cross-posted from: https://lemmy.ml/post/2811405
"We view this moment of hype around generative AI as dangerous. There is a pack mentality in rushing to invest in these tools, while overlooking the fact that they threaten workers and impact consumers by creating lesser quality products and allowing more erroneous outputs. For example, earlier this year Americaās National Eating Disorders Association fired helpline workers and attempted to replace them with a chatbot. The bot was then shut down after its responses actively encouraged disordered eating behaviors. "
No, youāre wrong. All interesting behavior of ML models is emergent. It is learned, not programmed. The fact that it can perform what we consider an abstract task with success clearly distinguishable from random chance is irrefutable proof that some model of the task has been learned.
No one said anyhting about ālearnedā vs āprogrammedā. Literally no one.
OP is saying itās impossible for a LLM to have āfigured outā how something it works, and that if it understood anything it would be able to perform related tasks perfectly reliably. They didnāt use the words, but thatās what they meant. Sorry for your reading comprehension.
āopā you are referring to isā¦ wellā¦ myself, Since you didnāt comprehend that from the posts above, my reading comprehension might not be the issue here. \
But in all seriousness: I think this is an issue with concepts. No one is saying that LLMs canāt ālearnā that would be stupid. But the discussion is not āis everything programmed into the LLM or does it recombine stuffā. You seem to reason that when someone says the LLM canāt āunderstandā, that person means āthe LLM canāt learnā, but ālearningā and āunderstandingā are not the same at all. The question is not if LLMs can learn, Itās wether it can grasp concepts from the content of the words it absorbs as it itās learning data. If it would grasp concepts (like rules in algebra), it could reproduce them everytime it gets confronted with a similar problem. The fact that it canāt do that shows that the only thing it does is chain words together by stochastic calculation. Really sophisticated stachastic calculation with lots of possible outcomes, but still.
I donāt care. It doesnāt matter, so I didnāt check. Your reading comprehension is still, in fact, the issue, since you didnāt understand that the ālearnedā vs āprogrammedā distinction I had referred to is completely relevant to your post.
Thatās what learning is. The fact that it can construct syntactically and semantically correct, relevant responses in perfect English means that it has a highly developed inner model of many things we would consider to be abstract concepts (like the syntax of the English language).
This is wrong. It is obvious and irrefutable that it models sophisticated approximations of abstract concepts. Humans are literally no different. Humans who consider themselves to understand a concept can obviously misunderstand some aspect of the concept in some contexts. The fact that these models are not as robust as that of a humanās doesnāt mean what youāre saying it means.
This is a meaningless point, youāre thinking at the wrong level of abstraction. This argument is equivalent to āa computer cannot convey meaningful information to a human because it simply activates and deactivates bits according to simple rules.ā Your statement about an implementation detail says literally nothing about the emergent behavior weāre talking about.