
Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence - e15ctr0n
https://www.cs.cmu.edu/news/carnegie-mellon-launches-undergraduate-degree-artificial-intelligence
======
jknoepfler
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.

~~~
habosa
This is a lie that I hear a lot from AI/ML experts. To me it reeks of
gatekeeping. Yes the foundations of the field and many of the breakthroughs
require this knowledge, but one can be a very effective practicioner of AI/ML
techniques with a few years of undergraduate level instruction. And given how
many industries are kicking the tires of AI/ML, we're going to need hordes of
practicioners.

~~~
jazoom
I can't create a new programming language that compiles to machine code. Maybe
I shouldn't be programming?

I don't see why you need to be a domain expert of the low level details to
make a career out of something so useful.

~~~
seanmcdirmid
I don't see why you would need a graduate degree to create a new PL that
compiles to machine code. In fact, many successful PL designers lack PhDs.

Most work in CS feeds more on experience than education. A PhD is simply a way
of getting deep experience (and advice) in areas that a job probably wouldn't
allow for. Since ML is hot, you can get hands on experience with it these
days, its not like 10 years ago when the only people looking at deep learning
were pretty much researchers, PhDs, and PhD students.

------
kendallpark
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
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.

~~~
gowld
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.

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

------
anonu
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?

~~~
xxpor
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
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.

------
wgyn
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
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
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.

------
learc83
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".

~~~
epicureanideal
Well, that's basically what Computer Science degrees started out as, right? An
Electrical Engineering degree with a bunch of software related electives? Even
now, at UC Berkeley for example, "EE/CS" majors can choose a set of courses
that end up almost exactly matching what the "CS" majors take, with not
necessarily more EE. It's really just a narrowing of the electives.

There are enough AI related courses available now at many schools that it
seems useful to separate "more general Computer Science" from "a focus on
Artificial Intelligence", and similarly I think there's room for a separate
major in "Software Engineering" as opposed to theoretical computer science.

~~~
mlazos
For me this just looks equivalent to adding a new major for every sub field of
electrical engineering. Signal processing, digital circuits, analog circuits
... truth is a CS major could go take the same classes and a computer
engineering student could also.

~~~
epicureanideal
I think there's just a (subjective) point where a field is different enough
that it's worth distinguishing from the rest. I could see Signal Processing
being a different major than Electrical Engineering, and digital circuits is
basically Computer Engineering.

------
scott_s
I think I now know how the electrical engineers felt in the '50s and '60s.

~~~
komali2
What do you mean?

~~~
cjoelrun
I think he's referencing the beginning of "Computer Science" degrees.
Computers were researched by electrical engineers and mathematicians before.

~~~
Animats
Yes. My undergraduate diploma says "Electrical Engineering - Computer
Science". Computer science wasn't a full department yet.

~~~
osteele
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.

------
majormajor
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
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
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.

------
triska
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.

~~~
joshuamorton
>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.

~~~
mepian
Is recognizing happy faces the only use for AI? Who redefined AI to solely
mean Machine Learning and excluded everything else that defined it before the
AI winter?

~~~
joshuamorton
No, but if you want to solve certain useful problems, ml works and expert
systems don't. That's what I was getting at.

------
KSS42
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...](http://engsci.utoronto.ca/explore_our_program/majors/machine-
intelligence/)

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
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.

~~~
Rescis
I'm currently about to enter my freshman year of College, and have been
looking at majoring in Cognitive Science. What were your thoughts on it/it's
applicability to the rest of your life?

~~~
zukzuk
I think it very much depends on where you're doing it. There are many
approaches to cog sci, so your experience will likely be different depending
on your profs, their schools of thought, and the kind of research being done
at your institution. U of T at the time was dominated by people doing work in
embodied cognition (e.g. Evan Thompson), neo-continentalism / phenomenology,
philosophy of mind, and a few dynamical systems people. I very much enjoyed
it, but I later learned that this was a rather unorthodox take on cognitive
science, not at all representative of how things are done elsewhere. I'd stay
away from programs too rooted in more traditional experimental cognitive
psychology, or developmental psychology. To me this seems incredibly dry, but
I guess it depends on your own personal proclivities.

------
harveynick
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.

~~~
iamcasen
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.

~~~
dwcnnnghm
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)

~~~
harveynick
Oh cool, what year are you in?

If it's 3rd or 4th I was one of the judges at your systems design practical.

------
joshuamorton
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
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
I'm getting a panic attack just reminiscing about it.

213 and 251, the twin terrors.

------
mkirklions
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
> 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?

~~~
ItsMe000001
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.

~~~
asdsa5325
> To me "AI" means it learns by itself

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.

~~~
ItsMe000001
No I'm not confused, as I said, I took the courses. I know what _is_. I'm just
saying that I don't see this as "AI" at all and why. I don't really care that
someone decided to define what is possible now as "AI" for whatever reason
because I don't agree and don't see that as reasonable. It's not like those
"definitions" are laws either, ask around, even among professionals, and you
get as many different definitions as you want. Therefore I prefer to use a
"common sense expectation/interpretation" approach, and "common sense" here to
me means coming from above (where we want to go), not from below (what we have
thus far achieved).

------
bertjk
"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."

~~~
FractalLP
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.

------
inputcoffee
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/curricu...](https://www.cs.cmu.edu/bs-in-artificial-
intelligence/curriculum)

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.

------
greesil
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.

------
jimbokun
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
As of recently, CMU had a Human-Computer Interaction track, which I know a lot
of HSS students took.

~~~
aroman
There's an entire second major (and minor) in HCI, now :-)

------
epmaybe
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
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/curricu...](https://www.cs.cmu.edu/bs-in-artificial-
intelligence/curriculum)

~~~
dofly
Thanks. I was wondering about that. Do you know, by chance, what books or
notes they might use?

~~~
epicureanideal
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)

------
bitxbitxbitcoin
That... is a cool degree name.

Still waiting for CMU to launch an Undergraduate Degree in Digital Currency,
though.

~~~
deepreader
lol. Undergraduate Degree in what-so-ever-buzzwords.

------
a-dub
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
The help button for the Google Cloud for Education Administrators Console is
made by PhDs.

------
mooneater
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.

------
nopinsight
Related: China has just published the first AI textbook for high-school
students.

[http://www.scmp.com/tech/china-tech/article/2144396/china-
lo...](http://www.scmp.com/tech/china-tech/article/2144396/china-looks-school-
kids-win-global-ai-race)

------
philjohn
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.

------
avelis
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.

------
skate22
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

------
Vinnl
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.

------
Upvoter33
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.

------
sp332
What did they call it before? As the article mentions, CMU has been doing AI
for a long time.

~~~
dgacmu
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-
curr...](https://csd.cs.cmu.edu/academic/undergraduate/bachelors-curriculum-
admitted-2017)

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/curricu...](https://www.cs.cmu.edu/bs-in-artificial-
intelligence/curriculum)

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
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).

------
bytematic
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
My traditional CS curriculum had ethics as the capstone. Do most CS programs
not do this?

~~~
fixermark
I do not think they do.

------
camdenreslink
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.

------
jcranmer
Looking at the course list ([https://www.cs.cmu.edu/bs-in-artificial-
intelligence/curricu...](https://www.cs.cmu.edu/bs-in-artificial-
intelligence/curriculum)), 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
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
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).

------
lawrenceyan
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.

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Rainymood
Has been standard in Europe for quite some years now to see an undergraduate
degree in AI ... weird seeing the US lag in this.

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rajacombinator
Reputable academic institutions should not get caught up in passing trends, if
they want to stay reputable.

~~~
pishpash
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.

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uptownfunk
It seems similar to when universities started spinning out stats degrees as
separate from math

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rlanday
Which university is going to be the first to start offering blockchain
degrees?

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michaelsjoeberg
>include a strong emphasis on ethics and social responsibility

~~~
adjkant
This should be for all CS majors, not just those in AI

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bcatanzaro
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.

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yeison
That is so awesome. I think it is very valid to do so.

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dqpb
> _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.

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huffer
so... what will we call a graduate holding such a degree?

~~~
adjkant
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.

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graycat
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.

~~~
throwawayjava
Whenever it makes sense to ground research in practice, CMU professors
generally do so by working with industry and govt collaborators. Many CMU CS
professors also do some paid consulting on the side.

However, top tier PhD programs are not and never will be highly discounted
consulting shops. At places like CMU grad students have perhaps more academic
freedom than even their advisors. And good thing.

The day CMUs of the world become "Accenture with cheap student labor" is the
day basic research dies.

~~~
gowld
What is evil about non-basic research?

~~~
throwawayjava
Nothing. Why are you asking this here?

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crazy_monkey
Will students do better than random on exams?

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imranq
Is CMU trying to compete with Udacity? I personally couldn’t take a degree in
AI seriously.

~~~
epmaybe
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
I took the Udacity Deep Learning Nanodegree after a masters in CS. It was
really fun, and I would highly recommend it.

