Corn Woman 🌽 @WomanCorn@schelling.pt

It would be nice if there was a reliable framework for judging changes made to art in order to sell better in the marketplace.

What's reasonable and what's bad. What about decisions made based on marketplace concerns at the time of creation vs in revision.

"The Early Internet Era" aka when you first got access to a computer in a place where your parents couldn't look over your shoulder at any moment.

@flengyel I use a TOTP app.

But if you know what TOTP is, you're already ahead of the person who needs to hear the message that SMS 2FA is not good.

For people just entering, SMS 2-factor authentication is only one flavor of 2-factor authentication, and one known to be less secure.

If you have the option, you should use a 2-factor app, of which there are many.

@hrefna my understanding is that some telcos had bots just constantly 2fa-ing so they could collect the SMS fees.

@niplav mine or theirs?

(No for both, generally. With an exception for cute blush.)

@niplav I'm not sure how much of the magic of LLM is that the input and output are both text.

If we can get something that learns from videos, they may be more value in that.

I expect that the text -> art bots will have similar limitations, but probably decoupled from the text -> text ones.

Today on the discussion board:

It's very important not to misgender... Ungoliant, spider-demon who plunged Valinor into darkness by destroying the two trees.

Ungoliant's pronouns are she/her.

LLMs are not sentient, and are not people, but behaving towards them in a way that it would be bad to behave towards people is probably bad for you.

Training yourself to be cruel is bad for you.

Reminder that you shouldn't listen to me about anything. I'm a dilettante and my knowledge is a mile wide an an inch deep.

In 30 years, LLMs will be used for short text generation in products that aren't considered to be AI anymore.

We won't ever hit Peak Parameters, because a new paradigm will appear and draw people away from LLMs before we do.

We will reach a point of diminishing returns on increasing parameters within the next 20 years, where the cost of hardware to increase parameter counts isn't worth the increase in value you get from the model.

We will reach Peak Training Data in the next five years, where you can't improve the model by feeding it more training data because you're already using everything worth using.

Because the babble problem isn't solved, people will learn not to trust the output of an LLM. Simple, raw factual errors will be caught often enough to keep people on their toes.

It will put cheap copywriters out of a job, but will never be good enough for research.

The babble problem will not be solved. Effectively ever. It cannot be solved without a major change in architecture.

Large Language Model Predictions: