201–210 of 362 posts
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
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Ha! Grad school at CMU took a chainsaw to my GPA after 1st year. Studying 16h/day and still didn't have enough time to complete all assignments and understand the material. Awesome school though, would do it again.
Serious question, from someone who hasn't had to do it: how is it possible to do productively study for 16 hours a day? Controlled substances? I don't last that long even on occasion, much less regularly.
I only have anecdotal evidence of this, but more so than just about anywhere else, CMU as a university prides itself on a very difficult workload and a lot of the solutions that students come up with are extremely unhealthy.
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Ha! Grad school at CMU took a chainsaw to my GPA after 1st year. Studying 16h/day and still didn't have enough time to complete all assignments and understand the material. Awesome school though, would do it again.
Serious question, from someone who hasn't had to do it: how is it possible to do productively study for 16 hours a day? Controlled substances? I don't last that long even on occasion, much less regularly.
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…
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 like rn and ftp were all there were on the net.
Regression isn't that hard. Heck, most advanced Calculus and further maths are way harder.
Neural networks aren't even that complicated, although tuning and understanding the output sure is. But that comes with time.
Markov Chains are basically advanced flowcharting. If they can teach PERT analysis to Business majors, then Markov should be easily understood.
Sure, there are some hardcore things, but the foundation is based on stuff many smart people should be able to learn in undergrad. Twenty years from now, when we are surrounded by "AI"-everything, we'll wish more schools did this. And I guarantee you'll wish your kids learned this in high school.
Yes, I am serious about that last comment. I have an 8 year old son, and I'm already working on teaching him Markov Chains using sports. Yes, it's super simple, but easy enough to teach him consequences of actions, without getting into nested probabilities.
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.
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.
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> 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?
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"…
I think the real issue appears that the media has confused people as to what "ai" practically means from a compsci perspective.
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…
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Most CS programs have a lot of filler from the Math department and various other places. Your third semester of Calculus for example. So, I think their is plenty of room for 6-8+ AI focused classes which would get you reasonably far. Especially if it's a comprehensive major that covers both Major and Minor requirements.
Not sure how you're going to have someone understand gradient descent and backprop without having a strong command of multivariable calculus.
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…
https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...
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> 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?
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"…
Your definition doesn't match the real definition. Technically, a hard coded rule-based decision algorithm is a form of very basic AI. You seem to be confusing ML and AI- ML is a subset of AI that focuses on training a complex model.