None of the things you mentioned are even close to AI. They’re applied statistics, and they mostly use techniques we’ve known about for decades but have only now found a use case because computing and storage is cheap enough to make them viable.
There was a time, not all that long ago, when SVMs, Bayesian networks, and perceptrons were considered AI. That's behind the spam filters, predictive keyboards, and most of the search signals.
There was a time, a bit longer ago, when beam search and A* were considered AI. That's behind the game opponents.
As the linked Wikipedia article says, "AI is whatever we don't know how to do yet." There will be a time (rapidly approaching) where deep learning and robotics are common knowledge among skilled software engineers, and we won't consider them AI either. We'll find something else to call AI then, maybe consciousness or creativity or something.
As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.
And
I left the arena to pursue a more challenging task: reasoning with cause and effect. Many of my AI colleagues are still occupied with uncertainty. There are circles of research that continue to work on diagnosis without worrying about the causal aspects of the problem.
I don't see how this follows from this:
the 'AI' tools we use are increasingly good function approximators
Nothing in the definition of AI says that AI has to work the same way the human brain does... and as far as that goes, we're probably not 100% sure that, in the end, the brain is anything more than a really good function approximator and some applied statistics.
I would say the canonical definition of AI, to the extent that there is one, is roughly something like "making computers do things that previously only humans could do". If people think "AI is bullshit" I'd say it's because they're applying their own definition to the term, where there definition imposes much more stringent requirements.
Yeah, maybe the marketing people said that..
Also, because something has been around for decades does not make it not AI. For ex the cheque OCR mentioned probably runs off (or can feasibly run off) of a neural network. I think the parent's comment holds well - not sure about the last line though ...
> when you say AI you don't mean AI as is practiced by most of academia and the industry but the vision of Artificial General Intelligence (AGI).
What I actually mean is people practicing what they call "AI" in academia and the industry have co-opted the name to make what they do sound more interesting. First it was called "statistics". Then it was called "pattern matching". Then it was called "machine learning". Now it's called "AI". But it hasn't changed meaningfully through any iteration of these labels.
To your point, I agree. They hype around the area has evolved much faster than the area itself.
FWIW, "AI" as a field has been around since the 1950's. So calling something "AI" in no way implies that the techniques are especially new.
then what is 'close to AI'?