“employers and taxpayers” is like “job creators”.

They don’t want to say “the ultra wealthy” because that would be too accurate for the owner of the site, who is an ultra wealthy person.

Really? Maybe I have been missing them my whole life.

Yup, but if you try to tell the youth of today that, they’d never believe you

Worms/Scorched Earth/Liero (pixel physics!)

That era had so many good Gorilla clones.

I watched it at 11, spent half the movie thinking that I had half a medieval movie on the VCR tape (👴) because it didn’t make any sense.

At the black knight scene I got the joke and rewatched the whole thing several times a month for a while.

I’m not dead extinct yet!

This looks like malicious compliance to me.

They were probably given a list of things that the parade had to have and they went down the list. Marching in formation (doesn’t say in step anywhere), check. Tanks (from 1980), check. Soldiers with drones (from Best Buy), check. Music, check.

It could be a distro if enough of us download it

Is nothing sacred?

Unless the person is use math terms elsewhere, I always assume people mean ‘unexpected’ then they say random.

It’s not random in the sense of a uniform distribution which is what is implied by “generate a random [phone] number”.

Yeah, true.

There, I was speaking more to the top level comment’s statement that an LLM cannot generate random numbers. Random numbers are pretty core to how chatbots work… which is what I assumed they meant instead of the literal language model.

You could say that they’re technically correct in that the actual model only produces a deterministic output vector for any given input. Randomness is added in the implementation of the chatbot software through the design choice of having the software treat the language model’s softmax’d output as a distribution from which it randomly chooses the next token.

But, I’m assuming that the person isn’t actually making that kind of distinction because of the second sentence that they wrote.

https://worldbuilding.stackexchange.com/questions/167049/assuming-a-flat-world-and-no-obstacles-how-far-could-you-see

Basically, you could see for a long way but your eyeballs suck so it largely doesn’t matter. Even with the best telescope and optics on a perfect day you will be limited by the gasses in the atmosphere which scatter light.

Also, Barad-dûr was destroyed when Frodo threw the One Ring into the fires of Mount Doom so it wouldn’t be there.

Yes, and some fine work as been put into making it a great choice for a webserver:

https://totl.net/Spud/

Because they were told that Trump would protect them against all of the bad things that Trump’s backers were blasting into their brains 24/7 via social media and partisan ‘news’ organizations.

Ignorance and disinformation did way more work that Trump’s charisma which is, as you’ve said, lacking

Yup, that’s the death groan.

It’ll run for a few more weeks and eventually it’ll start making that sound all the time and then the motor will freeze up and then magic smoke will signal that the electricity demons have exited its earthly form.

No way, tokens are almost always sub-word length.

Using larger tokens means that you need way more tokens to represent data and so encoders always learn to use short tokens unless they’re specifically forced not to.

Just to put it in perspective. Imagine that you were trying to come up with a system for writing down every phone number. The easiest system would be to have a vocabulary of 10 items (digits), with such a vocabulary you can write down all phone numbers. While storing entire phone numbers as a single ‘word’ would require a vocabulary of 10 billion items in order to write down all phone numbers.

That’s why encoders learn to use the smallest token sizes possible.

LLMs can’t generate random numbers, but the process of selecting the next token involves selecting a random (using a pseudorandom number generator) next token from the distribution of possible next tokens. The ‘Temperature’ setting alters how closely the random selection is to the distribution in the vector describing the next token.

An extreme example would be, on one end of the Temperature scale it always chooses the highest probability next token (essentially what the person you’re responding to is thinking happens) and on the other end of the scale it completely ignores the distribution and chooses a completely random token. The middle range is basically ‘how much do I want the distribution to affect my choice?’

In the end, the choice of the next token is really random. What’s happening is that the LLM is predicting the distribution among all possible tokens so that the sentence fits into its model of how language works.

That’s great, it beats having to buy new hardware!

Psh, what do professors know

There is always two, a master and an apprentice

It may have survived, but I’m sure it had a fowl day

OP generated that note with AI

Just download this executable file from the Internet and run it as administrator.