“ 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.”
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.
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.
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.
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.
> AI Policy for the AI Course
“ 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.”
Nice to finally see the revival of Lisp and Prolog.
Do you think this is a good course? Or, what do you suggest as a structured course to learn how LLMs work?
Nothing on symbolic reasoning ?
I believe that would be part of whats now "classical ai"
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Don't trip over words. The course offers quite a range of knowledge that is suitable outside LLMs. It's an introduction.
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.
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.
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.
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.
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.