takes one person to report it, and then the IT department sends out a massive e-mail to warn everyone else about it, so it doesn't rely on them recognizing it (and anyone that already fell victim to it can report that they have, so action can be taken to minimize the damage). And in cases like you and I experienced, they can also do the opposite and confirm that it's real.

I agree, but go the other route for the comparison to humans and thought: people need to stop thinking that what we do when we "think" isn't algorithmic. Of course it is. We're not that special.

The models are trained on the same data, and they create their output based on the connections they made with all the previous data. When we ask it to generate "random" numbers, they're not any more random than when a human is asked to generate a random list of numbers. It's not purposefully encoding the information in the numbers because transmitting its love for owls is important to it, but the favorite animal information tokens are part of the seed made when it's generating those numbers.

Invariably, the second LLM that has been trained on the same data as the first will have the same data connections to those numbers. It's similar to how, when I was dating, I was filtering out anyone that added the information in the app that they had not been vaccinated for COVID. There's a *lot* of information associated with the type of person who was not only not vaccinated, but felt that they needed to state it. The information isn't contained in that assertion alone, but combined with the information already in my brain, it tells me a lot about their belief structure in things completely unrelated to vaccines and COVID. The LLM is doing that.

Yup. And TBH the shares haven't declined that much. $600b sounds like a lot of money, but Apple is trading at $200 ATM and peaked out at something like $230 or $240 when the market was hot before people realized Trump wasn't just talking shit about ruining the economy, but was actually going to do it. The fact that Meta is spending stupid money on AI at the moment ought to be frightening Meta shareholders, not Apple shareholders.

#clickbait

If some government is spending $20 million to investigate this, that seems like an insane misuse of taxpayer money.

BTW, the ascii glorp above is a unicode em-dash, proving that I am an LLM and hence not a disinterested party to the discussion. :)

At least that's what I heard skeeted on Bluesky: https://bsky.app/profile/danbi...

And of course this is no surpriseâ"an inconclusive study that can be used to justify a controversial headline is gold in the infotainment biz.

How would an LLM accurately determine which cases were "easy"? They don't reason, you know. What they do is useful and interesting, but it's essentially channeling: what is in its giant language model is the raw material, and the prompt is what starts the channeling. Because its dataset is so large, the channeling can be remarkably accurate, as long as the answer is already in some sense known and represented in the dataset.

But if it's not, then the answer is just going to be wrong. And even if it is, whether the answer comes out as something useful is chancy, because what it's doing is not synthesis—it's prediction based on a dataset. This can look a lot like synthesis, but it's really not.