Re: cogAI vs AI KR characterization

See inline ...

> On 5 Feb 2021, at 21:08, Paola Di Maio <paoladimaio10@gmail.com> wrote:
> 
> Hi Dave
> I am cc ing the lists, because this exchange is part of the discourse in our respective CGs and relates
> to a post to the lists
> 
> well, I do study the child mind, but not with CogAI
> 
> Because it is not yet clear what CogAI does in relation to other approaches
> (the slide aims to  help clarify)
> 
> so, is the method of COGAI (as your emails suggest) mimicking? is there a reference for that?

The idea is to match or improve upon human performance through executable software implementations of functional models inspired by observations and theories of human behaviour, especially that of children.

> 
> you write
>  If we can successfully reproduce how the best people reason,....
> 
> how does COGAI defin best people ?

That would depend on what you’re looking for. One metric is how good people are at passing exams.

Human-like AI is perhaps a better term than Cognitive AI as it makes it immediately clear that the focus is on mimicking human abilities.

> 
>> 
>> an afterthought
>> 
>> in respect to mimicking how humans reason and communicate well, 
>> each human  is different, we can generalize up to a point
>> 
>> and mimicking may result in some kind of parrot engineering ....
>> useful to start with but nowhere near intelligence at its best 
> 
> You’re missing the big picture.  If we can successfully reproduce how the best people reason, we will be in a strong position to improve on that by going beyond the limits of the human brain. The more we understand, the further and faster we can go. This is an evolutionary path that will go very much faster than biological evolution. At the same time we can make AI safe by ensuring that it is transparent, collaborative and embodies the best of human values.
> 
> Human-like AI will succeed where logic based approaches have struggled. 500 million years of evolution is not to be dismissed so easily.
> 
> I remember the enthusiastic claims around “5th generation computer systems” and logic programming at the start of the 1980’s, and had plenty of fun with the prolog language. However, the promise of logic programming fizzled out. Today, 40 years on, much of the focus of work on knowledge representation is still closely coupled to the mathematical model of logic, and this is holding us all back. We need to step away and exploit the progress in the cognitive sciences.
> 
> I am especially impressed by how young children effortlessly learn language, given the complexity of language, and the difficulties that adult learners face when learning second languages. Another amazing opportunity is to understand how some children are so much better than others when it comes to demanding subjects like science and mathematics. Moreover, warm empathic AI will depend on understanding how children acquire social skills.
> 
> Let’s lift up our eyes to the big picture for human-like AI.
> 
> Dave Raggett <dsr@w3.org <mailto:dsr@w3.org>> http://www.w3.org/People/Raggett <http://www.w3.org/People/Raggett>
> W3C Data Activity Lead & W3C champion for the Web of things 
> 
> 
> 
> 

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