The irony is that not one of these bills itself as AI. It's just "a product that works", and the company that produces it is happy to keep the details secret and let users enjoy the product. So you may be right that the term "AI" itself is pure salesmanship. When it starts to work it ceases to be AI.

https://en.wikipedia.org/wiki/AI_effect

Also - humans only look like we're fast at picking up new domains because we apply a helluva lot of transfer learning, and most "new" domains aren't actually that different from our previous experiences. Drop a human in an environment where their sensory input is truly novel - say, a sensory deprivation tank where all visual & auditory stimulation is random noise - and they will literally go insane. I've got a 5-month-old and a project where I'm attempting to use AI to parse webpages, and I will bet you that I can teach my computer to read the web before I can teach my kid to do so.

I think you are on to something, put differently: If you need to use the term "AI" to enhance the marketability of the product it is probably because the product sucks.

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'?

The most creative, intelligent and least frustrating "AI" I've ever encountered was in some games, such as Dota2 or many years ago F.E.A.R. They were frustrating but only due to unpredictability, even after hundreds of hours of playtime. YouTube and NetFlix AI after hundreds/thousands of hours invested are also very unpredictable and frustrating, but that's the opposite experience I am looking for in those situations.

Translate can be useful at times...like once a year when I want to comprehend a Japanese website, usually I close the tab after 2 minutes.

I used GMail for many years and still do to some degree but I'm moving to a different mail provider. GMail's spam filter is great!

Not sure, since 2 years it became acceptable to make no difference between ML and AI. ML appears smart because of bizillions of training samples and I feel very impressed when I hear of that. But yeah, at the end of the day it doesn't have exactly the biggest impact on me... ;)

YouTube recommendations aren't great, they have a short memory and my feed is rarely diverse, it just shows a bunch of whatever I just watched.

You severely underestimate the bandwidth of your eyes and ears and other senses, and the volume of your brain's memory (despite it's uber-loosy compression). That's terabytes a day probably, if not big data than I dunno what is. Yeah, 99% of it is thrown away at passing through the first few hundreds of layers of your neural networks, but they still know what to throw away...

To get a digital computer on "equal" terms with the zillions of hacky optimizations your semi-analog brain uses you need a shitton of raw power and data volume ("if you don't know what to throw away of the input data, you need to just sift through all/more of it") to compensate for the fact that you don't have N million years of evolution to devise similar hacky optimizations.

Also, humans work as a "network of agents", that's also recurrent (aka "culture"). Current sub-human-level AI agents are far from any sort of reliable interop.

My guess is that we'll get human level performance levels at AGI tasks when we learn to build swarms of AI-agents that cooperate well and "model each other", and few people are working on this... Heck, when it happens it will probably be an "accident" of some IoT optimizations thing, like, "oops, the worldwide network of XYZ industrial monitoring agents just reached sentience and human level intelligence bc it was the only way it could solve the energy-efficiency requirement goals it was tasked to optimize for" :)

This is so common there’s a term for it https://en.m.wikipedia.org/wiki/Moravec%27s_paradox

Sample size and record size are two different things.

Product interfaces usually offer simple features to users and the value proposition is easy to see. Effective use of machine learning is well hidden upstream in a bunch of unsexy preprocessing or heavy lifting to get to the interface. Not something you’d ever need to emphasize in marketing, except maybe at tech meetups or in recruiting materials, but not to the end consumer.

It just makes pop references to AI-powered products more egregious.

Evolution by natural selection is the OG genetic algorithm, and it's been "running" on billions of organisms in parallel for hundreds of millions of years. The intuition that we take for granted such as the abstract concept of a shape is all hard-coded in our brains from trial and error.

No. Siri is shit.