Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

Denny Britz, He drops school (his phd) to go into industries.

Christopher Olah: Same story, at least he doesn’t communicate about his education (http://colah.github.io/cv.pdf).

Both are well-known for their expertise, and followed by a large amount of people in the deep learning community.

You can def become an great machine learning engineer who used deep learning technique, without going to any university.

--- Ian Goodfellow:

"One of my biggest heroes is Geoffrey Hinton. His PhD was in experimental psychology ( . Biographical Sketch ).

In mechanical engineering, I think you learn quite a lot of the mathematical tools that are used in machine learning, so you won’t be starting from scratch.

In general, we often find that revolutions in one field happen when someone from another field arrives with fresh ideas that developed elsewhere."

(https://www.quora.com/Would-you-encourage-people-from-anothe...)

How can someone with almost no technical knowledge learn ML?

Ian Goodfellow::"It’s important to master some of the basics, like linear algebra, probability, and python programming.

But you don’t need to know all of linear algebra (I don’t think I’ve ever used a QR decomposition outside the classroom), all of probability (we don’t usually do much involving combinatorics / re-ordering of sequences in deep learning), or all of python programming (a lot of the more obscure language features are actually forbidden at some companies).

I’d say maybe start by learning enough linear algebra, probability, and python programming that you can implement logistic regression yourself, using only python and numpy..."

(https://www.quora.com/How-can-someone-with-almost-no-technic...)

As a college sophomore, how can I prepare myself for artificial intelligence?

Ian Goodfellow:

"Take classes on linear algebra and probability

Take classes on how to write fast code that is friendly to the hardware you’re working with.

Take classes on how to write good high performance, realtime multithreaded code.

Read Deep Learning.

Pick a simple machine learning project that interests you.

Work through this project, and when you have questions about the material you read about machine learning, trying to answer your own questions by running experiments in your project codebase.

If your university has them, take classes on machine learning, computer vision, natural language processing, robotics, etc."

(https://www.quora.com/As-a-college-sophomore-how-can-I-prepa...)

*You can find all the courses online.

Unless they really mean "CS-degree with surface level knowledge of stats" this should really be an offshoot of the math/applied math department, not CS

I don't think I would claim most AI work is research, but regardless, I think there is a still quite a lot of work that is quite applied nowadays. For example, there are a huge number of special-use-case neural networks you can think of to make very specific decisions based on image data. Or, nearly every online retailer would love someone to be able to come up with a good recommendation engine for products on their site. While a PhD might build a slightly-better one, I'm fairly confident that an undergrad trained properly (no pun intended) could pull off something usable.

The major sounds similar in intent to the Data Science major we introduced into our Bachelor of Computer Science at UNE (in Australia) last year. These days there is something of a branch in computing routes -- to express it in a very muddy way, designing and building the systems themselves (Software Development) versus applying machine learning algorithms, AI, and statistics across the data systems produce (Data Science). And of course there's other branches and specialisms too, but for our particular market those are what we have at the moment.

The level is still clear -- it's an undergraduate degree, and they are coming out with a good undergraduate's knowledge of the area. So that's entirely appropriate. There's no need to shy away from stating what they've been focusing on (offering a named major). And we do find they are useful and in demand.

As I said, no expert in AI & ML, but I don't see anything that differentiates them from other domains earlier (and wrongly) regarded as beyond undergraduate work.