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In your dead reply you say "Inconsistent and wrong answers does not negate the fact that it understands you.". If you ask a schoolkid to multiply 5x5 and they say 25 and then you ask them to multiply 300x10 and they say 310 you don't say "they understand multiplication and wrong or inconsistent answers can't convince me otherwise", you say "they memorised the twelve times table but don't understand multiplication". The other way that you say doesn't exist is the way it actually was built to work - it's trained to find repeated text patterns in its training data, and then substitute in other text into them using them as templates. Yes there is no Google result for "Bash script to put jokes in a file" but there are patterns of jokes, patterns of Bash scripts putting text into file and examples of filesystem behaviour. That it can identify those patterns and substitute them inside each other is what makes it work. You say "ping bbc.co.uk" it says "ping reply NN milliseconds where NN=14" because there are many blog posts showing many examples of pings to different addresses and it can pull out what's consistent between them and what changes. You say "{common prime number code}" it replies "{prime numbers}". Ask it to "write an APL program to split a string" and it says "{complete nonsense}"[1] because there aren't enough examples for it to have any idea what to do, and suddenly it's clear that it isn't understanding - it doesn't understand the goal of splitting a string or whether it's achieved the goal, it doesn't understand the APL functions and how they fit together so that it can solve for the goal, it can't emulate an APL interpreter and try things until it gets closer to its goal even though it has enough computing power that it potentially could, it can't pause for thought or ask someone else for help, it can't apologise for not knowing enough about APL to be able to answer because it doesn't know that it doesn't know. Your 17th Century man also couldn't write an APL line to split a string, but he would shrug his shoulders and say "sorry, can't help you". The internet it was trained on has a lot of Python/Docker/Prime numbers/Linux shell basics and a lot of Wikipedia / SEO blogspam because they are written about millions of times over, and they contain much less about APL. Pareidolia is the effect where humans see two blobs and a line and 'see' a face. People see a bag with buckles and folded cloth looks like a scowling face, our mirror neurons project a mind into the bag and we feel "the bag's feelings" and say the bag is angry, and laugh because it's a silly idea. When ChatGPT parrots some text which looks correct, we project an intelligence behind the text, when it parrots some text which is wrong we excuse it and forgive it, because that's what we do with other humans. A human who speaks Spanish from a phrasebook is stumped as soon as the conversation deviates from the book. Even if they have excellent pronounciation. A human who understands Spanish isn't. ChatGPT is a very big phrasebook. [1] https://i.imgur.com/7z4LB9W.png - plausible at a glance, the APL comment character is used correctly, so is variable assignment. But you can't split a string using catenate (,) and replicate-down (⌿) it has effectively done a Google search for APL and randomly shuffled the resulting words and glyphs into a programming style example. What the code does is throw a DOMAIN ERROR. You can say "there is understanding but it made a mistake", but it's the kind of mistake that makes me say "there is no understanding". |
I would say the child on a certain level understands the concept of multiplication by virtue of being able to calculate the complex answer despite his secondary answer being incorrect.
You're wrong. It is trained with text from the internet as an llm but on top of that it is also trained on good and bad answers. This is likely another layer on top of the LLM that is regulating output. Look it up if you don't believe me. There was an article about how openai outsourced a bunch of Kenyans to do training work.
The apl example doesn't prove your point imo. I'm not saying chatGPT understands everything. I'm saying it can understand many things. The thing with apl is that it has incorrect understanding of it due to limited data.
I don't think I'm biased. My opinion is so contrary to what's on this thread it's more likely your the one excessively projecting a lack of intelligence behind chatGPT. Pareidolia is you, because you're the one following the common trope. It takes extra thinking and an extra step to go beyond that.
It is true that chatGPT has a big phrasebook. However. It is also true that the example I mentioned is NOT from the phrasebook. It is obviously a composition of multiple entries in that phrasebook put together to form a unique construction. My claim is that there are a many number of ways that those phrases can be composed but chatGPT chose the right way because it has enough training data to understand how to compose them.
Clearly for apl it understands it incorrectly. The composition of an incorrect result means incorrect understanding and the composition of a highly improbable correct result means true understanding which is inline with what I am saying that both understanding and incorrect understanding can exist in parallel.