“ Students are permitted to use AI assistants for all homework and programming assignments (especially as a reference for understanding any topics that seem confusing), but we strongly encourage you to complete your final submitted version of your assignment without AI. You cannot use any such assistants, or any external materials, during in-class evaluations (both the homework quizzes and the midterms and final).
The rationale behind this policy is a simple one: AI can be extremely helpful as a learning tool (and to be clear, as an actual implementation tool), but over-reliance on these systems can currently be a detriment to learning in many cases. You absolutely need to learn how to code and do other tasks using AI tools, but turning in AI-generated solutions for the relatively short assignments we give you can (at least in our current experience) ultimately lead to substantially less understanding of the material. The choice is yours on assignments, but we believe that you will ultimately perform much better on the in-class quizzes and exams if you do work through your final submitted homework solutions yourself.”
piker 4 hours ago [-]
My money is on extraordinarily poor final exam results and/or cheating.
khannn 3 hours ago [-]
In my day professors said that you'd never have an AI in your pocket
_joel 52 minutes ago [-]
but can I write 5318008 on my AI?
piker 2 hours ago [-]
True. Why even bother with school anyway?
linhns 4 hours ago [-]
This is the way it should be. AI to speed up the understanding process, and one final evaluation without any help to cement the understanding.
topherhunt 2 hours ago [-]
I don't think the final evaluation is to "cement the understanding" so much as _verify_ that students have taken accountability for their own learning process.
aanet 2 hours ago [-]
^ This
This is what a student, who truly wants to learn rather than simply complete a course / certification, would do... Use AI tools to explain + learn, but not outsource the learning process itself to the tools.
4 hours ago [-]
teleforce 51 minutes ago [-]
I hope the instructor will publish a textbook to support and accompany the course, will buy in a heartbeat.
gabrieledarrigo 6 hours ago [-]
Do you think this is a good course?
Or, what do you suggest as a structured course to learn how LLMs work?
sim04ful 7 hours ago [-]
Nothing on symbolic reasoning ?
cultofmetatron 7 hours ago [-]
I believe that would be part of whats now "classical ai"
cubefox 2 hours ago [-]
It's called GOFAI, or not AI at all. It's basically all machine learning nowadays.
leonvoss 43 minutes ago [-]
That's not AI.
xdavidliu 5 hours ago [-]
that would be the exact opposite of modern
chvid 5 hours ago [-]
No. That will be covered by the Post-modern AI course in the fall semester.
mold_aid 4 hours ago [-]
Can't wait for postmodern AI.
blackoil 2 hours ago [-]
How to flip burgers better than an AI robot!
aanet 2 hours ago [-]
:) Too true
But tbh, it'll more likely be repairing those burger flippin' robots
frankdenbow 2 hours ago [-]
thanks for sharing, these look great.
emil-lp 8 hours ago [-]
[flagged]
small_model 8 hours ago [-]
Well it's the dominant and most successful implemented AI, would a comp sci course teach every failed computer architecture or focus on the ones that are in wide use today.
gield 4 hours ago [-]
Your analogy to computer architectures doesn't make sense, unless comparing GPT-like LLMs to different LLM architectures like Mamba or RWKV. It indeed wouldn't make sense to not teach about Mamba or RWKV in an introductory AI or LLM course.
AI is much broader than LLMs alone. Computer vision, RL, classical ML, recommender systems, speech recognition, ... are still part of AI, just not very visible to the average consumer.
utopiah 3 hours ago [-]
> most successful implemented AI
According to what? Spent money? Number of users? Outcomes and if so which ones?
boredemployee 3 hours ago [-]
probably according to marketing and not limited to hallucination
suddenlybananas 7 hours ago [-]
I think comp sci courses focuse on fundamentals rather than what's popular. Besides, other kinds of AI are not "failures", they have plenty of uses.
smokel 7 hours ago [-]
Don't trip over words. The course offers quite a range of knowledge that is suitable outside LLMs. It's an introduction.
axseem 8 hours ago [-]
It really depends on the target audience, because a lot of people have no idea what they are using is called an LLM or that there are various types of generative AI.
gignico 8 hours ago [-]
I think the problem is the under representation of other branches of AI research: knowledge representation, automated reasoning, planning, etc.
These are important topics with important industrial applications which have the only downsides to not be suitable for implementing friendly chatbots and for raising the stocks of Silicon Valley companies.
Kaethar 7 hours ago [-]
I doubt renowned US universities don't offer courses that cover those topics.
As someone who studied in a university system where the courses you had to take were mostly set in stone (just starting to offer some electives now), I really fancy the option of being able to choose what you study as much as possible.
The AI course I took was mostly symbolic methods and some classic ML at the end. Most students were not interested at all and would've probably been more engaged studying ML directly. Too bad that wasn't an option.
cubefox 2 hours ago [-]
This is a perfectly reasonable take. It's quite outrageous that this was flagged.
jccx70 7 hours ago [-]
[dead]
aboardRat4 6 hours ago [-]
Nice to finally see the revival of Lisp and Prolog.
signa11 3 hours ago [-]
prolog in another skin is called erlang you know.
Rendered at 16:28:12 GMT+0000 (Coordinated Universal Time) with Vercel.
“ Students are permitted to use AI assistants for all homework and programming assignments (especially as a reference for understanding any topics that seem confusing), but we strongly encourage you to complete your final submitted version of your assignment without AI. You cannot use any such assistants, or any external materials, during in-class evaluations (both the homework quizzes and the midterms and final).
The rationale behind this policy is a simple one: AI can be extremely helpful as a learning tool (and to be clear, as an actual implementation tool), but over-reliance on these systems can currently be a detriment to learning in many cases. You absolutely need to learn how to code and do other tasks using AI tools, but turning in AI-generated solutions for the relatively short assignments we give you can (at least in our current experience) ultimately lead to substantially less understanding of the material. The choice is yours on assignments, but we believe that you will ultimately perform much better on the in-class quizzes and exams if you do work through your final submitted homework solutions yourself.”
This is what a student, who truly wants to learn rather than simply complete a course / certification, would do... Use AI tools to explain + learn, but not outsource the learning process itself to the tools.
But tbh, it'll more likely be repairing those burger flippin' robots
AI is much broader than LLMs alone. Computer vision, RL, classical ML, recommender systems, speech recognition, ... are still part of AI, just not very visible to the average consumer.
According to what? Spent money? Number of users? Outcomes and if so which ones?
These are important topics with important industrial applications which have the only downsides to not be suitable for implementing friendly chatbots and for raising the stocks of Silicon Valley companies.
As someone who studied in a university system where the courses you had to take were mostly set in stone (just starting to offer some electives now), I really fancy the option of being able to choose what you study as much as possible.
The AI course I took was mostly symbolic methods and some classic ML at the end. Most students were not interested at all and would've probably been more engaged studying ML directly. Too bad that wasn't an option.