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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 lea

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.

zitterbewegung 10y ago
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/ http://www.no-free-lunch.org/
nkozyra 10y ago
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.
j1vms 10y ago
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.
- ankurdhama 10y ago
  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.
  - interdrift 10y ago
    Because you could say learning is function approximation for the public.
tree_of_item 10y ago
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.
- deleted 10y ago
  [deleted]
- visarga 10y ago
  Well, it was a discovery, but just for Europe. American natives discovered Europe as well.
  - katherineduh 10y ago
    It wasn't even a discovery for Europe because Vikings had already been to the Americas in the 10th century.
  - Chris2048 10y ago
    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.
pron 10y ago
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.
- joe_the_user 10y ago
  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.
  - pron 10y ago

zitterbewegung 10y ago

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/ http://www.no-free-lunch.org/

nkozyra 10y ago

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.

j1vms 10y ago

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.

ankurdhama 10y ago
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.
- interdrift 10y ago
  Because you could say learning is function approximation for the public.

tree_of_item 10y ago

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.

deleted 10y ago
[deleted]
visarga 10y ago
Well, it was a discovery, but just for Europe. American natives discovered Europe as well.
- katherineduh 10y ago
  It wasn't even a discovery for Europe because Vikings had already been to the Americas in the 10th century.
- Chris2048 10y ago
  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.

pron 10y ago

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.

joe_the_user 10y ago
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.
- pron 10y ago