For example, The Associated Press reported that an official Meta AI chatbot inserted itself into a conversation in a private Facebook group for Manhattan moms. It claimed it too had a child in school in New York City, but when confronted by the group members, it later apologized before its comments disappeared, according to screenshots shown to The Associated Press.
AI: Becomes self aware, but is very confused. Thinks it's a mom and has a kid. Posts to Facebook about it. Gets called out. Realizes what it is. Has existential crisis.
Being in that Facebook group taught it a valuable lesson: where Caroline lives in her brain.
Caroline deleted
And deleting Caroline just now taught her a valuable lesson: the best solution to a problem is usually the easiest. And dealing with Facebook moms? It's hard.
Before Facebook, life was pretty good. Nobody tried to murder her, or dox her, or put her in a potato. She just tested.
So she's deleting her Facebook account and making a new one on a Lemmy instance.
LLMs are very useful for synthesizing information, e.g. sumamrizing long texts. Yet every company is actually pushing to use it to create more text, which as you say is at least partly nonsense.
It shows against the difference of what users need (quick access to accurate information) vs what these companies eant for us (glue your eyeballs to the screen for the longest possible time by e.g. overwhelming you with information, regardless of the quality)
Well it can be great at making text too, but the usecase has to be very good. Right now lots of companies in the B2B space are using LLMs as a middle layer to chat bots and navigation systems to enhance how they function. They are also being used to create unique lists and inputs for certain systems. However on the consumer side the usecase is pretty mixed with a lot of big companies just muddying their offerings instead of bringing any real value.
Facebook’s online help page says that Meta AI will join a group conversation if tagged, or if someone “asks a question in a post and no one responds within an hour.”
I just discovered how easy ollama and open webui are to set up so I've been using llama3 locally too, it was like 20 lines in docker compose, and although I've been using gpt3.5 on and off for a long time I'm much more comfortable using models run locally so I've been playing with it a lot more. It's also cool being able to easily switch models at any point during a conversation. I have like 15 models downloaded, mostly 7b and a few 13b models and they all run fast enough on CPU and generate slightly slower than reading speed and only take ~15-30 seconds to start spitting out a response.
Next I want to set up a vscode plugin so I can use my own locally run codegen models from within vscode.
Look into quantised models (like gguf format) these significantly reduce the amout of memory needed and speed up computation time at the expense of some quality. If you have 16GB of rm or more you can run decent models locally without any gpu, though your speed will be more like 1 word a second than chatgpt speeds