It’s so human how - instead of admitting its error - it’s pulling this bs right out of its ass 🤣
🤔 I wonder what the hell it is that’s so scary about admitting they’re wrong to other people.
Growing up in an environment where mistakes were unacceptable sets the stage. Our willingness and ability to understand that that’s fucked up and change our attitudes about mistakes takes more growth.
For some people it’s easier to dig in their heels and double down.
🤔🤔🤔 I guess I can empathize. People are always traumatized by whatever their parents tell them. What a shame.
“where?” comes across as confrontational, you made it scared :(
The future of information ladies and gentlemen
Wow it’s so realistic and smart and easy to use I can feel my knowledge being revolutionised
Tbf I’m sure this is an unpaid version of some online LLM, you can only expect so much lol.
When I use GPT3.5 for things like finding specific quotes from famous books, it’s excellent… but asking it to play chess gives you blatantly illegal moves. Then GPT4 kicks my ass in chess.
Large Lying Model. This could make politicians and executives obsolete!
More like large guessing models. They have no thought process, they just produce words.
They don’t even guess. Guessing would imply them understanding what you’re talking about. They only think about the language, not the concepts. It’s the practical embodiment of the Chinese room thought experiment. They generate a response based on the symbols, but not the ideas the symbols represent.
I’m equating probability with guessing here, but yes there is a nuanced difference.
:(
I think these models struggle with this because they don’t process text as individual characters, but rather as tokens that often contain parts of a word. So the model never sees the actual characters within a token, and can only infer the contents of a token from the training data itself if the training data contains more information about it. It can get it right, but this depends on how much it can infer from training data and context. It’s probably a bit like trying to infer what an English word sounds like when you’ve only heard 10% of the dictionary spoken aloud and knowing what it sounds like isn’t actually that important to you.
More info can be found here: https://platform.openai.com/tokenizer
Ok, so, tokenization of the words is why I get that I have seen tech nerds get so excited about a system that allows for being able to come up with synonyms for words that were auto-generated that have a basic ability to sometimes be correct by looking at the words before and after it…
But it’s such a shitty way to look up synonyms! Using the words on either side doesn’t mean you found a synonym just that you found another word that might work and it still has to use the full horsepower of ridiculously overpowered system.
Or you could have a lookup table that just reads the frickin word and has alternate synonyms predefined and it was able to run in word 97.
It’s ridiculous that we think this is better in any meaningful way instead of just wasteful development.
pregante moment
nnayonnaise
Kerning!
