Re: Linguistics for the Age of AI

Hi Amirouche,

> On 23 Oct 2021, at 07:04, Amirouche BOUBEKKI <amirouche@hyper.dev> wrote:
> The whole book is open-access pdf available at:
> 
>  https://direct.mit.edu/books/book/5042/Linguistics-for-the-Age-of-AI


Thanks for the pointer, which is very timely given my work on human-like natural language processing. I am taking the unpopular knowledge-rich approach, given the need for cognitive agents to understand what people mean as part of human-machine collaboration, and likewise for agents to make themselves understood.

The section (1.6.3) on unmotivated beliefs is good, as I hear some of these criticisms frequently from people who act as naysayers for work on human-like AI.

I appreciated the comment in chapter 1: 

> Domain-specific NLU successes are often criticized for not being immediately applicable to all domains (under the pressure of evaluation frameworks entrenched in statistical NLP)


We need different ways to evaluate progress on cognitive agents, ways that focus on understanding and reasoning in respect to collaborative AI.

I want to use selected examples to explore the models and algorithms needed for natural language understanding in terms of mapping a sequence of words into chunk graphs that represent the informal natural language semantics, including context dependent fuzzy concepts as blends of discrete concepts.

The knowledge bottleneck can be addressed by focusing on a) what knowledge is needed for limited applications, and b) mimicking how humans learn new words and ideas as a basis for incremental learning, and paving the way for crowd sourcing as a basis for teaching cognitive agents.

I am not optimistic about learning knowledge at scale automatically from textual resources, as that is likely to lead to a patchwork of knowledge rather than real understanding which coherently combines declarative and procedural knowledge.

An open question is the applicability of existing resources for common sense knowledge graphs, see:

 https://arxiv.org/abs/2012.11490 <https://arxiv.org/abs/2012.11490> 
 https://usc-isi-i2.github.io/ISWC20/ <https://usc-isi-i2.github.io/ISWC20/> 

Ilievski et al’s CSKG is publicly available as a tab separated value file which is about one gigabyte in size when uncompressed. That highlights the challenges for human understanding of large knowledge graphs, and points to opportunities for work on web-based tools and techniques for browsing and querying such graphs.

This is a long and complicated road to work on, but no pain, no gain as it were!

Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
W3C Data Activity Lead & W3C champion for the Web of things