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Carnegie Mellon Launches Undergraduate Degree In AI (cmu.edu)
Earlier this week, Carnegie Mellon University announced plans to offer an undergrad degree in artificial intelligence. The news may be especially attractive for students given how much tech giants have been ramping up their AI efforts in the recent years, and how U.S. News & World Report ranked Carnegie Mellon University as the No. 1 graduate school for AI. An anonymous reader shares the announcement with us: Carnegie Mellon University's School of Computer Science will offer a new undergraduate degree in artificial intelligence beginning this fall, providing students with in-depth knowledge of how to transform large amounts of data into actionable decisions. SCS has created the new AI degree, the first offered by a U.S. university, in response to extraordinary technical breakthroughs in AI and the growing demand by students and employers for training that prepares people for careers in AI.
The bachelor's degree program in computer science teaches students to think broadly about methods that can accomplish a wide variety of tasks across many disciplines, said Reid Simmons, research professor of robotics and computer science and director of the new AI degree program. 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, he added. 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. Simmons said the program also would include a strong emphasis on ethics and social responsibility. This will include independent study opportunities in using AI for social good, such as improving transportation, health care or education.
The bachelor's degree program in computer science teaches students to think broadly about methods that can accomplish a wide variety of tasks across many disciplines, said Reid Simmons, research professor of robotics and computer science and director of the new AI degree program. 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, he added. 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. Simmons said the program also would include a strong emphasis on ethics and social responsibility. This will include independent study opportunities in using AI for social good, such as improving transportation, health care or education.
I was thinking along similar lines before I read your post. I'm about to head back to school for my masters and put some thought into which area I wanted to study. Partly, I want to retire as early as I can, which means making good money first. There is a huge demand for AI professionals, leading to high salaries. It just doesn't interest me much, though.
In my case I think it's partially because I've been on a software quality kick the last few years. If aerospace engineering was done like software engineering, planes would crash every day. It doesn't have to be like that. We can do it right, the first time. The attitude of "it seems like it pretty much worked when I tried it, let's ship it" gets on my nerves.
While AI isn't exactly "it seems like it pretty much works", it tends to lean much more in that direction than the systems I want to create, systems about which I can say "this is known to be absolutely correct; it has been mathematically proven correct".
âoeIf we want to be serious about quality, it is time to get tired of finding bugs and start preventing their happening in the first place.ââ" Alan Page
>The whole 'provably correct code' disappeared from reality as soon as I was half a step beyond academia.
It did at one point. Maybe around 1988 or so. In the 1970s programmers were people with degrees in math, so there was a lot more correctness. As math majors, they had done plenty of mathematical proofs, so the idea of knowing that you're getting the right answer made sense to my mom's generation.
We've had a phase of "sloppy" programming for a while now, but over that time our tools have improved immensely. Static analysis, which can automatically prove certain things about code, is coming back into style. New tools, and possibly new languages, may allow for a degree of reliability that wasn't feasible in 1988.
To give two examples, functional programming, which is currently in production use, can readily prove certain things. The most basic is using a functional language by itself proves that every function has no side effects - the only thing it does is return a value, which is determined entirely by the arguments passed to the function. Starting with those guarantees, it's fairly easy for tools to prove some other useful things about the function. Another example is that SQL schemas allow you to fairly easily make certain guarantees about the system. I do that at times and it's faster than not enforcing guarantees, because I don't have to debug problems caused by assumptions not actually being true. SQL *can* be used to make "we hope it works" systems, but in some cases that takes longer. It can be faster for me to apply rules which guarantee things, rather than tracing down problems related to assumptions.
I have hope that better and better tools and processes will be developed, and I'd like to help develop them. So far I've started by applying practices such as code review in organizations that didn't previously do it. We've found that code review / peer review reduces bugs enough to make it worthwhile. As a bonus it is a great training tool - programmers learn from each other practical approaches that apply directly to your codebase - because they ARE your codebase.
"Undergraduate Degree In AI"
AI needs Overgraduates.
Standard CMU undergrad CS curriculum: https://csd.cs.cmu.edu/academic/undergraduate/bachelors-curriculum-admitted-2017
CMU AI degree curriculum: https://www.cs.cmu.edu/bs-in-artificial-intelligence/curriculum
I dunno. IMO this could be a concentration or a graduate program. I think a classical undergrad CS program would be worth more to a student because it's more generic and thus more widely applicable.