Stop Calling Everything AI, Machine-Learning Pioneer Says

I think the technical side of the industry has known this all along. AI is a dream to pursue.

The business/marketing side of the industry has doubled down on the term. Many industries outside have adopted it as a way to make their boring widget sound new and interesting.

I bought a TV with built in AI recently. It’s just a TV to me. I’m sure it has some algorithms but that word is old and does not sound premium anymore.

Whenever I see an AI startup, I mostly am expecting its really just some guy doing things that don’t scale, like manning a chat bot or something.

'AI' as a term has been used by people in the industry for decades even by early computer science pioneers reffering to incredibly simple applications - it was hollywood that appropriated the term to the likes of skynet - not the other way around.

The description of the Turing test certainly alludes to a test of machine intelligence. So to some degree it is appropriate for AI to have a large umbrella.

My problem with the endless battle to define AI is that there are a hopelessly clueless cohort of people with money that chase the term like its their golden goose, and therefore anybody that wants funding needs to market their thing as AI.

And I think a lot of researchers cringe at the wanton use of AI because it devalues the work that they're doing. I just give it the shoulder shrug of approval - "I get it, you need funding too". And from that perspective I really wish that tree-based ML methods and logic programming languages and rule engines were still called AI, because they're really cool but horribly neglected because theyre not the latest thing.

My background is in automation and robotics; I studied system identification: a discipline where you would use mathematical means to identify a dynamic system model by observing input/output.

You treat the system as a black box and estimate a set of parameters that can describe it (e.g., Kalman filter).

I struggle to understand what's the fundamental difference between system identification and ML/AI. Anyone?

You ultimately have a bunch of data and try to estimate/fit a model that can describe a particular behavior.

It all comes down to a big optimization/interpolation problem. Isn't what they call "Learning" just really "estimating" ?

Then the more CPU/memory/storage you have, the more parameters/data you can estimate/process, the more accurate and sophisticated the model can be.

> We have "big data". We have (brittle and rather poor) "pattern recognition". We have (very limited) "simulate a conversation".

Yes, yes, yes, exactly.

> We have NO artificial intelligent systems, for any sensible definition of "intelligent". None. ZERO.

Yes, though -- what are some of your sensible definitions of intelligence?

> But we do not even know what "intelligence" really means!

...oh. I mean, you're not wrong, we don't know. But then how can you argue that AI isn't "intelligent"?

What if human "intelligence" really just is pattern recognition too? With maybe some theory of mind, executive function, and "reasoning" -- Mike complains machines can't do thinking in the sense of "high level" reasoning, though one could argue they just don't have enough training data here.

And everything else is emergent?

Then we're maybe not as super far off.

I'm reminded of the Arthur C. Clarke quote [0]:

> If an elderly but distinguished scientist says that something is possible, he is almost certainly right; but if he says that it is impossible, he is very probably wrong.

[0] https://www.scientificamerican.com/article/remembering-sir-a...