And we promise we won't spy on you. Pinky swear. No, you can't audit it in any way, you have to trust us. You trust Facebook, right?
But at least we now know just how much money your private data really is worth. You're gifting Facebook with 250 bucks every single year.
If that makes you feel like a sucker: That's because you are.
They're now trying to swindle the Nigerian princes out of their money?
Talk about trying to teach an eagle to fly....
STMicro support isn't the best. True. Still heaps above Espressif who'll just suddenly and for no valid reason simply pull the rug out under you and disable whole portions of their firmware because it would allow you to actually do with the hardware what you want to do instead of what they deem appropriate.
Certainly. But it's not a drop-in replacement. You can't just replace a Z80 with a STM32 and be done.
There's nontrivial costs associated with redesigning hardware, along with writing a new firmware. I highly doubt that anyone who designs anything new will reach for a Z80. Or has for the last 20 years.
But believe it or not, there are implementations that have gone unchanged for 30 and more years simply because what the hardware needs to do didn't change. So why design a new hardware?
It's probably dirt cheap and for a lot of applications "good enough".
Then they will have to find another place to produce their stuff.
With a bit of luck, eventually jobs will have to come back home because everyone else would want to steal the whole production.
Well, since I plan to neither go to China nor Russia but might have to go to the US, I guess that's the lesser evil.
Management working? Yeah, that's some far out shit, unheard of here.
... are we putting blatant pseudoscience on Slashdot? Are we becoming Natural News? Ir InfoWars maybe?
I've probably done tens of thousands of legit, constructive edits, but even I couldn't resist the temptation to prank it at one point. The article was on the sugar apple (Annona squamosa), and at the time, there was a big long list of the name of the fruit in different languages. I wrote that in Icelandic, the fruit was called "Hva[TH]er[TH]etta" (eth and thorn don't work on Slashdot), which means "What's that?", as in, "I've never seen that fruit before in my life"
In other words, even Indian programming wasn't bad enough to drive the bottom feeders away, let's try to put the quality limbo bar down a notch.
Hold on, they want management to work? And 6 days a week?
The difference is maybe that raping someone is illegal while selling your services to Israel isn't.
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"
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.
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.
UNIX was not designed to stop you from doing stupid things, because that would also stop you from doing clever things. -- Doug Gwyn
