Hacker News Reader

Comment by kendallpark | Hacker News Reader

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."

View on HN

I made the mistake my sophomore year at CMU of enrolling in a graduate-level economics class called "Game Theory" because I got my course prefixes incorrect, and assumed it was a CS class about video games.

I am now much more versed in Nash equilibria than I ever thought I'd be, but damn, that class took a chainsaw to my GPA.

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.

I would do it again, too!

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.

All joking aside, CMU does very much have a problem with promoting and fetishizing a culture of stress.

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.

I have personal experience (EE,CE,AM '88). CMU sucks in terms of student experience - at least then it did. At least the physical plan is far superior now. The place was fugly in the 80s. The teaching was weak. I felt that I was paying for a reputation.

And on top of that, in our freshman year they had us all come to an auditorium to tell us this: "Sorry, we have to fail half of you out because there are too many of you. Look to your left and then to your right. Those two students will be gone." Any EE in that class can testify to the truthfulness of what I say.

I'm curious too. I've done controlled substances for studying and still can't do 16 hours a day.

It is not productive to study for 16h/day, or even 10h/day for an extended period. It is not only less effective for sustained learning or intellectual work than spending less but more focused time, it also leads to physical and mental health problems, and sometimes results in severe burnout.

The problem is that (a) students are young and many of them are quite inexperienced with managing their own time and work, (b) students are so stressed and sleep deprived that it is hard to introspect about process or get into a productive rhythm of focused productive work alternated with rest, and (c) there is often a workaholic student culture which creates peer pressure and presents the illusion that staring at a textbook for hours while already half asleep is the mark of a good student. For grad students (especially foreign students) sometimes there is additional pressure from abusive advisors.

It’s sad that e.g. MIT’s unofficial motto is “I hate this fucking place”.

Unfortunately the same kind of culture extends into some people’s professional lives. I dated a lawyer for a while who was a few years out of law school and working for a big firm, and with all the hours she needed to “work” and the few hours she could sleep each night her ability to think through complicated legal arguments or write briefs was severely compromised; sometimes she would be “reading” for an hour before bed with her eyes half closed, barely able to parse the words on the page. But that’s what the firm expected (and by their standards she was performing well), so she felt she had no choice.

Everyone takes Adderall. Not just that, but a lot of the time is spent explaining things to your classmates, getting answers to match our expectations, etc.

So it's not like 16 hours of reading and trying to understand the material, more like 16 hours of school work.

I've always wondered: is the information retained successfully in the long term even if acquired using Adderall?

I have taken Adderall for a few months, and I developed serious memory gaps. I have little recollection of several events during that time.

What's it like with Adderall? Does it impact your sleep cycle, or just allow you to concentrate?

I did the same thing as a grad student in chemical engineering — I thought it would be fun to take graduate-level quantum field theory as an elective. Despite the blow to my GPA, I really enjoyed the course and don't regret taking it at all.

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.

Looking at the curriculum CMU has put together (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).

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.

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.

Only referring to my experience as an undergrad: most students seemed to be turned away by the mathematics.

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 learning about SVMs. The new guy did not, but he'll be have a checklist of when to use an SVM and when not to, and be pretty good at python. So, in a way, it's deflating the intellectual rigor of the field even further, considering CMU's reputation. Of course, they think it's beneficial to their CS program for whatever set of reasonable reasons we can likely guess at.

How many people really need to know functional analysis to practice machine learning? How many developers need to know TCP coming out of school?

You're reading that into my post. Most do not need to know functional analysis. Some absolutely should. My point is not that one needs to know functional analysis to get a machine learning job, its that one of the top AI institutions is decreasing the intellectual rigor of its "average output" in that domain.

I find it scary that you're asking the second question. I think any accredited university handing out CS diplomas should make sure their graduates know what TCP is, especially CMU, which will theoretically be sending its graduates to good companies

> candidates who don’t know what TCP is or what context switch means.

Nope, look at the curriculum again.

Are you trolling?

I did my undergrad in CS at CMU, and have first-hand experience of what’s covered in the core courses, which are also requirements for this new program.

Perhaps you should take a look at the curriculum again like I told you, instead of spewing out falsehoods like “churning out candidates who don’t know what tcp is”.

You’re not entitled to your own facts.

https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...

I see an Introduction to Computer Systems course which looks like the only thing that could potentially teach networking, but from looking at the curriculum, it does not. Can you please find the course on this list that teaches networking, even if it isn't in-depth?

15-213 Introduction to Computer Systems is the one. Anyone who passed that class knows what context switching means, and what TCP is.

Whether they still remember it many years down the line is a different matter :)

I stand corrected; there is a lecture referencing networking that I missed upon first glance

At CMU you took no courses in operating systems? Algorithms? Computer hardware or logic? Compilers? Graphics? Databases? Web programming? Distributed systems? Networks? Parallel/HPC? Language theory? Security/crypto?

Because these students will take none of these courses, they will differ significantly from those with a BS in CS. But their AI skills still won't run deep enough to make them expert there either. At best, they'll be conversant with a couple of foci in AI, but not in many other AI areas.

In fact, this program seems custom made to prep for work most typical at Google, Facebook, Microsoft, and not that many others -- doing pattrec forms of ML on large data. Yet they'll lack the skills typical of today's data engineers (basic ML plus HPC/distributed/throughput, networking, and DB /sys admin) or typical of data scientists (nasic ML with a BA in statistics, plus facility with RDBMSes).

Will the absence of these CS skills hamper their competitiveness one day in most mainstream general computing software jobs? I think it probably will.

Therefore, if those with this degree don't spend their entire careers working only in big data areas of AI, they will likely will be at a competitive disadvantage to those with broader skills in CS.

> At CMU you took no courses in operating systems? Algorithms? Computer hardware or logic? Compilers? Graphics? Databases? Web programming? Distributed systems? Networks? Parallel/HPC? Language theory? Security/crypto?

The core that's required in both programs (15-122, 15-128, 15-150, 15-210, 15-213, and 15-251) is very broad and touches pretty much all of those areas. To be clear, hardware design isn't covered there, but the (x86-64) programmer's side of memory management and the CPU is covered well.

Other than algorithms, dedicated courses in all of those areas are offered as electives and you pick some of them. I recall taking OS, security, digital design / RTL (which was actually in the ECE department), web, and logic - but I could have subbed OS with Parallel/HPC, for example. The BS in CS curriculum[1] requires enough free and area electives that students gain depth in several of those areas.

> Because these students will take none of these courses, they will differ significantly from those with a BS in CS.

The BS in AI curriculum[2] only requires two CS-wide electives, so students in that program will indeed have depth in fewer of the areas. This is why these students will receive BS in AI degrees, to differentiate them from those who receive BS in CS degrees. I think you're in agreement with CMU's decision here?

That said, with the broad base of the core classes like 15-213 and the second half of 15-210, plus implementation details covered in the AI/ML courses, I'm sure no graduate of that program would struggle with HPC, networking, or DB/sysadmin in the workplace, or in a graduate program in AI.

Ultimately, there's only so much you can fit into four years, but I'd bet it would be easier for someone from this new program to deepen their skills in those areas, than it would be for most BS in CS graduates to add ML skills.

[1] https://csd.cs.cmu.edu/academic/undergraduate/bachelors-curr... [2] https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...

Having graduated with the CMU CS undergrad: if I could go back in time and replace about 80% of the language theory classes that were mandated by the curriculum with regression and machine learning modeling classes, hell yes I would.

(And this is coming from someone who TA'd one of those language theory classes ;) ).

That I will agree with!

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

So in CMU it's quite easy to get into grad classes and you can start doing it as a freshman - there are generally a number of juniors+ in masters/phd classes

Couldn't the same be accomplished with a minor or concentration as is common in many universities?

(Registering formally for a minor gives you preference)

I don't really have an issue with this degree, but I think it's mostly a marketing ploy to have a "major in AI" versus "AI concentration" or "AI minor" (set of electives alongside the normal CS degree)

What's the "ploy"? It's got about 4-6 more on-focus classes than a traditional "minor"

> "Hey, we are making it a priority that undergraduates have access to our rich AI curriculum."

Maybe these AI classes will be reserved for AI majors, so regular CS majors still have to pray for getting off the waitlist.

This. I think people forget that majors are also an operational consideration.

The problem with CS education in general is that it's very hard to get teachers, since anyone who can teach CS could earn 2-100x the income in industry.

Depends on the school. At a good school you can generally always find the classes to learn what you want to learn.

Reply on news.ycombinator.com