Where I live (Kamloops, BC, Canada) the all-time high temperature record for a long time was 42C, set in July 1941. Most of southern B.C. set records that month. No air conditioning. Ugh! We demolished that record when it hit 47C in June 2021. I've never been so hot in my life...

The hottest we've been so far this summer was 36C. I expect to hit the Big Four Oh at least once, but the long-term forecast isn't promising.

...laura

A hybrid is the answer for many people. They work well. Electric continues to improve but is still too much of a niche product.

I recently bought a new car. If I could lay my hands on something like a RAV4 Hybrid I'd be all over it. Good luck. I bought a VW Taos instead.

...laura

Of course people want to live in large cities; I for example live in a city of about 200,000 people but would absolutely love to live in New York or Boston or another large city. You are taking your own personal preference and erroneously assuming it applies to everyone else. As for economic productivity per a capita, you are wrong. The difference is large. One standard estimate says that doubling city size results in an increase in productivity of 3 to 8%. See https://www.sciencedirect.com/science/article/abs/pii/S1574008004800063. That means that a city going from 200,000 people to 800,000 has increased in productivity by somewhere between about 7 and 19 percent. But when we compare the 200,000 person city to the 8 million person city, that's around 5 doublings, which means an increase in productivity by around 15 to 40 percent. Now, there are some other estimates with some being lower and others higher. See https://www.journals.uchicago.edu/doi/10.1086/675534 and https://www.sciencedirect.com/science/article/abs/pii/S0166046208001269. Complicating factors, there's some evidence that the productivity increase to cities in the US is not as large as it was historically which is likely due to stricter zoning, building codes, and general red tape making efficient use of dense space harder. See e.g. https://www.vox.com/2014/7/15/5901041/nimbys-are-costing-the-us-economy-billions.

This is an even worse idea. More people mean more ideas, more new inventions, more economies of scale, and more comparative advantage. We all benefit from a big population.

Large cities are a good thing. People want to live in them because they are so massively economically productive. And large dense cities are also better for the environment. The larger cities are the lower their CO2 per a capita with New York being a really good example https://advisorsmith.com/data/most-sustainable-cities/ and this is true with other metrics of environmental pollution also, like run-off, habitat destruction, NOx pollution, and many others. Large cities are one of the best things humans have ever made, and destroying them is the exact opposite of what we should be doing.

My job doesn't have much to do with this at all. All humans engage in motivated reasoning and other cognitive biases. But it is also very easy to think someone one disagrees with is engaging in some sort of cognitive error even when they are not. So instead of just labeling this as motivated reasoning, maybe you could explain what it is wrong with the point I made?

Not just a question of legality, but of ethics. Jimmy Carter had to give up his peanut farm out of a concern of conflicts of interest. Obama also had a policy of turning away even gift books from authors that were sent to the Whitehouse when we was President. George W. Bush had a policy almost as strict as Obama's. How far we've come from that point.

The current AI systems have some definite use cases, but right now outside some very narrow areas (such as some customer service oriented jobs and some of the more basic programming jobs), the efficiency increases are too small to reasonably justify reducing headcounts based on them. Seems like a good way of identifying areas where management is on a hype-train which can cause real damage to the companies and the quality of their services.

Pages of instruction are not the only thing that matters. Lots of humans don't learn well from simply reading instruction sets. And since ChatGPT doesn't have a good visual representation of the board, this is equivalent to trying to teach a human who has never learned to play chess to learn to play without a visual board and only able to keep track of moves based on the move notation. Even some strong chess players have trouble playing chess in their heads this way.

Obnoxious snark aside, it appears that you are missing the point. Yes, ChatGPT is trained on a large fraction of the internet. That's why it can do this at all. What is impressive is that it can do that even without the sort of specialized training you envision. Also, speaking as someone who has actually taught people how to play chess, you are to be blunt substantially overestimating how fast people learn.

You shouldn't be surprised that it will try. All of the major LLMs are wildly overconfident in their abilities. I'm not sure if this is more because they've got human reinforcement to be "helpful" or if because they are trained on the internet where there's very rarely a response in the training data of "That's an interesting question, I've got no idea."

Hmm? No. I'm a mathematician. Instead of ad hominem attacks maybe try to address the actual points?

That LLM AIs are bad at abstract reasoning of this sort is not a new thing. People have seen that very early on with these systems, such as their inability to prove theorems. If someone thought that an LLM would be good at chess by itself in this situation they haven't been paying attention.

ChatGPT is not a chess engine. Comparing it to an actual chess system is missing the point. The thing that's impressive about systems like ChatGPT is not that they are better than specialized programs, or that it is better than expert humans, but that it is often much better at many tasks than a random human. I'm reasonably confident that if you asked a random person off the street to play chess this way, they'd likely have a similar performance. And it shouldn't be that surprising, since the actual set of text-based training data that corresponds to a lot of legal chess games is going to be a small fraction of the training data, and since nearly identical chess positions can have radically different outcomes, this is precisely the sort of thing that an LLM is bad at (they are really bad at abstract math for similar reasons). This also has a clickbait element given that substantially better LLM AIs than ChatGPT are now out there, including GPT 4o and Claude. Overall, this comes across as people just moving the goalposts while not recognizing how these systems keep getting better and better.

It is worth noting even the easiest puzzles here are puzzles which many, if not most humans, cannot solve. The fact that we're now evaluating AI reasoning based on puzzles above human baseline should itself be pretty alarming. But instead we've moved the goalposts and so are reassuring ourselves that the AIs cannot easily solve genuinely tricky puzzles.

Every single ride is more data for them to work with and more money to do research with. They are already expanding in those cities to areas which are more complicated. Snow and rain are going to be more issues, but even with rain now, the Waymo cars can often run. 50 years is likely a substantial overestimate; 20 or 25 years seems more plausible. (That said, I guessed 15 years ago that by now the majority of new cars would be self-driving. So I may be systematically overestimating how fast this tech is going.)