this post was submitted on 10 Apr 2024
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What I think is amazing about LLMs is that they are smart enough to be tricked. You can't talk your way around a password prompt. You either know the password or you don't.
But LLMs have enough of something intelligence-like that a moderately clever human can talk them into doing pretty much anything.
That's a wild advancement in artificial intelligence. Something that a human can trick, with nothing more than natural language!
Now... Whether you ought to hand control of your platform over to a mathematical average of internet dialog... That's another question.
I don't want to spam this link but seriously watch this 3blue1brown video on how text transformers work. You're right on that last part, but its a far fetch from an intelligence. Just a very intelligent use of statistical methods. But its precisely that reason that reason it can be "convinced", because parameters restraining its output have to be weighed into the model, so its just a statistic that will fail.
Im not intending to downplay the significance of GPTs, but we need to baseline the hype around them before we can discuss where AI goes next, and what it can mean for people. Also far before we use it for any secure services, because we've already seen what can happen
Oh, for sure. I focused on ML in college. My first job was actually coding self-driving vehicles for open-pit copper mining operations! (I taught gigantic earth tillers to execute 3-point turns.)
I'm not in that space anymore, but I do get how LLMs work. Philosophically, I'm inclined to believe that the statistical model encoded in an LLM does model a sort of intelligence. Certainly not consciousness - LLMs don't have any mechanism I'd accept as agency or any sort of internal "mind" state. But I also think that the common description of "supercharged autocorrect" is overreductive. Useful as rhetorical counter to the hype cycle, but just as misleading in its own way.
I've been playing with chatbots of varying complexity since the 1990s. LLMs are frankly a quantum leap forward. Even GPT-2 was pretty much useless compared to modern models.
All that said... All these models are trained on the best - but mostly worst - data the world has to offer... And if you average a handful of textbooks with an internet-full of self-confident blowhards (like me) - it's not too surprising that today's LLMs are all... kinda mid compared to an actual human.
But if you compare the performance of an LLM to the state of the art in natural language comprehension and response... It's not even close. Going from a suite of single-focus programs, each using keyword recognition and word stem-based parsing to guess what the user wants (Try asking Alexa to "Play 'Records' by Weezer" sometime - it can't because of the keyword collision), to a single program that can respond intelligibly to pretty much any statement, with a limited - but nonzero - chance of getting things right...
This tech is raw and not really production ready, but I'm using a few LLMs in different contexts as assistants... And they work great.
Even though LLMs are not a good replacement for actual human skill - they're fucking awesome. 😅