[aub3bhat]

Frankly as a grad student (The kind that the author apparently considers "dim witted"), the entire article is meaningless babbling without any underlying theme.

I wonder if the author truly understands "Machine Learning", what are his qualifications? A degree in Art History, and some "programming experience" aren't very assuring. E.g.

>> "The names keep changing—it used to be unsupervised learning, now it’s called big data or deep learning or AI"

WTF?? The author should enroll in a beginner Machine Learning course on Udacity or Coursera before making philosophical statements about fields he has zero clue about.

It seems the only skill the author has is piecing together meaningless arguments that appeals to average HN users incapable of distinguishing between informed opinions and pseudo-scientific rants. Hell at least bad graduate students have to give examinations, read papers and make original contributions that get peer reviewed (otherwise they fail/get-kicked-out/drop-out). Not like this guy who does not understands difference between "supervised" and "unsupervised" machine learning, yet feels comfortable in making "prophetic" statements about machine learning.

Also

>>> "These techniques are effective, but the fact that the same generic approach works across a wide range of domains should make you suspicious about how much insight it's adding."

What does he means by "same generic approach". If we assume he is implying specific algorithms then we have a good "No free lunch" theorem that shows that a single algorithm is not effective across all domains. Now if by "generic approach" the author mean "machine learning" in general then its as ridiculous as saying

"Mathematics is effective, but the fact that the same Mathematical approach works across a wide range of domains should make you suspicious about how much insight it's adding."

The entire article is filled with "truthiness" and "feel-good" statements, which fall apart on closer examination.

[idlewords]

My degree was in studio art, not art history.

[aub3bhat]

Those two are still orders of magnitude closer to each other when compared to difference between unsupervised learning with deep learning.

[detaro]

As someone with not particularly deep knowledge in either area, that admittedly sounds a lot like "no, but the differences between subfields in MY subfield are way more important than all that stuff over there", which is similar to what you claim the article does. They are important once you care about any details, but not for just describing changing fashions.

So I'm curious to hear a good explanation for that assertion, founded in knowledge of both areas.

[bdr]

The difference doesn't really matter in context. You're fixating on a small part of the article that isn't important to the main thread.

[aub3bhat]

Its not a "small part", its a basic litmus test. The four terms are completely different from each other, and are not names of methods.

Unsupervised learning: Learning without a set of labels.

Big Data: Collecting / using large amount of data.

Deep Learning: Complex, multilayer representations which perform better than shallow/linear representations.

AI: Artificial Intelligence, an overarching subject or grouping of subjects involved in building intelligent systems.

Can you imagine someone talking about space exploration while making a statement such as

>> "The names keep changing—it used to be black holes, now it’s called radio telescope or reusable launch system or Astronomy"

Thats how ridiculous the original statement is.

[elmigranto]

It's important to understand that this is not a technical talk/article and providing those examples in a sense "there are data, people analyze it, here are some stuff you might've heard" is fine.

You wouldn't complain that someone mentioned astronomy and music as an example in the talk about education, even though those are quite different disciplines.