Big difference? Do you seriously think that the entire field of artificial intelligence research produced nothing usable until today? For as long as there were AI algorithms there were also attempts to commercialize them (with varying success). The problem wasn't that all of those algorithms turned out to be useless, but rather the mismatch between hype and reality.
You should look into the history of expert systems for a classic example. With things being as they are, deep neural networks might very well go though the same cycle, which is not a good thing for the AI field in the long run.
Caution and skepticism are not the enemies of research and engineering. Hype is.
Is there a mismatch between the hype and the reality for the people funding this stuff? Certainly there's a mismatch for the general public, but they don't matter.
This is exactly why I said you should look into their history. They took off big time. Search Amazon for "expert systems" and observe how many books there are about the subject.
They were used by large corporations, hospitals, universities and governments. They were the subject of a lot of research. There were countless startups based around the concept.
> AT&T just sent me a letter saying they're giving me an extra 5GB/month bonus data as long as I keep my plan, which I assume happened because their data indicated I was a risk for downgrading or switching carriers.
This isn't Artificial Intelligence though. These seem like they fall under 'Reactive systems' or 'Data Analytics'.
In any case, it doesn't matter what you call it. Current research is producing results that can improve these systems, which means that research is worth a lot of money.
Hmm, I've not heard of this happening. Do you have any examples of it?
> In any case, it doesn't matter what you call it. Current research is producing results that can improve these systems, which means that research is worth a lot of money.
I don't see how this really adds to or is relevant to my comment, I was simply restating and adding to hitekker's point that AI is improperly named.
For example, automakers are shipping all sorts of fancy driver assistance systems, and a lot of that is driven by fancy image recognition and deep learning systems.
Looking beyond, there's the constantly-discussed self-driving car. It's not here yet, and it's going to take a ton of work, but at this point it looks like there's nothing fundamental standing in the way. It's no longer a question of whether it will happen, but whether it's happening in five, ten, or fifteen years.
Outside of transportation, businesses are applying AI techniques to analytics to squeeze ever more money out of their client base. There's the infamous story about how Target knew a teenage girl was pregnant before her father did. AT&T just sent me a letter saying they're giving me an extra 5GB/month bonus data as long as I keep my plan, which I assume happened because their data indicated I was a risk for downgrading or switching carriers. Improving these systems is worth a ton of money, and as far as I understand it fits really well with where current research advancements are being made.
As long as there are clear, immediate, profitable uses for AI advancements, there shouldn't be any worry of another winter. I guess the question is whether current advancements will peter out eventually, or whether we've reached a point where more research will always produce more immediate dividends. It sure feels like the latter to me, but I could easily be wrong.