In this instance, I think the protesters were fools and they should have been fired and escorted out immediately.

However, "just shut up and be grateful you have work" is not a good attitude. That's how you get widespread abused of people who don't happen to be CEOs. And eventually violence.

Not just off, but on, sideways and upside down. They'll cater to more or less anything these days.

It's all one big Uniparty

It is not.

There is not as much difference as I'd like, but to conflate the two is frankly stupid. Just because one is bad does not imply the other is not much much worse.

I have no love for Starmer, I think he's a cowardly shitheel, and yet he (and his party) is infinitely preferable to the narcissistic, incmpetent, crony capitalist and frankly mendacious lot currently in power. Yes I would like to see an opposition with some actual ideas rather than a less corrupt version of the same shit which didn't work for the last 12 years, but that's still better than the same shit with a massive dose of incompetence and funneling money to billionaire donors.

It's utterly bonkers to suggest the two are equivalent.

war is expensive, but the blue-branch is getting in on that with their Ukraine fantasy

I have literally no idea what you are talking about. I don't know what your Ukraine fantasy is and given you're probably American and speaking on a subthread about British politics, I don't know if you mean red/blue as left/right (UK) or right/left (US).

People who don't understand hyperbole should be strung up in the town square, publicly eviscerated, and garlands hung of their entrails.

They're saying it's a shitty company, OK? Are they not allowed to say that because Thames Water is LITERALLY a shitty company? And only companies spewing actual non metaphorical shit can be shitty?

Also taking on some equivalent of "staving children in North Korea have it worse" is just lame. We all know that. No one's saying that google is equivalent to slave labour or some shit.

Wondering if you inveterately work from home because you appear to have forgotten how normal conversation works.

The difference is maybe that raping someone is illegal while selling your services to Israel isn't.

Everyone who publishes that data is just providing free evidence. Why would they do that? Who knows, but this is smarter, and only slightly less scrupulous.

They charge so much that it's literally cheaper to have a new part soldered to the board. They're fostering an industry dedicated to bypassing their ridiculous design decisions.

It took no skill, because you made no point. You just threw up and called it a meal.

The author has no clue what they're talking about:

Meta said the 15 trillion tokens on which its trained came from "publicly available sources." Which sources? Meta told The Verge that it didn't include Meta user data, but didn't give much more in the way of specifics. It did mention that it includes AI-generated data, or synthetic data: "we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3." There are plenty of known issues with synthetic or AI-created data, foremost of which is that it can exacerbate existing issues with AI, because it's liable to spit out a more concentrated version of any garbage it is ingesting.

1) *Quality classifiers* are not themselves training data. Think of it as a second program that you run on your training data before training your model, to look over the data and decide how useful it looks and thus how much to emphasize it in the training, or whether or not to just omit it.

2): Synthetic training data *very much* can be helpful, in a number of different ways.

A) It can diversify existing data. E.g., instead of just a sentence "I was on vacation in Morocco and I got some hummus", maybe you generate different versions of the same sentence ("I was traveling in Rome and ordered some pasta" ,"I went on a trip to Germany and had some sausage", etc), to deemphasize the specifics (Morocco, hummus, etc) and focus on the generalization. One example can turn into millions, thus rendering rote memorization during training impossible.

B) It allows for programmatic filtration stages. Let's say that you're training a model to extract quotes from text. You task a LLM with creating training examples for your quote-extracting LLM (synthetic data). But you don't just blindly trust the outputs - first you do a text match to see if what it quoted is actually in the text and whether it's word-for-word right. Maybe you do a fuzzy match, and if it just got a word or two off, you correct it to the exact match, or whatnot. But the key is: you can postprocess the outputs to do sanity checks on it, and since those programmatic steps are deterministic, you can guarantee that the training data meets certain characteristics..

C) It allows for the discovery of further interrelationships. Indeed, this is a key thing that we as humans do - learning from things we've already learned by thinking about them iteratively. If a model learned "The blue whale is a mammal" and it learned "All mammals feed their young with milk", a synthetic generation might include "Blue whales are mammals, and like all mammals, feed their young with milk" . The new model now directly learns that blue whales feed their young with milk, and might chain new deductions off *that*.

D) It's not only synthetic data that can contain errors, but non-synthetic data as well. The internet is awash in wrong things; a random thing on the internet is competing with a model that's been trained on reems of data and has high quality / authoritative data boosted and garbage filtered out. "Things being wrong in the training data" in the training data is normal, expected, and fine, so long as the overall picture is accurate. If there's 1000 training samples that say that Mars is the fourth planet from the sun, and one that says says that the fourth planet from the sun is Joseph Stalin, it's not going to decide that the fourth planet is Stalin - it's going to answer "Mars".

Indeed, the most common examples I see of "AI being wrong" that people share virally on the internet are actually RAG (Retrieval Augmented Generation), where it's tasked with basically googling things and then summing up the results - and the "wrong content" is actually things that humans wrote on the internet.

That's not that you should rely only generated data when building a generalist model (it's fine for a specialist). There may be specific details that the generating model never learned, or got wrong, or new information that's been discovered since then; you always want an influx of fresh data.

3): You don't just randomly guess whether a given training methodology (such as synthetic data, which I'll reiterate, Meta did not say that they used - although they might have) is having a negative impact. Models are assessed with a whole slew of evaluation metrics to assess how good and accurately they respond to different queries. And LLaMA 3 scores superbly, relative to model size.

I'm not super-excited about LLaMA 3 simply because I hate the license - but there's zero disputing that it's an impressive series of models.

I couldn't possibly do worse than Thatcher.

Depends. Is your name Liz Truss or David Cameron?

Pithy quotes prove nothing and often hide deception.

Margaret Thatcher said that while more or less giving away a ton of publicly owned stuff to indirectly buy votes. We now have a massive housing and water quality crisis (among other things) thanks to the conservatives running out of other people's money.

Your "often" was predicated on you cherry picking a small subset in order to make a point.

Under your (directly contradicting their words) theory, then creative endeavour on the front end SHOULD count If the person writes a veritable short-story as the prompt, then that SHOULD count. It does not. Because according to the copyright office, while user controls the general theme, they do not control the specific details.

"Instead, these prompts function more like instructions to a commissioned artist—they identify what the prompter wishes to have depicted, but the machine determines how those instructions are implemented in its output."

if a user instructs a text-generating technology to “write a poem about copyright law in the style of William Shakespeare,” she can expect the system to generate text that is recognizable as a poem, mentions copyright, and resembles Shakespeare's style.[29] But the technology will decide the rhyming pattern, the words in each line, and the structure of the text

It is the fact that the user does not control the specific details, only the overall concept, that (according to them) that makes it uncopyrightable.