Yeah I have no idea what these people are talking about. The current gen of AI is qualitiatively different than previous attempts. For one, GPT et al are already useful without any kind of special prompting.
I'd also like to challenge people to actually consider how often humans are correct. In my experience, it's actually very rare to find a human that speaks factually correctly. Many professionals, including doctors (!), will happily and confidently deliver factually incorrect lies that sound correct. Even after obvious correction they will continue to spout them. Think how long it takes to correct basic myths that have established themselves in the culture. And we expect these models, which are just getting off the ground, to do better? The claim is they process information more similarly to how humans do. If that's true, then the fact they hallucinate is honestly a point in their favor. Because... in my experience, they hallucinate exactly the way I expect humans to.
Please try it, ask a few experts something and I guarantee you that further investigation into the topic will reveal that one or more of them are flat out incorrect.
Humans often simply ignore this and go based on what we believe to be correct. A lot of people do it silently. Those who don't are often labeled know-it-alls.
Yon don't ask a neurosurgeon how to build an house, just like you don't ask a plane pilot how to drill a tunnel. Expertise is localized. And the most important thing is that humans learn.
> And the most important thing is that humans learn.
Implementation detail that will be solved as the price of AI training decreases. Right now only inference is feasible at scale. Transformers are excellent here since they show great promise at 'one shot' learning meaning they can be 'trained' for the same cost as inference. Hence the sudden boom in AI . We finally have a taste of what could be should we be able to not only inference but also train models at scale.
Humans learn from seeing I don't think we are the stage of training models with videos / images dataset. We only reached the plateau with text dataset to train with.
I'd also like to challenge people to actually consider how often humans are correct. In my experience, it's actually very rare to find a human that speaks factually correctly. Many professionals, including doctors (!), will happily and confidently deliver factually incorrect lies that sound correct. Even after obvious correction they will continue to spout them. Think how long it takes to correct basic myths that have established themselves in the culture. And we expect these models, which are just getting off the ground, to do better? The claim is they process information more similarly to how humans do. If that's true, then the fact they hallucinate is honestly a point in their favor. Because... in my experience, they hallucinate exactly the way I expect humans to.
Please try it, ask a few experts something and I guarantee you that further investigation into the topic will reveal that one or more of them are flat out incorrect.
Humans often simply ignore this and go based on what we believe to be correct. A lot of people do it silently. Those who don't are often labeled know-it-alls.