mrtranscendence

That doesn't work for library projects, though.

> Your local plumber is going to want a funny action movie trailer slash plumbing advertisement to advertise their services. They wouldn't have even been in the market before.

And why would your local plumber hire someone to produce this funny action trailer (which I'm not convinced would actually help them from an advertising perspective), when they can simply have an AI produce that action funny action trailer without hiring anyone? Assuming models improve sufficiently that will become trivially possible.

> Independent filmmakers will be making their own Miyazaki and Spielberg epics that cater to the most niche of audiences - no more mass market Marvel that has to satisfy everybody, you're going to see fictional fantasy biopic reimaginings of Grace Hopper fighting the vampire Nazis.

Well, first of all, if the audience is "the most niche of audiences", then I'm not sure how that's going to lead to a sustainable career. And again -- if I want to see my niche historical fantasy interests come to life in a movie about Grace Hopper fighting vampire Nazis, why will I need a filmmaker to create this for me when I can simply prompt an AI myself? "Give me a fun action movie that incorporates famous computer scientists fighting Nazis. Make it 1.5 hours long, and give it a comedic tone."

I think you're fundamentally overvaluing what humans will be able to provide in an era where creating content is very cheap and very easy.

> "We're profitable on inference. If we didn't pay for training, we'd be a very profitable company."

That's OpenAI (though I'd be curious if that statement holds for subscriptions as opposed to API use). What about the downstream companies that use OpenAI models? I'm not sure the picture is as rosy for them.

Waar heb je het over? "Welgelegen Buitenrust Nooitgedacht Rustenburg" is volkomen cromulent Engels.

For what it's worth, I do use AI for language learning, though I'm not sure it's the best idea. Primarily for helping translate German news articles into English and making vocabulary flashcards; it's usually clear when the AI has lost the plot and I can correct the translation by hand. Of course, if issues were more subtle then I probably wouldn't catch them ...

> the iPhone was only "in charge" for a brief year or two, and then Android ate its lunch in terms of marketshare.

This is true worldwide, but there are significant regions where iOS quite handily beats Android (such as the US, Japan, and even some parts of Europe).

Oh man, I played the shit out of the Lone Wolf books when I was a kid in the eighties and nineties. My brother had the first couple of them, and I still have his old books somewhere with his notes. I should find them for nostalgia's sake (my brother having long since passed).

> Character stats, names, details about players - those are inputs, and structured ones at that.

Some details about players are structured and can be easily stored and referenced. Some aren't. Consider a character who, through emergent gameplay, develops a slight bias against kobolds; who's going to pick up on that and store it in a database (and at what point)? What if a player extemporaneously gives a monologue about their grief at losing a parent? Will the entire story be stored? Will it be processed into structured chunks to be referenced later? Will the LLM just shove "lost a father" into a database?

Given current limitations I don't see how you design a system that won't forget important details, particularly across many sessions.

You sound like a fun guy.

You seem to readily agree that my use case is inappropriate for LLMs, but not ToucanLoucan?

We're not allowed to record meetings at work, with some exceptions such as trainings. I'm not at all sure why, but I believe it has to do with merger plans. We're not working on anything particularly shady, to my knowledge.

Notes are often taken but not always. Depends on the people in the meeting.

I suspect you're overstating the degree to which an LLM might be unsuitable for some types of work. For example, I'm a data scientist who works primarily in the field of sales forecasting. I've found that LLMs are quite poor at this task, frequently providing answers that are inappropriate, misleading, or simply not a good fit for the data we're working with. In general I've found very limited use in engaging LLMs in discussion about my work.

I don't think I'm calling myself a super special snowflake here. These models are just ... bad at sales forecasting.

LLMs aren't entirely useless for me. I'll use ChatGPT to generate code to make plots. That's helpful.

If I'm not conscious, it's rather difficult to explain what I'm experiencing right now.

Prove what point? There have clearly been crooked or underhanded companies that achieved success. Microsoft in its early heyday, for example. The fact that they paid a price for it doesn't obviate the fact that they still managed to become one of the biggest companies in history by market cap despite their bad behavior. Heck, what about Donald Trump? Hardly anyone in business has their crookedness as extensively documented as Trump and he has decent odds of being a two-term US President.

What about the guy who repaired my TV once, where it worked for literally a single day, and then he 100% ghosted me? What was I supposed to do, try to get him canceled online? Seems like being a little shady didn't manage to do him any harm.

It's not clear to me whether it's usually worth it to be underhanded, but it happens frequently enough that I'm not sure the cost is all that high.