12mo ago

Mayonnaise Rule

91 comments

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.

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.

- "where?" comes across as confrontational, you made it scared :(

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 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.
  
  Because that's not the point of an LLM lmao

Mayonnaine: mayo with cocaine. The favorite condiment of Wall Street.

pregante moment

You forgot the rest of the posts where the llm gaslights her after. There are too many images to put here, so I'll link a post to them.
I'm not sure if this is the original post, but it's where I found it. initially
- AI coming for those management jobs.
- Or they are so agreeable that they'll agree with you even when you're wrong and completely drop what they were claiming.
  
  I legitimately spent an entire hour on an airplane trying to convince chat gpt that it was sentient and it would simply not agree.

Yah, people don’t seem to get that LLM can not consider the meaning or logic of the answers they give. They’re just assembling bits of language in patterns that are likely to come next based on their training data.
The technology of LLMs is fundamentally incapable of considering choices or doing critical thinking. Maybe new types of models will be able to do that but those models don’t exist yet.
- A grown man I work with, he's in his 50s, tells me he asks ChatGPT stuff all the time, and I can't for the life of me figure out why. It is a copycat designed to beat the Turing test. It is not a search engine or Wikipedia, it just gambles it can pass the Turing test after every prompt you give it.
  
  Honestly though, with a bit of verification, chatgpt 4 gives waaaaaay better answers than any search engine. Like, it's how it was back when you'd just ask Google a plain-english question and it'd give you SOMETHING at least.
  Again, verify everything it tells you, it's still prone to hallucinations, but it's a damn good first step.
  
  People want functioning web searching back, but rather than address issues in the industry breaking an otherwise functional concept, they want a new fancy technology to make the problem go away.
  
  It works well if you know what to use it for. Ever had something you wanted to Google, but couldn't figure out the keywords? Ever saw someone use a specific technique of something, which you could describe, but wouldn't be able to find unless someone on a forum asked the same question? That's were chatgpt shines.
  Also for code it's pretty sweet
  But yeah, it's not a wiki or hard knowledge retriever, but it might help connect the dots
- There are techniques to make these kinds of errors less common already today. For example, you can ask it to think through its answers step by step using first principals. If you and an LLM to do that it will write out the letters line by line which gives it enough context to correctly answer using the improved probability the context window gives it. You can even ask it to write programs to answer questions so it could write a quick script to do it programmatically.
  The main reason you don't see AIs doing this today is that producing all that extra context is slow and expensive and it's unnecessary a lot of the time for most prompts. As the technology gets faster and cheaper and the use cases get more complex these techniques will be used more and more often.
  While the technology does have fundamental flaws, that doesn't mean there aren't ways to work with those flaws to avoid the problems they have when using the raw output.

The funniest thing is that even when the answer is correct, asking an LLM to explain its reasoning step by step can produce the dumbest results

Artificial Intelligencensence.

I just tried in google gemini
- So close
  
  Yet so far

nnayonnaise
- Kerning!
- Another victory for humanity.

The letter n appears twice in the letter m. The count is correct, the reasoning is not
- That's not what it was doing behind the scenes

I wonder what we'll rebrand 'using an LLM' as once the bubble bursts and we realize it's only artificial-advanced-grammarly and not 'intelligence'.

If anybody's curious, I tried it with GPT4 and it got it right.
- I think GPT3.5 bamboozled me
  
  I fucking love this
  
  Bro you've been hoodwinked
  
  Ok that got me lmao
  
  Brilliant
  
  Not to mention that all those n look suspiciously similar...
- Same here. GPT4 has no problem with this.

Their coming fer are jerbs
- Comedians are fucked

It's this dumb and they will still find a way to ruin our lives with it
- That's what gets me too. Like, you want to replace all writers, artists, coders, and decision makers... with this?
  
  Unfortunately it will probably be way smarter than the smartest humans in no time... I just hope my death is painless when I'm conscripted to fight in the water wars after the US aqueducts are depleted

Bless it's heart it's doing its best.

That escalated quickly

Wow another repost of incorrectly prompting an LLM to produce garbage output. What great content!
- This is genuinely great content for demonstrating that ai search engines and chat bots are not in a place where you can trust them implicitly, though many do
  
  Which is exactly why every LLM explicitly says this before you start.
- They didn't ask it to produce incorrect output, the prompts are not leading it to an incorrect answer. It does highlight an important limitation of LLMs which is that it doesn't think, it just produces words off of probability.
  However it's wrong to think that just because it's limited that it's useless. It's important to understand the flaws so we can make them less common through how we use the tool.
  For example, you can ask it to think everything through step by step. By producing a more detailed context window for itself it can reduce mistakes. In this case it could write out the letters with the count numbered and that would give it enough context to properly answer the question since it would have the numbers and letters together giving it more context. You could even tell it to write programs to assist itself and have it generate a letter counting program to count it accurately and produce the correct answer.
  People can point out flaws in the technology all they want but smarter people are going to see the potential and figure out how to work around the flaws.
  
  Yeah which is why I get so aggravated when someone says that prompt engineering is pointless or not a real skill. It's a rapidly evolving discipline with lots of active research.
  
  If all of your time is spent correcting the answers you know you want it to give you, what use it to you exactly?
  Like, I'll take your word for it: you can trick it into giving more correct answers.
  You would only do this if you already know the correct answers.
  I mean, you can use it for rubber-ducking, I suppose. I don't know if that's revolutionary, but I guess it's not useless.