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.
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.
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.
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.
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.
> most successful implemented AI
According to what? Spent money? Number of users? Outcomes and if so which ones?
probably according to marketing and not limited to hallucination
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.
Don't trip over words. The course offers quite a range of knowledge that is suitable outside LLMs. It's an introduction.
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.
This is a perfectly reasonable take. It's quite outrageous that this was flagged.
[dead]