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rainsfordtoday at 12:18 AM2 repliesview on HN

I have generally moved from bearish to bullish on the future of current AI technology, but the continued inaccuracy with basic facts all while the models significantly improve continues to give me significant pause.

As an example, creating recipes with Claude Opus based on flavor profiles and preferences feels magical, right up until the point at which it can't accurately convert between tablespoons and teaspoons. It's like the point in the movie where a character is acting nearly right but something is a bit off and then it turns out they're a zombie and going to try to eat your brain. This note taking example feels similar. It nearly works in some pretty impressive ways and then fails at the important details in a way that something able to do the things AI can allegedly do really shouldn't.

It's these failures that make me more and more convinced that while current generation AI can do some pretty cool things if you manage it right, we're not actually on the right track to achieve real intelligence. The persistence of these incredibly basic failure modes even as models advance makes it fairly obvious that continued advancement isn't going to actually address those problems.


Replies

Brian_K_Whitetoday at 12:51 AM

I hate to help provide possible soultions to an entire process I don't approve of, but maybe the fuzzy tools need old style deterministic tools the same way and for the same reasons we do.

So instead of an LLM trying to answer a math or reason question by finding a statistical match with other similar groups of words it found on 4chan and the all in podcast and a terrible recipe for soup written by a terrible cook, it can use a calculator when it needs a calculator answer.

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themafiatoday at 12:39 AM

> we're not actually on the right track to achieve real intelligence.

Real intelligence means you have to say "I don't know" when you don't know, or ask for help, or even just saying you refuse to help with the subtext being you don't want to appear stupid.

The models could ostensibly do this when it has low confidence in it's own results but they don't. What I don't know if it's because it would be very computationally difficult or it would harm the reputation of the companies charging a good sum to use them.

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