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How is this article on nautil.us ? Did the author just read the wikipedia.com page on Machine Learning ?

There is an entire field in ML called unsupervised learning. Labelling data that do not have labels attached to them.

Its not a Fundamental (uggh) limit, I am not sure if the author even knows what Fundamental means, its not like the halting problem, or heat death of the universe.

ML is also a very young field with poor mathematical understanding, optimism is the best way forward. Christopher Columbus didn't discover an entire NEW WORLD because he had a pesimistic attitude towards his ideas.

Its going to take time, but historially, we are rapidly learning about how the human brain and intelligence works - similar to how we rapidly learnt a lot of physics in the 20th century.

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In popular discussion, Deep Learning has unfortunately become synonymous with Machine Learning, which also, as of late, has itself become synonymous with AI.

Now, this might sound facetious, but to help avert another AI winter (maybe 10 years down the road), we need to be loud and vocal, educating at the very least the investing strata of society as to hyponymy/hypernymy relationships between these terms.

It is bound to happen when researchers uses words like "Machine" and "Learning" to describe their field. Why not use words that actually describe it, like function approximation.

Because you could say learning is function approximation for the public.

1. Just because someone decided to use the words "intelligence" and "neural" when describing a class of statistical clustering algorithms often based on backpropagation of errors doesn't mean these algorithms have anything to do with the brain or intelligence, and if they do, the relationship is not necessarily direct and immediate. Speaking about the two as if the connection is clear only muddles our understanding. It's important to remember that these terms are meant to capture the imagination, not to describe scientific knowledge.

2. Even if there are proven limitations to those algorithms, and even if those algorithms are related to human intelligence, so what? We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others. Obviously, the "intelligence algorithm" (whatever that means) has some serious limitations, and is not so good at some things. For example, human intelligence doesn't seem helpful in approximating solutions to computationally hard problems. It is obvious that intelligence (or any algorithm) has its limitations.

3. Understanding the limits of a field and having optimism are two separate things. A few good impossibility (or infeasibility) theorems help serve as a map, so you can be optimistic while knowing a bit more about your surroundings, rather than being optimistic while fumbling in the dark.

We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others.

Now wait a second. I would say humans are better at solving some problems directly and computers programmed by humans are better at solving other problems.

However, a computer with a single, fixed program alone will choke completely at some problems and humans are far more robust at finding a solution or at least "dealing" with any problem whatsoever one throws at them.

In the end, you're right that we don't know what intelligence is. And so just about any description is going to be somewhat tautological but "general problem solving ability" seems relatively less tautological than other concepts - ie, "general problem solving ability" seems about right for some value of "general" which we'll have to determine as we go along.

> a computer with a single, fixed program alone

I'm not sure what you mean by a "fixed program". Is a statistical clustering algorithm not a "fixed program"? Even the human brain is running some "fixed program", as we cannot reprogram our brains to efficiently run general sorting algorithms on "bare neurons".

> "general problem solving ability" seems about right for some value of "general"

It may be more useful than nothing at all, but I don't see how it conveys too much information. Clearly, a universal Turing machine has a "general problem solving ability" for much more general values of "general". So I'm not sure how much information "general problem solving" conveys. More importantly, why do we even need a definition? Statistical learning is very effective at solving some useful problems. Why do we need to philosophize about the relationship between those algorithms and human intelligence before we have more data?

Because it would be interesting to solve a wider class of problems. And having a deeper insight helps define which data to look at.

Having said that - I'm always bemused that AI is supposed to compete with a hypothetical single human brain, and that the Turing Test is supposed to prove we have AI when we can build a brain that appears human.

Learning is a social activity, not an individual one. Individual brains are useless without social training and access to culture.

And some humans are actually quite stupid.

A machine that passes the Turning Test for a hypothetical middle-of-the-bell curve human isn't so interesting a thing.

I wonder if trying to emulate"human intelligence" is the way to go. What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems?

If I had to build an AI, I would like to "train it" by pitching it against other AIs. Imagine an open AI network where bots will learn by challenging each others. Does such a thing exist?

> What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems?

Until we know what intelligence is, I'm not sure we could classify something as being distinct from human intelligence. If AI is any computational model that can learn (i.e., adapt its parameters as a response to inputs to solve various problems), then evolution would qualify as well (well, maybe it is).

But currently, "AI" is a marketing or science-fiction term, not anything we define even remotely rigorously. If we use the Turing test as a definition to AI (i.e., intelligence is the quality of an agent that is indistinguishable from a person through communication), we are not much closer today than we were forty years ago.

Science fiction can inspire science, but using sci-fi terms as if they were scientific terms is confusing, and I think we should similarly leave marketing terms to marketers (over the past decades, marketers have assigned the name AI to very different algorithms solving very different problems).

> where bots will learn by challenging

Many such things exist and have existed for years, just google it.

Also google AGI (artificial general intelligence) or see the videos I linked to on youtube in this thread.

Really the questions you are asking are sort of interesting but it indicates that you are completely unfamiliar with the decades of AI and AGI research that have been done.

Never claimed any expertise on AI research. Thanks for the suggestion nonetheless.

Actually it appears that they read that supervised machine learning has limitations according to the no free lunch theorem and applied it to all of machine learning. See http://www.no-free-lunch.org/

Christopher Columbus didn't discover "an entire new world", he was just one of the first Europeans to land on a continent that had already been there, with plenty of people, for thousands of years.

Well, it was a discovery, but just for Europe. American natives discovered Europe as well.

Unless American natives knew of Europe, it was a trivial discovery. When one continent discovers another, the relevance is the new link between them, and interactions between the two economies.

If we discover aliens on a new planet, the fact that the aliens knew about themselves before that doesn't change much - first contact may be when they discover us too.

But let's not pretend this isn't slightly about political correctness, and sensitivity over colonialism, which ruins the objectivity over the subject.

It wasn't even a discovery for Europe because Vikings had already been to the Americas in the 10th century.

Yeah, this article was not well-researched or useful at all. As mentioned upthread, it seems focused on a very specific type of supervised learning that has since gone through a major leap in usefulness in the last few years.

The problem posited at the beginning of the article is in fact one of the example applications in a couple of ML courses/textbooks.

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