Black Mirror creator unafraid of AI because it’s “boring”::Charlie Brooker doesn’t think AI is taking his job any time soon because it only produces trash
The best ones can literally write pretty good code, and explain any concept on the Internet to you that you ask them to. If you don’t understand a specific thing about their explanation, they can add onto their explanation, and they can respond in the style you want (explain as if I’m ten, explain as if I’m an undergrad, etc).
I use it literally every day for work in a somewhat niche field. I don’t really agree that it’s a “parlor trick”.
LLMs are awful for facts, because they don’t understand what facts are. You should never rely on them if you require factual correctness.
They are OK for text summation, formatting and just making shit up. For summation a human with experience still produces nicer output, because they understand the content and don’t just look at words. As for making shit up you will get the statistically most likely output, so it’s usually trite and boring. I think the progress is amazing, but there are still so many problems to be solved.
Right now I use them for boiler plate stuff, like writing a text with some parameters and then I polish it. For code I find them quite useless, because with an IDE I can write boiler plate just as fast as when I polish the prompts until the LLM delivers useful stuff. And with the IDE I don’t get references to methods or entire libraries that just don’t exist.
Right now I use them for boiler plate stuff, like writing a text with some parameters and then I polish it
It’s actually great for dnd to produce NPC dialogue or names on the fly. We also tried using it to calculate area of effect spells, ie “how many average sized humans in armor with swords could fit in a circle with a diameter of 30ft.” We were rolling with it before someone pointed out that it didn’t calculate the area of a circle correctly, however it got the rest more or less accurate. So we don’t use it for that anymore, and it’s funny how what often appears to be the simplest component of a question is the thing it most often gets wrong.
People are also kind of shit at facts. There are so many facts, and many of them aren’t practical for every person who needs to assess a fact’s accuracy to do so. But it isn’t structurally impossible to mimic how humans learn how to gauge truthfulness, we just have to be prepared for the idea that it will be bound by the limitations of language, as well as the risk inherent in trusting data that it has not independently verified.
I use LLMs for having things explained to me, too… but if you want to know how much salt to pour in that soup, try asking it about something niche and complicated you already know the answer to.
They can be useful in figuring out the correct terminology so that you can find the answer on your own, or for pointing some very very obvious mistakes in your understandings (but it will still miss most of them).
Please don’t use those things as answer machines.
I’m going to use those things as answer machines and you can’t stop me.
Jokes aside, I always validate what chatbots tell me, not even just important things. I use GPT-4 for work and 90% of the time it can show me how to use very specific functions in complex ways, but yesterday (for the first time in awhile) it made up a function that didn’t exist. To its credit, I said, “Are you sure about [function]?” and it said, “I’m sorry, I got confused. That function doesn’t exist. However, look into X, Y, Z for further resources” and I did and they were the correct things to look into.
No they can’t. Your phrasing is misleading. It’s a Chinese Room test output and nothing more. I had an Encarta CD that could do rudimentary version of this in 1995. That was more impressive, tbh.
