I use copilot on a daily basis for programming. It has made me much more productive and it’s a real pleasure to use it. Nothing overhyped about it.

Curious to see what it will bring for other domains, e.g. for dealing with emails.

I do agree that there’s a lot of filtering happening. Not a huge deal for more applications. Luckily you can run your own models that are not filtered. I can definitely see a future where you run your own models locally. Afaik Apple recently did some stuff around that.

Check out the Law of One for some funky spiritual stuff. There might be something in there that makes sense of this reality for you.

It knocked my shoes off

Definitely dead here.

Consumer mindset and thinking of people first and foremost is brought to you by capitalism.

We’re not consumers, we’re people. Social animals that enjoy living in groups. Owning shit isn’t something that makes us inherently happy. Just look at how other apes live. Just think about the things that make you happy.

Endless consumption is a blight. It’s killing our habitat. It’s killing us.

Correct, it’s not just regurgitating words, it’s predicting which token comes next. A token is sometimes a whole word, but for longer ones it’s part of a word (and some other rules that define how tokenization works).

How it knows which token comes next is why the current generation of LLMs is so impressive. It seems to have learned the rules the underpin our languages, to the point that it seems to even understand the content. It doesn’t just know the grammer rules (without anyone telling it, it just learned the patterns), it also knows which words belong to each other in which context.

It’s your prompt + some preset other context (e.g. that it is an OpenAI LLM) that creates that context. So being able to predict a token correctly is one part, the other is having a good context. This is why prompt engineering quickly became a thing. This is also why supporting bigger contexts is another thing (but a larger context requires way more processing power, so there’s a trade-off there).

It’s btw not just the trained model + context that gives you the output of ChatGPT. I’m pretty sure there are layers before and after, possibly using other ML models, that filter content or make it more fit for processing. This is why you can’t ask it how to make bombs, even though those recipes are in its training set and it very likely can create a recipe based on that.

Looks like it does: https://chat.openai.com/share/1b487711-c1be-468a-877b-98091449b55e

I asked it to translate ‘meeting agreements’ to Dutch and it came up with the word ‘bijeenkomstafspraken’, which is a valid but very uncommon Dutch word (I’m native Dutch and don’t think I’ve seen it before). If I throw it into google with quotes around it, the first page is results with ‘bijeenkomst afspraken’, where ‘afspraken’ is used as the past tense of ‘afspreken’ (to agree) instead of as its noun (agreements).

It btw also suggested ‘vergaderafspraken’ as a translation, which is a way more common word.

ChatGPT speaks other languages. It’s actually a really good translator.

I just asked it to describe an organization using UK English and it indeed used ‘organisation’ instead (didn’t check for other words).

When smart watches came about I thought it would be cool to be able to look at your watch when a notification comes in. Never ended up buying one and when I see how some people behave, I’m glad I never did. Some people will just glance mid conversation at their smart watch, which imo is just as bad as grabbing your phone mid conversation.

I’m happy with my cheap Casio. Looks heaps better too.

I work in IT and we have truly self steering teams without formal hierarchy. Not everyone is used to it and IT people can be interesting characters, so it’s quite a process to get the team to perform well.

I see some tourists wear one in Amsterdam. Very rarely a local, of which I always assume they’re ill themselves or easily get ill (immunocompromised and what have you).