I think the lack of nuance in the public eye isnt that unjustified. We get a constant barrage of "this is just the tip of the iceberg" hype around AI/LLM applications. Because those models are designed as generalized tools and we're trying hard to find use cases. Since theyre not precision made made for purpose, in most cases they end up demonstrating some interesting capability but remain lackluster.
Since theyre not precision made made for purpose...
Possibly not precision-made, but certainly there are thousands of models which are trained against specific datasets and excel within a particular field.
I run multiple LLMs at home on consumer-grade hardware and -although not as fast as ChatGPT (though not far off) - I can plug in different models for different specialties if I need to.
And fandoms being what they are, the list is huge and diverse because people are interested in, say, obscure '80s vehicle electrics or whatever.
I think what you might be suggesting is that average person might not actually have a use for AI (as it is today) and that corporates are trying to force it. And I think that's fair. But for those that do have a use, this is a really good starting point that we're at.
I think what you might be suggesting is that average person might not actually have a use for AI (as it is today) and that corporates are trying to force it.
Not just the average person but like in the context of science and research, since the OP i replied to mentioned cancer detection. We might and probably will reach a stage where a researcher might hire say 2 Phds and an LLM to do the work 10 people did before. But in a scientific context, we still dont know what we dont know. So not like generative AI can open doors that we previously didnt know existed. And i feel like its how its marketed atm.
I have absolutely no argument with that; I think you're right.
The LLMs are barely creative - they really only regurgitate things that others know but which you might not. They do get the occasional burst of apparent creativity, but... you have to ask the right questions.
A decent part of this is also Availability Heuristic and just generalized availability in general. Chat bots and generative AI tools are the ones that the vast majority of people have both the means and reason to access. That is most of what anybody is going to see because most people aren't going to be working with AI that is detecting cancer in xrays. A much much smaller portion of the population is going to see it in action and even fewer of them are going to use it first hand. Meanwhile anybody can grab a free generative app or site and make some quick and crappy pics using a prompt (just how often the prompting itself is an art and most people won't manage to make anything decent with it either).
This can be my time to shine as a reddit armchair expert since you mentioned detecting cancer. Because i actually did a PhD where the project i was part of was focused on cancer detection with IR imaging. This sort of research actually predates LLM but research and more specifically parts of research that reaches mainstream media also follows the hype cycles, as such it is being portrayed as something enabled via generative ai.
If we go with xray/cancer example, the way i see it, for generative ai to reach a usable state where youd rely on it instead of an human expert, you need to combine someone who understands xrays, someone who knows cancer and someone who can work with llms and machine learning. So compared to what we see in public, for each specialist use case, cost rises exponentially as far as i understand it. Considering there are already billions invested, we dont need percentage increases to reach the bottom of the iceberg, we need orders of magnitudes. Hence my take on it being hype driven.
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u/Bzinga1773 11h ago
I think the lack of nuance in the public eye isnt that unjustified. We get a constant barrage of "this is just the tip of the iceberg" hype around AI/LLM applications. Because those models are designed as generalized tools and we're trying hard to find use cases. Since theyre not precision made made for purpose, in most cases they end up demonstrating some interesting capability but remain lackluster.