I disagree with you because I've learned to follow the money.

The phrase was coined in the 1976 film "All the President's Men", which by the way is pretty great if you're into things like that. You do have to have an attention span longer than the time between a SOCK and a POW in a Marvel movie, though.

You are welcome to catch up to the wisdom of over four decades ago.

Twitter only needed to show a profit for one year, and they only showed a profit for one year. What a remarkable coincidence!

You should kill whoever wrote that comment, then.

Quite possible, but Microsoft stole and included Stacker and that also caused quite a lot of data loss since it also sucked.

It actually made sense on the Mac, because programs were officially called "applications" and even had the type code APPL.

So DOS 4 is Vista, and DOS 5 is Windows 7... oh how history repeats

Creating the training dataset is the *last* step. I have dozens of TB of raw data which I use to create training datasets that are only a few GB in size. Of which I'll have a large number sitting around at any point in time.

Take a translation task. I start with several hundred gigs of raw data. This inflates to a couple terabytes after I preprocess it into indexed matching pair datasets (for example, if you have an article that's published in N different languages, it becomes (N * N-1) language pairs - so, say, UN, World Bank, EU, etc multilingual document sets greatly inflate). I may have a couple different versions of this preprocessed data sitting around at any point in time. But once I have my indexed matching pair datasets, I'll weighted-sample only a relatively small subset of it - stressing higher-quality data over lower quality and trying to ensure a desired mix of languages.

But what I do is nothing compared to what these companies do. They're working with common crawl. It grows at a rate of 200-300 TB per month. But the vast majority of that isn't going to go into their dataset. It's going to be markup. Inapplicable file types. Duplicates. Junk. On and on. You have to whittle it down to the things that are actually relevant. And in your various processing stages you'll have significant duplication. Indeed, even the raw training files... I don't know about them, but I'm used to working with jsons, and that adds overhead on its own. Then during training there's various duplications created for the various processing stages - tokenization, patching with flash attention, and whatnot.

You also use a lot of disk space for your models. It's not just every version of the foundation you train (and your backups thereof) - and remember that enterprise models are hundreds of billions to trillions of FP16 parameters in their raw states - but especially the finetune. You can make a finetune in like a day or so; these can really add up.

Certainly disk space isn't as big of a cost as your GPUs and power. But it is a meaningful cost. As a hobbyist I use a RAID of 6 20TB drives and one of 2 4TB SSDs. But that's peanuts compared to what people working with common crawl and having hundreds of employees each working on their own training projects will be eating up in an enterprise environment.

Taiwan reinforces its silicon shield.

This is all to produce a peak of 240k EVs per year. Production "starts" in 2028. It takes years for a factory to hit full production. Let's be generous and say 2030.

Honda sold 1,3 million vehicles in the US alone last year - let alone all of North America, including both Canada and Mexico. If all those EVs were just for the US it'd be 18% of their sales, but for all of North America, significantly less.

In short, Honda thinks that in 2030 only maybe 1/7th to 1/8th of its North American sales will be EVs. This is a very pessimistic game plan.

I'm talking about, if I may invoke fiction vaguely, a machine made in the likeness of the human mind. I'm talking about what separates us from software, and how for some jobs it isn't much at all. I'm talking about how slavery never ended and the wealthy would like to replace all of us with very small shell scripts. And to them, that's actually viable. They don't understand any of the reasons why it isn't; even the ones that are that smart aren't that educated in that way. If they were, they couldn't do what they do simply because they could see it's unsustainable.

That's very much my point. They are already doing a shit job because it was all that was available. Now what are they supposed to get, a shittier job? It's hard to find one that pays, as backwards as that is.

I'm not new, friend.

Please please please write a screed explaining how forcing Apple to use stuff that users want them to use and support things users want them to support with absolutely zero harm to other users who simply don't turn on any additional features which will be disabled by default is bad for consumers. And don't cheat and use AI, really lean into it.

Most people process fairly visually, some people especially so. You see the same set of logos presented over and over again alongside the logos you are already predisposed towards and they are associated in your mind.

Maybe there's actually an advantage to aphantasia...

If you cannot detect when things are changing then you are doomed to be confused by changes. Welcome to the rest of your life, where changes are going to keep coming faster and faster, like they have been doing for all of history.

If you had a brain, you'd know.