I work on ML networks used for industrial control systems that characterize defects in the product and determine what to do about them. Not what normal people think of when they hear "AI", but it's not really much different from what LLMs are built on.

We've been selling these systems for a decade or so now.

Generative AI chat is incredibly useful for learning, especially tasks that require a lot of inline learning such as software engineering. I use generative AI chat constantly, because it is basically a Stack Overflow that is near instant with it's answers and can debug error messages extremely well.

I don't even have to leave my code editor if I install the extension in my IDE. And with an IDE extension it can be aware of the actual context of my codebase to the point where it can write code samples that reference my own methods and variables.

It's incredibly helpful. I've seen and used a lot of different learning techniques over the years. I started out learning coding from physical books. Then it was online bulletin boards and forums, and Google. But AI agents and chat have replaced almost all of that now. I rarely waste my time on Google anymore when I can get more relevant answers, faster, right in my IDE.

Sure there is the occasional hallucination, but its not that much worse than terrible answers on Stack Overflow, or junk blogs and outdated docs that Google surfaces because someone SEOed the hell out of them.

"Occasional hallucination" is doing a lot of heavy lifting here.

Perhaps if it was a research context sure, but for the most part I've found GPT4 to do a good job on small to medium size chunks of coding

Less hallucination than some of my students in their presentations

Honestly, I have found LLMs to be much inferior to my usual methods for this sort of use case. So much so that I've stopped using them.

I am fascinated by how different people have such different experiences with these systems. A study into what the difference between the "best thing since sliced bread" camp and the "meh" camp is would be very interesting.

>I keep seeing this argument that there is value out there

I am sure that every person with significant hearing problems greatly appreciates auto generated subtitles. Surely some people enjoy having access to voice commands. Having translation tools which are reasonably good at inferring context is a great help if you want to communicate with a person who has no shared language. Having the ability to do some automated screening for abnormality can definitely help in manufacturing, same for medical imaging where a computer might point out to a doctor that something warrants a second look can be helpful. Cars being able to detect pedestrians and cyclists, surely has saved many lives already.

I could go on, but this is what I mean with people conflating "chat bots", with the entire range of applications for neutral networks.

Neural networks are currently the best way for a computer to infer human like knowledge about the real world. To make distinctions and to detect things which might be hard for a human to detect.

Clinical machine learning for cancer diagnosis is one promising area

https://www.cell.com/cell/pdf/S0092-8674(23)00094-6.pdf

Any problem that has a large range of inputs and a large range of outputs with many possible permutations inbetween.

Yes, you could use specialised algoritms but why not let the AI design such thing? It might be better than you.

Machine translation is valuable and it’s all neural these days.

Protein folding. Drug candidate discovery.

You must be kidding, ALL algorithmic trading uses ML models, the VAST MAJORITY of money being made and traded is on the back of ML right now. Are there really people in tech today that don't understand how much ML has taken over?

Lol, you might be talking about chatgpt specifically, which is kinda dumb.

Fancy statistics is nothing new.

A solution in search of a problem