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Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

• sp332 8y ago
  What did they call it before? As the article mentions, CMU has been doing AI for a long time.
  • bitxbitxbitcoin 8y ago
    True that, CMU has been doing AI for a long, long time. In terms of what CMU undergrads called their AI work before... Probably just a focus within a more general major like Computer Science.
  • gervase 8y ago
    Probably "Computer Science", I'm guessing? Most schools (as far as I'm aware) treat AI as a part of computer science.
  • minimaxir 8y ago
    CMU CS undergrad courses do have an AI component (e.g. building a chess engine), but this appears to be a more formal classification.
  • dgacmu 8y ago
    We never had a differentiated "AI" undergraduate major before. A student who wanted to concentrate on AI/ML would have used their electives to synthesize something they liked. The CS curriculum is: https://csd.cs.cmu.edu/academic/undergraduate/bachelors-curriculum-admitted-2017 https://csd.cs.cmu.edu/academic/undergraduate/bachelors-curr... So that student would pick AI-ish classes using their applications elective and two CS electives. In contrast, the AI curriculum: https://www.cs.cmu.edu/bs-in-artificial-intelligence/curriculum https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu... Removes several of the required courses from the CS curriculum (such as the upper-division systems requirement - OS/networking/distributed systems and the logic & languages requirement), adds another required math course (modern regression), and then uses the space from those freed-up courses to add a bit more depth in the AI core. It also shifts the set of available electives towards a more stats/ML/AI-centric group. It's not a huge change from our CS curriculum, but it's one that lets AI/ML-interested undergrads create something that's more stats-heavy and deeper in AI than they would have been able to with the CS version. Keep in mind this is all still within the school of computer science. This doesn't change things at the masters and Ph.D. level.
    • fixermark 8y ago
      As a CMU BS-CS graduate myself, the most obvious and startling shift is no OS or networking requirement. That alone makes it "not the Computer Science undergrad track" as far as I'm concerned. (Not to mention the notable lack of the utterly gigantic forest of higher-level discrete math concepts and programming language theory. Were I in a place to re-do an undergraduate career, this would have been very appealing to me relative to what CMU offered).
• epmaybe 8y ago
  Seems like the university put a decent amount of thought into the program. I wonder whose brainchild this was, and pushed for it to be its own degree? Regardless, even if a student no longer decides to pursue AI research or employment after graduation, they still have a marketable skillset for a variety of jobs.
  • epicureanideal 8y ago
    So others can also see the thought they put into the program, here's the actual curriculum: https://www.cs.cmu.edu/bs-in-artificial-intelligence/curriculum https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...
    • dofly 8y ago
      Thanks. I was wondering about that. Do you know, by chance, what books or notes they might use?
      • bytematic 8y ago
        Sorry this isn't a super researched answer but most of the course titles can be googled to find a "course website" that will usually list that information or a syllabus
      • epicureanideal 8y ago
        I'm starting with gathering the book requirements for some of the electives which might be of interest to me... For 85-712 COGNITIVE MODELING: How Can the Human Mind Occur in the Physical Universe? 2009 Author: Anderson, John ANSI Common Lisp 1996 Author: Graham, Paul For 85-211 COGNITIVE PSYCH: Cognitive Psychology and Its Implications 7TH 10 Author: Anderson, John For 85-814 COGNITIVE NEUROSCIENCE: No books listed. For 85-421 LANGUAGE AND THOUGHT: Language in Mind: An Introduction to Psycholinguistics 2014 Author: Sedivy, Julie I'll update this with more books shortly. For 15-386 Neural Computation: From course website: http://www.cnbc.cmu.edu/~tai/nc17.html http://www.cnbc.cmu.edu/~tai/nc17.html Trappenberg T.P. (TTP) Fundamentals of computational neuroscience, 2nd edition, Oxford University Press 2009 (required/recommended). Hertz J, Krogh A, Palmer RG (HKP) Introduction to the theory of neural computation., Addison Wesley 1991 (reference). For 15-150: Principles of Functional Computation: From course website http://www.cs.cmu.edu/~15150/ http://www.cs.cmu.edu/~15150/ There is no required textbook for the course. All material we expect you to be familiar with will be covered in sufficient detail in the lectures and lecture notes. There is an optional (and free!) text which some students find useful, called Programming In Standard ML (PSML). This book is based on the lecture notes for the predecessor to this course, 15-212. For CS 15-122: Principles of Imperative Computation: http://www.cs.cmu.edu/~15122/syllabus.shtml http://www.cs.cmu.edu/~15122/syllabus.shtml No textbook, but uses C, Emacs, Linux. For 15-381: Introduction to AI Representation and Problem Solving: Artificial Intelligence: A Modern Approach, Third Edition (Typical at most schools for teaching Intro to AI/ML.) For 10-401: Introduction to Machine Learning Machine Learning, Tom Mitchell. (optional) Pattern Recognition and Machine Learning, Christopher Bishop. (optional) Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, available online, (optional)
• mkirklions 8y ago
  Do you call this Artificial Intelligence? I dont call this AI, I call this automation. I know AI is a buzzword, but unless something is trying to think, its not AI to me. What they are talking about seems to be automation through lots of code. But hey, I havent been keeping up with this field, not sure what people are calling this.
  • deelowe 8y ago
    > additional course work in AI-related subjects such as statistics and probability, computational modeling, machine learning, and symbolic computation How is this automation and not AI?
    • levesque 8y ago
      I probably wouldn't call it automation, but you could regroup all these under the term machine learning or data science and IMHO it would be valid. I think OP refers to AI as the autonomous agent type of AI. Conversation, understanding the world, moving & acting, planning & decision making, multi-agent collaboration, this type of stuff. Some of these might fall under machine learning, but I would say most are outside of the scope of machine learning -- and do not seem to be targeted by the so-called AI degrees. However, I think it's fine to call those degrees AI, because they are AI -- machine learning is AI. The question is should we name this after the smallest denominator (machine learning) or the biggest one (AI). Also, in the short term future, more and more real AI (i.e., not machine learning) will probably be integrated in those classes, so why not skip one painful rebranding step.
    • ItsMe000001 8y ago
      To me "AI" means it learns by itself. Including the decision what to learn, and how. Having done quite a bit of statistics courses over the last few years (albeit with a focus on medicine/biology) and some of free the basic machine learning courses, AFAICS that is not the case, what and how something is learned is all decided and done by the human(s) in front of the computer, no? So, I don't see much "intelligence" - in the machine. Lots of it in those humans, of course.
• bitxbitxbitcoin 8y ago
  That... is a cool degree name. Still waiting for CMU to launch an Undergraduate Degree in Digital Currency, though.
  • deepreader 8y ago
    lol. Undergraduate Degree in what-so-ever-buzzwords.
  • asdsa5325 8y ago
    I can't tell if you are serious or sarcastic
• scott_s 8y ago
  I think I now know how the electrical engineers felt in the '50s and '60s.
  • komali2 8y ago
    What do you mean?
    • pradn 8y ago
      He's referring to the newly-created "computer science" degrees. The thought must be: why create a degree for a subfield of my field?
    • scott_s 8y ago
      That was when computer science emerged as an academic discipline distinct from the design and implementation of computers themselves; up to that point, an electrical engineer probably had the confidence that they were at the forefront of technological change.
    • cjoelrun 8y ago
      I think he's referencing the beginning of "Computer Science" degrees. Computers were researched by electrical engineers and mathematicians before.
      • Animats 8y ago
        Yes. My undergraduate diploma says "Electrical Engineering - Computer Science". Computer science wasn't a full department yet.
        • osteele 8y ago
          Mine says “Linguistics”. I took CS graduate courses, but there wasn't an undergraduate major yet. My father-in-law had a math degree and was a math professor, and then an EE professor — the latter while he co-founded an AI lab, that hired physics major Richard Stallman and other non-CS-majors.
• nopinsight 8y ago
  Related: China has just published the first AI textbook for high-school students. http://www.scmp.com/tech/china-tech/article/2144396/china-looks-school-kids-win-global-ai-race http://www.scmp.com/tech/china-tech/article/2144396/china-lo...
• harveynick 8y ago
  My undergraduate degree (from The University of Edinburgh) is Artificial Intelligence. I remember when I was visiting different universities in the UK back in 2000, Edinburgh was the only one I saw which offered AI as a "real" degree. Everywhere else it was a specialization which was tacked on in the final year of a computer science degree. That seemed really odd to me then. Seems even odder now.
  • cjbprime 8y ago
    Could be worse, if you'd gone to Reading Uni around then you could have ended up with a Cybernetics degree from the Cybernetics department :)
    • bencoder 8y ago
      Holder of "Artificial Intelligence & Cybernetics" from Reading here ;)
  • iamcasen 8y ago
    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.
    • _asummers 8y ago
      CMU seems to agree with that. Upthread bertjk posted: "AI majors will receive the same solid grounding in computer science and math courses as other computer science students. In addition, they will have additional course work in AI-related subjects such as statistics and probability, computational modeling, machine learning, and symbolic computation."
    • dwcnnnghm 8y ago
      I am currently studying this degree at the same university. A few AI concepts (NLP and Formal Language Processing) are introduced in the 2nd year. Other than that, all courses are CS/Maths. Keep in mind that at Scottish Universities, students apply directly to their degree and besides one or two courses per year (some like Medicine or Law often have no electives), students take only courses within their degree. This way, with most AI courses in 3rd and 4th year, students tend to have a strong enough grounding in CS principles and Maths for this material. That's not to say that the degree is perfect, or providing "real/production" AI, but it is certainly well done. You can see the courses here - http://www.drps.ed.ac.uk/18-19/dpt/utaintl.htm http://www.drps.ed.ac.uk/18-19/dpt/utaintl.htm
• imranq 8y ago
  Is CMU trying to compete with Udacity? I personally couldn’t take a degree in AI seriously.
  • acdanger 8y ago
    Why not?
  • epmaybe 8y ago
    How many jobs could you get with a Udacity certification versus an actual bachelor's degree? On that note, how many people took the AI classes at Udacity after getting an undergraduate degree at a 4-year institution?
    • henryw 8y ago
      I took the Udacity Deep Learning Nanodegree after a masters in CS. It was really fun, and I would highly recommend it.
  • deleted 8y ago
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  • whoisjuan 8y ago
    Are you really comparing CMU with Udacity? That's like comparing any Ferrari model with a Corolla. Not saying Udacity is bad. I mean a Corolla is a great, cheap, pragmatic and utilitarian car, just like Udacity is a great, cheap, pragmatic way to learn academic topics. But you really can't compare the two things. Carnegie Mellon and Udacity are extremely different and non-comparable in any rational way.
    • deleted 8y ago
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• majormajor 8y ago
  This feels like a more useful CS degree, IMO. I don't do anything like machine learning, but for both scaling backend services and building day-to-day business logic, I've gotten a ton of value out of knowing stats, logistics, and a certain amount of pattern recognition (ah, how terms go in and out of fashion). Take this stuff instead of the other sorts of electives I was picking from - UML Modeling, for instance - and I think you'll be set up with a good broad base for understanding both code and machine learning applications, but also broader decision-making at a business level.
  • bytematic 8y ago
    You had a whole class dedicated to UML Modeling? That makes me laugh, I know it gets very complicated in high level java applications, but man that feels like a waste of time.
    • majormajor 8y ago
      In theory it was "system design" or somesuch. In practice we learned nothing particularly useful what to take into account when deciding where to draw boundaries, and just focused on what was easy to represent in UML.
    • mark_l_watson 8y ago
      I once co-wrote a book on UML, and I also think that nature class in UML is not a good idea. I still sometimes use UML sequence diagrams though.
    • ethbro 8y ago
      Same. I hope I was the last generation of "OO waterfall design is the pinnacle of software engineering" thought.
  • Bukhmanizer 8y ago
    AI has always felt like a buzzword to me, but I have to admit, I really like the approach taken by the AI course that I took and Peter Norvig's textbook: https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Modern_Approach https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Mod... . Mostly, I like the focus on breaking down the problem domain in a logical way, so you can decide on which approach to take. The problem with the other courses I took (Machine Learning, Statistics, Computer Algorithms) is that they are so focused on solving specific problems that they often didn't adequately define the problem domain. I'd really recommend both Norvig's books to anyone interested in AI (in the broad sense).
• bertjk 8y ago
  "AI majors will receive the same solid grounding in computer science and math courses as other computer science students. In addition, they will have additional course work in AI-related subjects such as statistics and probability, computational modeling, machine learning, and symbolic computation."
  • murph-almighty 8y ago
    CMU Alum here (though I was ECE) - this description seems to make sense, all the 200 level courses are also CS reqs. I was also surprised to find out they brought 15-151 (Math Foundations of Computer Science) back.
    • anaccountwow 8y ago
      It's a pretty good class now!
  • FractalLP 8y ago
    And it will still look less beneficial than a standard comp Sci degree unfortunately. The only good part is that the school is pretty prestigious, so not as bad. Hopefully we don't have another AI winter.
    • Echoheart 8y ago
      CMU alumnus here (SCS & LTI). I don't see the degree a looking less beneficial, but I'm biased. CMU has perhaps a unique way of handling CS education. When I was there about. 20 years ago the CS department required you to have a minor and strongly encouraged a second major. Most core CS work can be completed by the end of your sophomore year. There's not a lot of fundamentals. After that you can go for breadth of topic matter (embedded systems, networks, AI, cryptography/security, distributed systems, etc), as was the case for me. This degree seems to give more depth in regard to ML. You're right about the prestige of the school. 15 years after getting my degrees I still get fast-tracked for interviews. So I feel the coursework matters very little when it comes to being recruited. That being said, the majority of the material I learned hasn't been used in my career. It's largely the way they teach you how to think and solve problems that companies familiar with CMU value.
• jimbokun 8y ago
  A long long time ago, I got a Bachelors degree in Logic and Computation, with a specialization in Computational Linguistics from the Humanities and Social Sciences Department at Carnegie Mellon. Seemed to be the closest thing to an "AI" degree on offer at the time, from my undergrad perspective.
  • minimaxir 8y ago
    As of recently, CMU had a Human-Computer Interaction track, which I know a lot of HSS students took.
    • aroman 8y ago
      There's an entire second major (and minor) in HCI, now :-)
• jcranmer 8y ago
  Looking at the course list (https://www.cs.cmu.edu/bs-in-artificial-intelligence/curriculum https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...), I'd struggle to believe that students are going to come out of this strong enough to be effective in AI. I never went to CMU, so I don't know how rigorous the "Modern Regression" course is for actually getting people sufficiently well-grounded in statistics to be able to overcome p-hacking and similar fallacies in analysis. I also would much like to see some sort of capstone project showing that the student can actually pull the AI together to make something complete, rather than having a merely theoretically background.
  • nightski 8y ago
    How is that different from any other Undergraduate program. At the end of the day undergraduate degrees are like the bare essentials of education - there is a life long journey of learning in any technical field.
    • jcranmer 8y ago
      The short of it is that AI isn't an undergraduate-level specialization. Having a demonstrated capstone project, a full system that someone could point to when in an interview, would go a long way to ameliorating concerns. Masters degrees generally have a thesis that qualifies, and it wouldn't be hard to make a senior project be a requirement for an undergraduate degree (my CS department had such a requirement).
  • minimaxir 8y ago
    I took Modern Regression at CMU for my Statistics minor: yes, it's rigorous, with an emphasis on linear regression (and the necessity for proper p-value handling), with plenty of matrix algebra and statistical theory.
  • septimus111 8y ago
    CMU stats courses are stellar - especially this one, which was written by Cosma Shalizi, I believe
  • 8y ago
• bytematic 8y ago
  I love the ethics requirements. My current uni doesn't have this and I'm so glad I took it at the college I attended as an underclassmen. The different ethical theories apply so well to a lot of work being done in computer science.
  • kendallpark 8y ago
    My traditional CS curriculum had ethics as the capstone. Do most CS programs not do this?
    • fixermark 8y ago
      I do not think they do.
  • fixermark 8y ago
    I almost wonder if they're under-required. At most one required from a set of three? Is that going to keep Skynet at bay? ;)
  • greggarious 8y ago
    I'd love if they had a psych component that involves getting IRB certified. (Maybe mandate experimental psychology or a social science methods course - most psych departments have one for undergrads) The IRB certification process alone delivers more practical education on ethics (and what has happened in the past without them) than many formal courses in the subject.
• learc83 8y ago
  This seems like a publicity stunt. It sounds like a CS degree where the electives are predetermined. Why couldn't they just make this a concentration when it's so intimately intertwined with CS. The extra overhead graduates will have to deal with doesn't seem worth it. "AI majors will receive the same solid grounding in computer science and math courses as other computer science students. In addition, they will have additional course work in AI-related subjects such as statistics and probability, computational modeling, machine learning, and symbolic computation." They even say "other computer science students".
  • electricslpnsld 8y ago
    > It sounds like a CS degree where the electives are predetermined This is offered through CMU's school of computer science (SCS), so that is exactly what this is. CMU loves creating new sub-departments with SCS, for some reason, there are already 7 or 8.
    • ethbro 8y ago
      New faculty titles!
  • dgacmu 8y ago
    It's not just predetermined electives - it removes several of the CS upper-division breadth requirements to allow more depth in AI/ML/stats. (I posted a comparison below, so won't repeat it here.) It's a pretty decent change to serve the students who really want to push more on ML. The key thing to look at is what the CS major requires that the AI major doesn't -- in 8 semesters, you can only fit in so many classes.
    • learc83 8y ago
      I looked at your description, and compared the requirements myself. I'm even more convinced this is for publicity (or other political reasons). There's nothing there that couldn't have been done by very slightly altering the CS requirements. If CMU had done that instead, students would have the ability to take more AI classes, but they wouldn't be at a disadvantage if they decide (or need) to work in another field of CS.
• Upvoter33 8y ago
  What's most interesting about this list are the wide range of courses offered for undergrads in the topic (that is, assuming they are regularly offered) -- a very impressive list. Other schools will be hard-pressed to offer something so diverse and interesting.
• yeison 8y ago
  That is so awesome. I think it is very valid to do so.
• mooneater 8y ago
  I think its interesting that Deep Learning is just one course in a cluster of electives. To read recent press you might think that's all current AI is.
• joshuamorton 8y ago
  This looks good (which shouldn't be surprising coming from CMU). I'm kind of impressed by how similar this was to my undergrad curriculum (focusing on AI/ML and CS theory). Looks like a fun program. Also wow, Great Theoretical Ideas in Computer Science[1] is a hell of a course. Induction, DFAs, matchings, TMs, complexity, NP, approximation and randomization, Transducers, crypto, and quantum algos. That's a lot of material, even if most of it appears to be only introductory level. [1]: https://www.cs.cmu.edu/~15251/schedule.html https://www.cs.cmu.edu/~15251/schedule.html
  • aroman 8y ago
    yeah, it's rather notorious at CMU for being particularly challenging and fast-paced, especially for a freshman course. sometimes called "two-fifty-fun"!
    • exogeny 8y ago
      I'm getting a panic attack just reminiscing about it. 213 and 251, the twin terrors.
  • jcdavis 8y ago
    That course (15-251) is somewhat controversial among CS students (at least it certainly was when I was there) in that its primary goal in the curriculum seemed to be to act as the weedout course, since as you observed it covers a ton of material in little depth. It is typically taken 2nd semester freshman year, and I know at least 3 people my year who dropped out of CS from it :(
  • srinivasan 8y ago
    “250fun”, as we ironically called it. It was intense, but the topics were covered in a very interesting way.
• kendallpark 8y ago
  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 are making it a priority that undergraduates have access to our rich AI curriculum."
  • QML 8y ago
    While there may be a lot of demand for AI/ML, I’m concerned to whether there are enough students with the proper foundations in math and stats to do well. New classes such as Data Science seem to just instruct students how to use algorithms and not why.
    • fixermark 8y ago
      Looking at the curriculum CMU has put together (https://www.cs.cmu.edu/bs-in-artificial-intelligence/curriculum https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...), I see statistical fundamentals in the Math and Statistics Core. I expect it should handle the why and the how. (... though I get the sense from the outside looking in that a lot of machine learning at this point is still a little bit alchemy, so there may not always even be a firm "why" answer to give. All the more reason to give students firm general fundamental groundings so they can seek out those answers).
    • deleted 8y ago
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    • fma 8y ago
      I see students that graduate with degrees in CS/IT to fill the demand. Quality has declined... Translating to AI/ML I would assume the same. I think CMU would retain quality, but the local universities will start churning out AI degrees like butter.
    • gowld 8y ago
      Are you talking about undergraduate courses, or non-accredited certificate courses? We've had stuff like A+ / Cisco / Java certifications for decades. They fill an important niche, but they aren't how industry leaders are trained.
• michaelsjoeberg 8y ago
  >include a strong emphasis on ethics and social responsibility
  • adjkant 8y ago
    This should be for all CS majors, not just those in AI
• wgyn 8y ago
  Carnegie Mellon was the first university to offer a PhD in Machine Learning (via the Machine Learning Department which, again, I think is relatively unique in its existence). Regardless of how you feel about the hype, they made an early bet on the field and adding an undergraduate degree seems consistent. Source: https://www.ml.cmu.edu/about/index.html https://www.ml.cmu.edu/about/index.html
  • stochastic_monk 8y ago
    What I don't yet understand is how this new AI program differs from machine learning. Is AI about broader questions about conversational interactions and interpretability, closer to the Lisp heydays of 5 decades ago, or more about applications than theory? Edit: years to decades
    • willsinclair 8y ago
      From the article: > The bachelor's degree in AI will focus more on how complex inputs — such as vision, language and huge databases — are used to make decisions or enhance human capabilities My understanding is that AI is more about applying ML concepts to mimic human intelligence.
• huffer 8y ago
  so... what will we call a graduate holding such a degree?
  • adjkant 8y ago
    A CS degree with an AI concentration closer to that of a masters student than an undergrad. While I think the importance of this degree is small, there are jobs that will prefer that.
• uptownfunk 8y ago
  It seems similar to when universities started spinning out stats degrees as separate from math
• philjohn 8y ago
  My degree was in Computer Science & Artificial Intelligence way back in 1998 at the University of Birmingham (UK) - interesting to see not that many places offered it until recently. Good to see that other undergrads are going to have access to AI/ML courses rather than them being solely for post grads.
• dqpb 8y ago
  > providing students with in-depth knowledge of how to transform large amounts of data into actionable decisions. That is a depressing definition of artificial intelligence.
• a-dub 8y ago
  I think you can make this degree out of the standard electives offered in any good CS program, although the courses at CMU are more likely to be great rather than just good. To me it was just "the cool CS electives that you get to do if you get all the math, stats and signal processing down pat." Sad news is that the fields that make up these interesting classes are things that people do PhDs in, so unless you get a PhD you're unlikely to get anyone to pay you to do them when you're done and will get stuck making the help button for the Google Cloud for Education Administrators Console anyway...
  • gowld 8y ago
    The help button for the Google Cloud for Education Administrators Console is made by PhDs.
• crazy_monkey 8y ago
  Will students do better than random on exams?
• KSS42 8y ago
  University of Toronto Engineering also is starting a Machine Intelligence option: Engineering Science - Machine Intelligence Option http://engsci.utoronto.ca/explore_our_program/majors/machine-intelligence/ http://engsci.utoronto.ca/explore_our_program/majors/machine... What is the difference between the Machine Intelligence major in Engineering Science, and an undergraduate degree in Computer Science? While there are some commonalities between the Machine Intelligence major and what is offered through Computer Science, engineering offers a unique perspective. First, graduates will have a systems perspective on machine intelligence, which integrates computer hardware and software with mathematics and reasoning. This enables a focus on algorithm development and the relationship between machine intelligence with computer architecture and digital signal processing. Secondly, graduates will benefit from an approach that encourages problem framing and design thinking. Design thinking is a method for the practical and creative resolution of problems, which encourages divergent thinking to ideate many solutions, and convergent thinking to realize the best one. Students will be able to frame and solve problems in the MI field, and apply MI tools to problems in many application areas. These include finance, education, advanced manufacturing, healthcare and transportation. This field is in a phase of rapid development, and engineers are well equipped to contribute as a shaping force.
  • zukzuk 8y ago
    I got my BSc at U of T, in Artificial Intelligence (and Cognitive Science)... in 2006. So ahead of the curve! This was right before the big deep learning explosion, at the very end of the last AI winter. Our lecturers spent a whole lot of time lamenting at the endless disappointments of AI research. I walked away deeply skeptical, and can't help but see the current ML hype as a glass half empty.
    • gandreani 8y ago
      What were they disappointed in? Also, what do you mean by "see the current ML hype as a glass half empty"? I take it that you are also disappointed with the recent research I'm just getting into the field, but it seems to me at least in computer vision, voice recognition, and text to speech there have been great strides in the recent years
• camdenreslink 8y ago
  In general, I'd say why even have a separate degree, but it makes sense for CMU with their rich AI history with the Robotics Institute. They probably have a lot of opportunities that could fill an entire major.
• triska 8y ago
  Personally, I find the lack of logic programming, and logic in general, a rather notable property of the outlined curriculum. In fact, "logic" is nowhere explicitly mentioned in the course titles. Neither are "formal" and "method". For comparison, at the department of AI in Edinburgh, Prolog was very important and even actively developed to such an extent that current Prolog systems are still hugely influenced by "Edinburgh Prolog" (the original version being "Marseille Prolog"). Also, theorem proving is an important area of computer science with many connections to AI. In Vienna (TU Wien), the related Computational Intelligence curriculum also involves a lot of logic, Prolog, constraint solving and formal methods, which play an important role in many areas of AI. It is a graduate degree though and assumes familiarity with many of the topics that are mentioned in this curriculum.
  • ciscoriordan 8y ago
    Math Foundations of Computer Science (15-151) covers proofs, combinatorics, etc. https://csd.cs.cmu.edu/course-profiles/15-151-Mathematical-Foundations-for-Computer-Science https://csd.cs.cmu.edu/course-profiles/15-151-Mathematical-F...
    • triska 8y ago
      Yes, this is the book from the course's page: http://www.math.cmu.edu/~jmackey/151_128/infdes.pdf http://www.math.cmu.edu/~jmackey/151_128/infdes.pdf This definitely has some aspects of formal logic in it and contains a few definitions about proofs, theorems etc. The logic-oriented aspects are covered in Appendix B ("Foundations"), which is currently unfinished. Still, this is no substitute for, and clearly does not intend to be, a course on formal logic, let alone logic programming or model checking.
  • joshuamorton 8y ago
    >Also, theorem proving is an important area of computer science with many connections to AI. I think this is only true if you use a different definition of AI than the one likely used here. Expert systems aren't considered to be very effective tools for useful "AI" anymore. You can't define a procedure to recognize a happy face in logic programming, at least not with any degree of efficiency.
• bcatanzaro 8y ago
  I lead an AI research lab, and I feel that a large part of the work is just coding. So I hope graduates of this program will be excellent coders. I won’t be hiring AI graduates that don’t know how to write code.
• Rainymood 8y ago
  Has been standard in Europe for quite some years now to see an undergraduate degree in AI ... weird seeing the US lag in this.
• jknoepfler 8y ago
  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 knowledge of computers, programming, math, and data science, which is honestly worse than no knowledge at all. I'm not fundamentally opposed, but I think this is akin to creating a "Condensed Matter and Nanophysics" undergraduate degree alongside "Physics." Changing the name of a factory will not change the output. The only solution to creating more and better AI research is to invest in better fundamentals in computer science and mathematics, then create pipelines for specialization. Slow and low.
  • debt 8y ago
    You definitely do not need csci fundamentals to learn AI/ML. It’s more of a math discipline. Knowing what big endian means or the fundamentals of programming languages aren’t really needed when learning AI.
  • nabla9 8y ago
    I agree that AI/ML is kind of narrow for BS degree. I think there is room for specialist field called numerical programming or scientific programming. Someone who knows basics of numerical programming, math, statistics, data science, computational modeling and simulation, DSP etc. and can apply the skills to multiple different fields, including machine learning. The skill level needed to work independently usually requires at least masters level, but there could be BS level degree as well. Today the problem is that you have research PhD's doing basic grunt work because you can't just hire a coder. All they know is web stacks, android and SQL.
    • tw1010 8y ago
      The narrowness of a niche is wholly dependent on how much demand there is for the thing in the grander scheme of things. Programming used to be incredibly narrow, and I would bet ya there were people complaining along similar lines back when engineering meant just mathematics and physics. Clearly the AI and ML rift has been growing, partially evidenced by this program; enough for it to no longer be considered "too narrow for a BS".
• graycat 8y ago
  IMHO, a "modest proposal" for the the CMU CS AI degree, CMU CS, and much of STEM field academics: Have much of the department and program borrow from clinical medicine. So, have the department be in part a clinic for solving problems from outside academics via STEM material, information technology, CS, AI, etc. E.g., yes, continue to have seminars with graduate students and professors with, call it, solutions looking for problems but also have people from outside academics with problems looking for solutions. In the halls, should find, yes, students and professors but also eager, determined people from outside academics with problems looking for solutions. So, in part the halls should look like the ER of a major research-teaching hospital, like a cardiac center, stroke center, trauma center, birthing center, oncology ward, etc. working on important real problems from outside academics. So, some problems will yield to data collection, filtering, exploratory data analysis (J. Tukey), graphing, descriptive statistics, cross tabulation, some simple hypotheses tests, etc. Some problems will yield to optimization -- differentiate, set to zero and solve; linear programming, multi-objective linear programming, network linear programming, integer programming, quadratic programming, non-linear programming, convex programming, etc. There can be approximations, Lagrangian relaxation, achieving necessary conditions for optimality, exploitation of particular problem special structure, heuristics. There can be classic statistics, especially multi-variate statistics, regression, principal components and factor analysis, discriminate analysis, experimental design and analysis of variance, catagorical data analysis, time series analysis. And there can be more advanced tools in deterministic and stochastic optimal control, more in probabilistic and stochastic model building, etc. There can be work in natural language understanding, computer vision, and robotics. Some of the work for routine solutions can be done by students as part of apprenticeship, meeting and working with people from outside academics, etc. Then for the better stuff, some of the more serious problems from outside academics can be the start of research for students or faculty. Partly the justification for the research would be the importance of the real problem. There is an old recipe for rabbit stew that starts out, "First catch a rabbit.". Well, a recipe for applied STEM field work could start out, "First find an application ..." or at least a good problem. Then, sure, look up, stir up lots of good theorems and proofs and algorithms and code but focused on the motivating real problem. And then the research already has one good application. At that point, curiously, importantly, the chances of another application are relatively high, that is, higher than a first application for work with so far zero applications. So, maybe CMU can develop some relatively broad expertise in, say, scheduling, logistics, supply chain optimization, facility location, monitoring, automation, etc. When especially good results have been obtained for some business, sure, the Chair of CS, the Dean of the School of Engineering, the President of CMU, and various CMU Trustees might call the business CEO and mention that CMU has a fund .... That is, solicit donations! When the program is established with good credibility, audit the financial benefits obtained and suggest that 10% back to CMU will get a seat a the Dean's Round Table, etc. Research-teaching medical schools deal with real problems and also make progress in research. Academic departments of engineering, etc. should do much the same.
• inputcoffee 8y ago
  For those who don't want to click through all the links, here is the syllabus: https://www.cs.cmu.edu/bs-in-artificial-intelligence/curriculum https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu... It seems reasonable to me. They are required to take 7 humanities courses. I certainly hope they are required to take a lot of other classes where they are required to read, reason, and write.
• rajacombinator 8y ago
  Reputable academic institutions should not get caught up in passing trends, if they want to stay reputable.
  • pishpash 8y ago
    If it's a passing trend then CMU already hired too many tenure-track assistant profs. It looks like they will be used at least for some teaching.
• skate22 8y ago
  I feel like i would rather be going into a DS interview as a CS grad with ML/AI experience than as an AI grad. Maybe i'm wrong here, but our biggest painpoint hiring for our datascience team is lack of dev skills. Simple stuff like deploying a model to heroku & or writing tests
• greesil 8y ago
  I think if this is more the fundamentals for AI rather than "this is all you will ever need to know", in other words NOT vocational, then this makes a lot of sense. When I started counting off the prerequisites to know what you are doing in ML, I could see it filling up a few years of coursework. Calculus up to multivariate calculus (gradient descent), a semester or two of statistics (gotta understand precision / recall, expectation, moments, marginalization), some programming (how are you going to implement your solution?), the fundamentals of regression (overfitting, cross-validation, regularization), don't forget linear algebra. Then there's some prerequisites for unsupervised learning like expectation maximization. SVMs? You'll be needing some constrained optimization and some applied math to make/understand a reasonable solver. This seems like the greater part of an undergrad degree.
• avelis 8y ago
  In Fall 2007, USF (CA) CS program allowed me to take a Grad AI course as an undergrad. However, my coursework was graded differently ss an undergrad. I had a blast taking it, learned alot but I can say the requirements to get in depth with the material require a graduate program.
• rlanday 8y ago
  Which university is going to be the first to start offering blockchain degrees?
• Vinnl 8y ago
  Interestingly, I majored in Artificial Intelligence for my undergraduate degree, which has been a thing in the Netherlands for thirty years now. Vastly different scope, probably, but still interesting - I believe there are si universities here offering the program.
• anonu 8y ago
  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 becomes de-emphasized to focus on higher-levels of understanding and knowledge. On another note, CMU was always very good at cross-disciplinary studies. (Building Virtual Worlds comes to mind). As the article points out, this new degree bridges over to the humanities and ethics. With where we are today with AI, isn't it a good thing to train the AI developers of the future to think about the implications of their work?
  • GeorgeTirebiter 8y ago
    I completely agree. CS started often from the Math department, sometimes from the Engineering department. Further specialization is to be not only expected, but welcome, as complexity increases. Is there a better way?
    • 131012 8y ago
      I totally agree. I really think it is heading toward a med school styled specialization. And with that will come the added prestige. CS is so remote from pure/natural sciences, it screams for standalone schools, like law and medicine. And with it, prestige and power. Why are people against that?
  • xxpor 8y ago
    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.
    • throwawayjava 8y ago
      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.
• deleted 8y ago
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• lawrenceyan 8y ago
  That's why I chose to major in applied math. Impossible to invalidate, though if society somehow manages to do so, I won't be mad sheerly because of how impressed I'll be.