rossdavidh

I was recently introduced to the term "house-shy", for people who don't want anyone else seeing what the inside of their house looks like. I have a few friends who are "house-shy", that I for one reason or another have seen the houses of (often years after becoming friends with them). I think the percentage of Americans who are hoarders or borderline, is underestimated.

If anyone reading this has a job (plumber, A/C repair, etc.) that regularly gives them a glimpse of the inside of people's homes, I would be curious as to their thoughts on this.

"The Machine Stops"

Overthinking it. "You have money and need to seem AIish for the stock market, we want real money". You notice they don't do this deal in tokens, they do it in fiat currency.

One unexpected "benefit" of LLMs is that it is training us all to be even better at recognizing "you don't even know what you mean by that, do you?"

Less conspiratorially; they may care a lot less about it than ADO, and not give it the priority/resources they would otherwise. Neglect can often do the work of malign intention.

They don't lie, because they don't ever have an understanding of truth vs any other language that sounds good.

They don't cheat, because they can for example tell you the complete rules of chess, but don't know how to play chess without breaking those rules. They can recite rules, but they don't know what they are.

They don't steal, because they don't understand ownership.

In other words, they aren't intelligent. They're just algorithms. The flaw is in thinking that they think.

I've known people who did get inquiries from the IRS; they just had to provide a lot of evidence that they were actually going to markets to try to sell their stuff, actually making stuff to sell, etc. Generally, the IRS are more interested in people who make a bunch of income, then have a fake business to charge expenses against. Plus, there are many cosplaying businesses far too large to ever worry about the IRS accusing them of being a hobby.

This race was never going to get to AGI, at Google or anywhere else, and it appears that Google knows that.

A few points:

1) the simplest explanation whenever a bunch of people leave [x] at a company at the same time, is that [x] is becoming less important to that company moving forward. It's not proof, but you know, everyone is trying really hard to say that's not what's happening here. Sometimes the simplest explanation is correct.

2) rumor is that Sergey Brin, co-founder of Google, got bored during pandemic lockdown and eventually returned to Google in a lower profile role, related to AI. I have to think that he still has a lot of say in what goes on in Google relative to his interests.

3) Yahoo Finance article says Hassabis "has long prioritized research over profits", so his replacement might indicate that Google has decided it is time for this stuff to pay for itself?

FYI, the governor is Republican so you might be surprised/suspicious that he would be halting approvals. However, Gov Abbott had a political near-death experience a few years back involving an event called Snowpocalypse, in which the energy grid failed, so he might actually be motivated to make sure he's not put at risk of a repeat.

"The end result is a peculiar version of Hollywood: a place increasingly devoid of physical production, populated instead by executives, AI art, and unpaid interns drag-racing golf carts through empty studio streets..."

With the correct writer, that could be a cool indie movie, right there.

I mean, we all remember the cases where it was true, but do you really think most people think they are good at computer programming? Or speaking Russian? Or playing the harp? Or gardening? In the vast majority of cases, people who are not skilled at something, know that they are not. There are, sure, a few people who are overconfident, but the D-K effect as generally used in conversation was always pretty obviously untrue.

Not the least, I think! Appliances, computers, and especially clothing do have other manufacturers (some in Mexico, some in India). Semiconductors are among the most concentrated manufacturing industries, and also among the hardest to spin up. It takes some time to get an oven factory going, I'm sure, but not as long as a semiconductor fab, and the consequences of having mediocre ovens (or other appliances) are not nearly as great.

Of course, it is true that a lot more than chips will be impacted. And that it "will not be a good time", is certainly true.

'...when this all falls apart, I genuinely think there's going to be an authority crisis where people look at the media and go, "Hey, why did you believe every insane thing said by a CEO for four years?"'

So, some of the capital invested due to the AI boom may be for stuff like buildings, energy infrastructure, and other long-lasting stuff. I am reminded of all of the spending done in the runup to the Y2K, when I saw factories able to get all kinds of upgrades to equipment done, outside of the normal budgetary restraints, simply because the vendors were smart enough to say "not fixable, you'll have to buy the new model". It could happen.

However, GPUs and memory chips are some of the fastest-depreciating capital investments one can make. Overinvesting in this generation's GPU model will either be wasted (because the chips are worthless in a few years) or result in a lot of underinvestment in future years (because instead of replacing those GPUs with newer models you keep using them, unwilling to admit you bought several times as much as you should have).

If we have reached the point in the cycle where the boom's proponents are trying to argue that even if it was all a mistake, maybe it's ok, then one suspects were might be late in the boom part of the cycle.

Great stuff! Great! However, the issue is not, and has likely never been, that young people aren't willing to believe "they can do that". It's that most people, of necessity, are not good at both inventing, manufacturing (how to efficiently make that invention many times), and sales (how to get someone to buy it). Therefore, they will need to find someone willing to hire them to do one part of that process. And the modern labor market is not interested in training people on the job, they want someone who's already done the job somewhere else.

Not saying you shouldn't go tell kids about how things work! Just that the reason kids don't do that as much, is not primarily anything to do with education or the kids, but rather because the modern labor market has done its best to avoid ever having to give anybody a chance to do something they haven't already done many times before.

In various places and times in the past, otherwise rational and successful people had all sorts of odd religious (or other ideological) beliefs, that caused them to do odd things. Sometimes, those odd behaviors even had a modest negative impact on them. However, they signaled (perhaps more powerfully because somewhat costly) to those around them that they were on the "correct" side of things, that they had internalized the religion of their peers. This may have paid off for them, on balance, by making them more trusted or liked by other believers.

Not that it always turns out to be a net positive, but it can often enough to keep happening. Some niche religions have too costly demands on their members (e.g. chastity for all believers, giving away all possessions, etc.), and thus fail to survive. But others, and other belief systems that function psychologically in a similar way, can survive despite (or even because of) the modestly costly irrational behaviors. Costly signaling is a more honest testament to your commitment to the ideology.

No

Might be worth trying live yogurt in the meantime, per the article?

That is a general truth of most ML; many models _can_ find the information in the data, if the data is good enough. If it is not, then likely no model can.

Several instances of both. In at least one case they told us that they wanted to rent to a big-name tenant at a larger space in the same complex, before they started renting any of the smaller places. What seemed odd to me is that they would nonetheless put the property "on the market", i.e. online listing, even though if you called up they would say it's not for rent. I figured it had to be to claim that it was "available for rent" in some technical sense, but I didn't know why.

In others they would just have too high a price, and it is still empty six years later so they are clearly not unaware that they have it priced too high. Simple math showed they would be better off renting at a lower price than going years without anything, but it was quite common so it didn't seem like it could be simple stupidity.

The test, I guess, of this explanation is whether or not the flow of money out of private credit causes the end of "extend and pretend", and the market finally clears.

Oh my goodness, thank you. My wife had to move her store in 2020 in the midst of lockdown; you'd think rents would have been low, but no. Since then, many of the places that wouldn't lower the rent then, have sat empty ever since. This is in Austin, TX, a town that has had a healthy economy during that entire time.

Weirder still, many of them were on the market, theoretically for rent, but if you called them up it turned out they weren't actually available, and the landlord wasn't interested in renting them. I couldn't figure out why you would pretend something was for rent at $X, and let it sit empty for years, rather than actually rent it at something <$X. Now it makes sense.

It seems to me he contradicts his own thesis here: "The boring truth is that expertise in most subjects is largely a matter of having an enormous library of knowledge and skill. For example, if you want to learn a language, you need to learn a lot of words. Any method that tries to skip over the fact that there are tens of thousands of words to learn is doomed to failure. All skills are like this, it’s simply that the “atoms” of learning are usually less obvious than in languages."

...but we don't learn our first language, or any other, by first learning a few thousand words and only then speaking. We start using the very first words we learn, in real life situations, and add words as we need them. It's the real-world applicability and project-based method that he pronounces skepticism of elsewhere in the same piece.

Every coach of every sports team ever, knows that you need drills, but you also need to play actual games, to keep kids motivated to do those drills.

True that, but it could be both cause and effect. There are reports that gut bacteria can induce depression, which benefits the bacteria because it sends more calorie-rich, highly processed food down the gullet (think ice cream binges when depressed). Not hard to believe that gut bacteria optimized for a particular kind of food could evolve the ability to induce their host to eat that food exclusively. It would have the side-benefit (for the bacteria) of reducing competition from other microbes that aren't optimized for that, by starving them out. Things aren't always just cause or effect, especially in biology.

Working on a framework for factory management systems.

Do they mean, the moment when everyone realizes it's not as useful as they at first thought?

Should note that "top US markets" really means "most expensive US markets", not for example the largest by population.

It's somewhat akin to how documentation inside code is so often inaccurate; it may have been fine when it was written, but it doesn't get updated.

Sorry, didn't know/remember that a GR account was required just to read...

One of the authors of this paper, David Reich, has written a book called "Who We Are and How We Got Here", which is worth reading. My thoughts on it: https://www.goodreads.com/review/show/2605841954