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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

#331

Post by throwaway2345k »

harveynick wrote: post #60
iamcasen wrote: post #26

Earlier quoted context omitted.

I'm curious to hear about your experience. In my mind, artificial intelligence can't be separated from computer science. In fact, I feel like you need a full Comp Sci degree before you can effectively apply your skills to real world AI challenges.

It's a little more complicated than I made it sound above. 50% of my curriculum was courses from CS department, the other 50% courses from the AI department. It was actually possible to do AI without doing CS at all, though. There were AI and Linguistics, and AI and Phycology degrees, for example. There was basically no crossover in languages used in the courses taught by the two departments. CS was mostly Java with…

Don't they teach Haskell to students as their first language now?

Sethu had an incredibly high dropout rate among his PhD students. I've heard stories about him publicly berating them at lab meetings - calling them stupid etc. Smart guy, but I'd never want to work with him.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#332

Post by bertil »

physguy1123 wrote: post #292
bertil wrote: post #217

Earlier quoted context omitted.

You are describing those models as they are being applied (and as many introduction present them) not how they can be trained. To understand whether those models converge, how to calculate the solution to even a basic linear regression, you need graduate math: matrix algebra, differential calculus, topology.

Unless you mean some sort of differential topology, those are rigorously covered in and respectable math undergraduate.

I believe that’s the point of contention: I’ve you’ve gone through those programs, you are a graduate.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#333

Post by WhiteMonkey »

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…

As someone with a BSc. in AI from a university that's been handing them out since the 50s, I, er, disagree.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#334

Post by fooker »

gowld wrote: post #259
fooker wrote: post #190

Earlier quoted context omitted.

Not a big jump, if you conceptually stick to the 'area under curve' definition. Analytically computing surface/volume integrals, yes, that is a big jump. But I don't understand why students need to memorize N algorithms and M substitution rules for that.

Sure, and AI isn't a big jump from statistics.

I would say AI:Statistics :: Engineering:Physics .

Sometimes it is a big jump, sometimes it is all common sense.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#335

Post by madengr »

bertil wrote: post #217
MR4D wrote: post #204

Earlier quoted context omitted.

If you can teach a kid frequentist (regular) statistics, then you can certainly teach them things like Bayesian statistics. Add things like decision trees (which you need to build something like a chatbot). In fact, a friend of mine from my freshman year (way back before everyone had computers in college) wrote a chatbot. I had never seen anything like it, and he let it loose on IRC. Was pretty cool back when things…

You are describing those models as they are being applied (and as many introduction present them) not how they can be trained. To understand whether those models converge, how to calculate the solution to even a basic linear regression, you need graduate math: matrix algebra, differential calculus, topology.

Yeah, well those are not graduate courses, maybe the exception of topology (for a non math major). Those are 1st and 2nd year undergrad engineering/CS courses.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#336

Post by bertil »

MR4D wrote: post #237
bertil wrote: post #217

Earlier quoted context omitted.

You are describing those models as they are being applied (and as many introduction present them) not how they can be trained. To understand whether those models converge, how to calculate the solution to even a basic linear regression, you need graduate math: matrix algebra, differential calculus, topology.

To me, your statement is like saying an airline mechanic needs to have a pilot's license because he needs to know how the rudder affects the flight. There are three different roles that I think you are conflating: Designing, Building (and maintaining), and using. Each has a different skillset. But to think that you need to determine the convergence of models makes no sense to me. Why can't an undergrad build a simple…

I like the metaphor, but I think it would be more appropriate to flip the roles: it’s more akin to asking the person using the models (or the airplane in your simile, the pilot) to understand how their work (i.e. have basic mechanical understanding). As it happens, that’s precisely what we ask pilot: they need to have enough mechanical engineering talent to inspect the plane in detail before take of as well as understand, diagnose and fix a problem in the air.

To come back to modelling: I see daily people with a cursory understanding of models misusing their tools and not noticing blatant problems. Just today:

- someone using random forest to predict a quantity (which you can do, it’s often nicknamed “random tree regression”) so the first thing their did was to turn quantities to predict to as many categorical variables;

- someone else, on the same problem didn’t see why having a massively higher mean square error on the out-of-bag sample compared to the test sample was suspicious.

I’m not saying that five years of university will solve that, but I’m noticed that taking the time to dig into the model, vs. assuming it’s like a car -- you only need to turn the key and go with it -- is strongly correlated with making those mistakes.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#337

Post by madengr »

freyir wrote: post #251
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…

> AI and Machine Learning require graduate-level mathematical and computational skills Yeah, not really. A lot of day-to-day work in ML requires rudimentary math, at most. I know PhDs who quickly get discouraged with ML because they're suddenly spending 95% of their time doing the grunt work. It would be a boon if we could hire non-PhDs who are competent in the fundamentals of signals, algorithms, statistics, and exp…

I wish undergrad EEs knew how to solder.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#338

Post by madengr »

PascLeRasc wrote: post #294
freyir wrote: post #251

Earlier quoted context omitted.

> AI and Machine Learning require graduate-level mathematical and computational skills Yeah, not really. A lot of day-to-day work in ML requires rudimentary math, at most. I know PhDs who quickly get discouraged with ML because they're suddenly spending 95% of their time doing the grunt work. It would be a boon if we could hire non-PhDs who are competent in the fundamentals of signals, algorithms, statistics, and exp…

So essentially what we need is an "academic CSEE" degree, where you replace courses on industrial topics like application design and databases / circuits and electromagnetics with these theory-based classes from both departments. I'd also suggest adding some systems neuroscience courses in there too.

Electromagnetics is all theory, so much so it’s been dropped from most EE curriculums for being to difficult.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#339

Post by PascLeRasc »

moultano wrote: post #320
PascLeRasc wrote: post #294

Earlier quoted context omitted.

So essentially what we need is an "academic CSEE" degree, where you replace courses on industrial topics like application design and databases / circuits and electromagnetics with these theory-based classes from both departments. I'd also suggest adding some systems neuroscience courses in there too.

I think you could get away with swapping out upper level algorithms and systems courses and swapping in statistics and ML. I've used my OS class 0 times in my career. The class I've used the most was the second level statistics class I took for my econ minor.

definitely, and your algorithms class was probably industry-driven and an exposure to what you should be doing to write code in the workforce, not teaching you to come up with novel algorithms.

Re: Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

#340

Post by PascLeRasc »

madengr wrote: post #338
PascLeRasc wrote: post #294

Earlier quoted context omitted.

So essentially what we need is an "academic CSEE" degree, where you replace courses on industrial topics like application design and databases / circuits and electromagnetics with these theory-based classes from both departments. I'd also suggest adding some systems neuroscience courses in there too.

Electromagnetics is all theory, so much so it’s been dropped from most EE curriculums for being to difficult.

I wish my school would have done that, those were terribly intensive and not really stimulating like signals was.
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