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Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

cs.cmu.edu

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Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#351

Post by throwawayjava »

graycat wrote: post #323
throwawayjava wrote: post #238

Earlier quoted context omitted.

Whenever it makes sense to ground research in practice, CMU professors generally do so by working with industry and govt collaborators. Many CMU CS professors also do some paid consulting on the side. However, top tier PhD programs are not and never will be highly discounted consulting shops. At places like CMU grad students have perhaps more academic freedom than even their advisors. And good thing. The day CMUs of…

I've been a STEM field student and prof, and I've published some quite pure math research and also some AI research. My Ph.D. dissertation had its motivation from practice, e.g., from when I was Director of Operations Research at FedEx, and was an early case of what is now a major theme of the Department of Operations Research and Financial Engineering (ORFE) at Princeton. And since my Ph.D., I've made practical appl…

> Lesson: Practical problems, taken seriously, can result in some of the most important problems in pure research, and some of the progress in pure research can help get solutions to some practical problems. The motivation from pressing practical problems can help drive the research in both pure and applied research.

I'm pretty sure I explicitly agreed that this is often the case in my original post, so we must be talking past one another :)

What I'm arguing for is basically just academic freedom: the freedom of faculty and students to make choices about where they should resarch agenda. As your extensive history demonstrates, THIS APPROACH WORKS! All of those people chose to engage with industrial because it made sense for their research agenda!

More importantly, we can come up with an equally lengthy wall of text detailing accomplishments that would not have been possible without the freedom to work on things that industry isn't all hot and bothered about. E.g., neural nets until about 5 years ago!

And an even lengthier wall of text describing silly research agendas that only existed because of industry hype (AOP anyone?)

Industry collaboration can be a tremendous impetus. However, it can also be a distraction from more important problems or even an impetus to focus on silly problems. Professors and students should be incentivized and encouraged to do good research; industrial collaboration can sometimes be a useful tool, but it is a means, not an end.

Finally, IMO, the central premise of your argument (that there's not enough collaboration) is not factually accurate in the current climate. Read the proceeds of any major AI conference. Filter out papers written at top universities. Count the number of papers with vs. without an industrial collaborator named in the acks or even in the author list. Failure to collaborate isn't a failing of modern mainstream AI research.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#352

Post by throwawayjava »

xxpor wrote: post #312
anonu wrote: post #285

So much negativity on this discussion. I'm very surprised to see this from the HN crowd. Guess what? Computer Science, Engineering, etc... is getting more complicated and complex. So seeing a discipline (assuming this is CS) get broken down into more distinct groupings is actually a good phenomenon. Of course there's always foundational knowledge that is important to learn - but with time I feel like that information…

I just hope that they still include software engineering etc. I've interviewed many "AI specialists" who probably know AI pretty well (I'm not qualified to make that judgement) but they can't code their way out of a paper bag. Basic data structure errors, terrible organization, etc.

Internships work wonders here. No amount of coursework can substitute for doing the real thing. IMO internship placement, not coursework, will always be the best way to solve this problem.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#353

Post by graycat »

throwawayjava wrote: post #351
graycat wrote: post #323

Earlier quoted context omitted.

I've been a STEM field student and prof, and I've published some quite pure math research and also some AI research. My Ph.D. dissertation had its motivation from practice, e.g., from when I was Director of Operations Research at FedEx, and was an early case of what is now a major theme of the Department of Operations Research and Financial Engineering (ORFE) at Princeton. And since my Ph.D., I've made practical appl…

> Lesson: Practical problems, taken seriously, can result in some of the most important problems in pure research, and some of the progress in pure research can help get solutions to some practical problems. The motivation from pressing practical problems can help drive the research in both pure and applied research. I'm pretty sure I explicitly agreed that this is often the case in my original post, so we must be ta…

I never tried to constrain "freedom" in research. Freedom in research is crucial: With a good researcher, often only they have a good sense of the promise of their research direction. And, they are the one making a bet: If their research is soon good, then, modulo academic politics, they make progress in their academic career, e.g., maybe get to upgrade their 20 year old used Mazda to a 10 year old used Toyota and celebrate with a toast of tap water!!!

If current academic AI research is too close to non-academic problems, okay, I can believe that but see little downside since I have no respect for 90+% of current AI work anyway.

Net, contact with non-academic problems is crucial for STEM fields but with bad work can be abused. Of course it can be abused, special case of the general situation that nearly anything can be abused.

I spent a lot of time in STEM field academics: My considered, solid, well informed opinion is that there is far too little contact with important non-academic problems. E.g., when I went from Director of Operations Research at FedEx to graduate school in applied math, I brought with me a nice collection of important practical problems. In casual conversations, as I described some of those problems, even very pure research profs took detailed notes furiously. When I was an applied math prof in a B-school and MBA program, there were nearly no people from business in the halls with pressing problems looking for solutions, and that situation was really bad for the the business people, the students, the faculty, faculty research, and the B-school.

The suspicion has to be strong that if a research-teaching hospital were run like a B-school, then the physicians and researchers would be off studying the possibilities of silicon-based life on the planet Faraway, no one would know even how to dress a skinned knee, there would be no progress on any of the major, pressing medical problems, e.g., heart disease, cancer, and no one would want to go to a hospital no matter how badly they hurt.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#354

Post by physguy1123 »

bertil wrote: post #349
physguy1123 wrote: post #346

Earlier quoted context omitted.

To be honest most of those are covered at a good-enough level in the sophomore and junior years of a math undergrad. You don't need measures, differential geometry, or even epsilon-delta analysis to do ML which pins the calculus requirements pretty much to whatever proper multivariable calc class one takes in their sophomore year Edit: if my school wasn't so obsessed with teaching CS majors diffeq (probably just as a…

I would argue that you need measure and differential geometry to understand Support Vector Machine and the kernel trick properly. I think my contention is less things like being formally introduced to ‘epsilon-delta analysis’ (not sure what that is) but more that people trying to cut corners by skipping a semester of differential calculus tend to also skip a big part of the explanation around how models really work.…

You need differential calculus in R^n, but there's no need for the full force of differential geometry. Also, I don't think that measure gains much in terms of understanding, but it certainly is needed to do some proofs the proper way.

I agree that cutting corners is something I would be super skeptical of in this degree. It should really be an offshoot of a mathematics program, not a CS program with the bare minimal mathematics included. It's end goal is probably PR, money grab, and pumping out students that are really attractive for doing analytics grunt work.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#355

Post by evrydayhustling »

gowld wrote: post #255
evrydayhustling wrote: post #213

Earlier quoted context omitted.

They did a good job on this syllabus. Something had to give, and it is math and engineering depth. No 300 or 400 level courses, like networking or OS. Skipping some close to the metal stuff is probably fine, but I am surprised not to see at least some distributed computing or systems engineering skills. I'd be concerned that a grad could work well within an ideal data env but would get blocked by not being able to en…

I've worked with brilliant statisticians would not be able to engineer processing pipelines.

Absolutely. It just requires an environment where other folks are building, maintaining, and adapting data infrastructure to new needs -- e.g. a large company with a decent data engineering investment.

Also, in those specialized roles, a statistian with an advanced degree is likely to get more leverage. So I think it's smart for undergrads to work on being "unblockable" In a variety of environments so they can build experience - which is the spirit of the standard CS undergrad there.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#356

Post by garfieldnate »

jknoepfler wrote: post #164

I really do not like this move. AI and Machine Learning require graduate-level mathematical and computational skills. I don't think it's productive to pretend that we can train someone to be even remotely useful in these fields in four years of an undergraduate education. It sounds like an attempt to get around the fundamentals of csci to "skip to the interesting bits," which will produce graduates with a cursory kno…

There are plenty of engineering undergraduate programs. What's the difference here? Civil, mechanical, computer, electrical engineering are all very math-heavy. Sometimes an engineering degree can take 5 years instead of 4. So be it.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#357

Post by kiliantics »

aje403 wrote: post #88
kendallpark wrote: post #70

If you are an undergraduate in computer science these days, it is very hard to get into advanced AI/ML classes, which are typically reserved for graduate students. Back before the ML goldrush, a strong CS undergrad interested in AI could elect to take advanced coursework beyond the introductory AI class. Nowadays, good luck getting off the waitlist! Having an official "AI major" does at least tell students, "Hey, we…

Honestly, consider the flip side as well. This is just going to inflate the bubble more and create more unqualified candidates. AI degree means they're going to be churning out candidates who don't know what tcp is or what context switch means. Also, the guy with the (graduate or PHD) AI degree from CMU, before this, handing you his resume, may have studied functional analysis and convex optimization in addition to l…

Is an understanding of TCP necessary to do AI/ML? As someone who does work in ML (and has no formal background in CS but in physics), I see it as being mostly a combination of statistics and numerical computing. CS concepts outside of algorithms don't really come into it all that much.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#358

Post by aje403 »

kiliantics wrote: post #357
aje403 wrote: post #88

Earlier quoted context omitted.

Honestly, consider the flip side as well. This is just going to inflate the bubble more and create more unqualified candidates. AI degree means they're going to be churning out candidates who don't know what tcp is or what context switch means. Also, the guy with the (graduate or PHD) AI degree from CMU, before this, handing you his resume, may have studied functional analysis and convex optimization in addition to l…

Is an understanding of TCP necessary to do AI/ML? As someone who does work in ML (and has no formal background in CS but in physics), I see it as being mostly a combination of statistics and numerical computing. CS concepts outside of algorithms don't really come into it all that much.

Very large models train and evaluate across networks. Data pipelines are built across networks. You are a large handicap to a small team if you don’t know how networks work. I think a physics background is nothing more than checking off the math checkbox, which is certainly important

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#359

Post by throwawayjava »

graycat wrote: post #353
throwawayjava wrote: post #351

Earlier quoted context omitted.

> Lesson: Practical problems, taken seriously, can result in some of the most important problems in pure research, and some of the progress in pure research can help get solutions to some practical problems. The motivation from pressing practical problems can help drive the research in both pure and applied research. I'm pretty sure I explicitly agreed that this is often the case in my original post, so we must be ta…

I never tried to constrain "freedom" in research. Freedom in research is crucial: With a good researcher, often only they have a good sense of the promise of their research direction. And, they are the one making a bet: If their research is soon good, then, modulo academic politics, they make progress in their academic career, e.g., maybe get to upgrade their 20 year old used Mazda to a 10 year old used Toyota and ce…

> I never tried to constrain "freedom" in research. Freedom in research is crucial

Well then, I think we're violently agreeing. However, a couple of observations.

> e.g., maybe get to upgrade their 20 year old used Mazda to a 10 year old used Toyota and celebrate with a toast of tap water!!!

Here is CMU's dean on what happens to faculty with successful AI/ML research agendas: "How to retain people who are worth tens of millions of dollars to other organizations is causing my few remaining hairs to fall out".

I didn't realize how expensive used Toyotas have gotten...

>...applied math

I'll again reiterate that CS and especially AI have a completely different culture.

Also, this sentence seems to somehow undermine your entire thesis:

> If current academic AI research is too close to non-academic problems, okay, I can believe that but see little downside since I have no respect for 90+% of current AI work anyway.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#360

Post by harveynick »

bogomipz wrote: post #344
harveynick wrote: post #168

Earlier quoted context omitted.

Me too. I'm guessing they'll hear the word "perceptron" less than I did. Probably less Matlab and Prolog will be taught as well. I can't remember whether the Semantic Web course was CS or AI, but I suspect that won't come up, either. Fashions have probably changed enough that their probably won't be that much crossover. For me at least, it depends almost as much on who's doing the teaching than it does what's being t…

>"Me too. I'm guessing they'll hear the word "perceptron" less than I did." Why would that be? The Perceptron is very much a part of Neural Networks no? Wouldn't it be common now? I understand about Prolog being a big part of AI curriculum from that time but why was the Matlab so heavy?

Back then perceptrons (single neurons with engineered feature inputs) were a lot closer to cutting edge than they are now.

As for why Matlab was used a lot: because it comes "batteries included", I suspect. Probably the same reasons that Andrew Ng used it as the teaching language for his Stanford/Coursera Machine Learning course. Plus a lot of my lecturers had maths backgrounds.

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