Re: KR, Logic in a few slides

The lecture focuses on traditional logic and ignores the imprecision and context sensitivity of natural language. As a result it sounds rather dated.

The other lectures in the course:

    https://www.cs.princeton.edu/courses/archive/fall16/cos402/ <https://www.cs.princeton.edu/courses/archive/fall16/cos402/>

They cite Chomsky’s comments deriding researchers in machine learning who use purely statistical methods to produce behaviour that mimics something in the world, but who don't try to understand the meaning of that behaviour.

This of course predates recent work on large language models which demonstrate a very strong grasp of language and world knowledge, but are rather weak in respect to reasoning. The brittleness of both hand-authored knowledge and deep learning, motivates work on machine learning of reasoning for everyday knowledge.  

The last twenty years have shown that machine learning is vastly superior to hand authoring when it comes to things like speech, text and image processing. However, we have still to successfully mimic human learning and reasoning.

To get there, I believe that some hand authoring for small scale experiments can help illustrate what’s needed from more scalable approaches.  I don’t see anyone else on this list being interested, though, in helping with that. Where are the programmers and analysts when you need their help?

I disagree that the slides you linked to are effective in arguing for applying KR to ML.  You could say that ML requires a choice of KR since ML software has to operate with information in some form or other, but that is like stating the obvious.

Perhaps you are assuming that KR should use a high level theoretical formalism for knowledge? That’s wishful thinking as far as I am concerned. It certainly didn’t help when it came to former stretch goals for AI, e.g. beating chess masters at their own game.

> On 1 Nov 2022, at 13:25, Paola Di Maio <paola.dimaio@gmail.com> wrote:
> 
>  Sweet and short set of slides that speaks CS language in making the argument of KR for ML, the authors  may be instructors at Princeton and do not cite any literature, but do a good job at summarizing the main point-
> 
> Lecture 12: Knowledge Representation and
> Reasoning Part 1: Logic
> https://www.cs.princeton.edu/courses/archive/fall16/cos402/lectures/402-lec12.pdf <https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=0CAQQw7AJahcKEwi40bXYiI37AhUAAAAAHQAAAAAQAw&url=https%3A%2F%2Fwww.cs.princeton.edu%2Fcourses%2Farchive%2Ffall16%2Fcos402%2Flectures%2F402-lec12.pdf&psig=AOvVaw1dCxdvDmEmj3r5EiLn5UGV&ust=1667395111043516>
> 
> Some may appreciate the presence of a penguin illustrating the argument
> 

Dave Raggett <dsr@w3.org>