It's basically a year to a year and a half off people's life expectancies, from the heat alone.

Although this is not trivial, the antivaxxer movement will likely chop 10-15 years off life expectancies and greatly reduce quality of life for much of the remainder, same again for the expected massive reduction in air quality that will result from modern political movements, and the absurd puritanical movement in the US will likely chop another 10-15 years off the life expectancies of women.

These are, therefore, substantially more significant, although politically impossible to deal with right now.

I fully expect that, if current trends prevail, by 2040, life expectancies will resemble those of the Neolithic and Bronze Ages.

Attending work for 2 days means I pay £190 per week to work, with no recompense from the company. Because there's a decent amount of holiday time, my wages have only dropped £9000 per year from last year. If I needed to attend 5 days a week, I would have to leave the only job that I have ever held that actually made any functional effort to handle my disabilities. In other words, if I lost this job, I would not be capable of functionally working in any job at all, simply because most companes don't give a damn about disabilities. Legally, however, I would be deemed "capable of work". As such, I would have no wages and no benefits. Once my money ran out, I'd be on the streets. There is simply no viable alternative.

If a business guy thinks adding to the homeless is the best way to improve work morale, then maybe he's not a business guy that holds any opinion of value. He may well be listened to, which will cause a LOT of problems for a LOT of people and WILL increase unemployent and, in countries with failing industry, increase the homelessness of people who are far more competent than him, but that does not make his opinion valuable, merely incredibly stupid and sickeningly naive.

If you buy Intel, you finance American fascism.

Don't buy Intel.

Without knowing anything else about Threads users, I know one thing: they're people who don't mind using a Zuckerberg product and being subjected to intense corporate surveillance by Meta.

That tells me everything I need to know about them.

but now it's more eco-friendly shit.

You still can't use it as a fertilizer though. So it's still not useful for anything...

are hurting democracy, world peace, science, human progress and ecology.

Everything is derivative (or just plain copying), though generative doesn't even imply novelty. I can generate a list of the first X positive integers and that will still be generative as long as I didn't specify each one literally. It will not be novel.

I mean, the same is true of many things that are automated. Every once in a while someone dies on an escalator, but far fewer people die from riding an escalator than by falling down stairs.

I'm curious what specific problems have been "solved" by particular generative AI startups. I'm aware of AI-based tools for specific tasks, but nothing that uses LLMs to solve a problem (other than perhaps code generation).

It is literally a summarization model. Its context window is the text to summarize. It was literally trained on being given text as a context, and summaries as outputs.

Good! Considering the mess they'll probably make of whatever their next effort will be, I'm all for it.

Microsoft was the perfect company to push AI: it's always produced shit products, so AI couldn't make them any worse.

I've never had to post my resume on fucken LinkedIn. If there's one thing I truly detest, it's unprinciped Big Data sonsabitches like Microsoft making money out of people's need to make a living.

LinkedIn, Office365, Teams... All those tools are basically forced on professional who need to find or keep a job, and Microsoft essentially abuses people's data against their will.

AI Overview literally tells you what sources it's drawing on. I don't see why people are capable of looking at what the sources are when they're Google search answers but not when they're AI Overview source links, but apparently, that is hard for some people.

This. Most people inevitably respond in these threads talking about "the model's training". AI Overview isn't like something like ChatGPT. It's a minuscule summarization model. It's not tasked to "know" anything - it's only tasked to sum up what the top search results say. In the case of the "glue on pizza" thing, one of the top search results was an old Reddit thread where a troll advised that. AI overview literally tells you what links it's drawing on.

Don't get me wrong, there's still many reasons why AI overview is a terrible idea.

1) It does nothing to assess for trolling. AI models absolutely can do that, they just have not.
2) It does nothing to assess for misinfo. AI models absolutely can do that, they just have not.
3) It does nothing to assess for scams. AI models absolutely can do that, they just have not.

And the reason the have not is that they need to run AI Overview hundreds of thousands of times per second, so they want the most absolutely barebones lightweight model imaginable. You could run their model on a cell phone it's so small.

Bad information on the internet is the main source of errors, like 95% of them. But there are two other types of mistakes as well:

4) The model isn't reading web pages in the same way that humans see them, and this can lead to misinterpreted information. For example, perhaps when rendered, there's a headline "Rape charges filed against local man", and below it a photo of a press conference with a caption "District Attorney John Smith", and then below that an article about the charges without mentioning the man's name. The model might get fed: "Rape charges filed against local man District Attorney John Smith", and report John Smith as a sex offender.

5) The model might well just screw up in its summarization. It is, after all, as miniscule as possible.

I personally find deploying a model with these weaknesses to be a fundamentally stupid idea. You *have* to assess sources, you *can't* have a nontrivial error rate in summarizations, etc. Otherwise you're just creating annoyance and net harm. But it's also important for people to understand what the errors actually are. None of these errors have anything to do with "what's in the model's training data". The model's training data is just random pieces of text followed by summaries of said text.