Someone recently suggested to me that AI systems bring the users' ability closer to the average. I was intrigued by this idea because it reflects my experience. I am, for example, terrible at any kind of visual art, but with something like Stable Diffusion I can produce things that are merely quite bad, whereas without it I can produce things that are absolutely terrible. Conversely, with GitHub Copilot I can write code with more bugs that's harder to read. Watching non-programmers use it and ChatGPT with Python, they can produce fairly mediocre code that mostly works.
I suppose it shouldn't surprise anyone that a machine that's trained to produce output from a statistical model built from a load of examples would tend towards the mean.
An unflattering interpretation of this would suggest that the people who are most excited by AI in any given field are the people with the least talent in that field.
Generative AIs are designed to answer prompts giving the most likely answer to them if you put it in Internet. They don't even try to be right (they don't even know what they are talking about), they try to be credible.
There are two ways to do so:
* Copying a real answer a real person gave to that question, and retouch it minimally in order to fit. That kind of answers are usually great, but nothing you can get easily using standard Google search.
* Faking it and use a wording that makes it look like it makes sense. They are really great at it. So much, that debunking them can require a lot of expertise and effort. So if you are not an expert or you are in a bad day, you will take that bullshit as the perfect answer.
The most amazing thing about it is not even the AI has the slightest idea of which of the two paths it has taken, because the algorithm for both is exactly the same.
@jgg @david_chisnall that is to say, *by definition* bullshit.