Re: the intersection between AIKR and COGAI

Thank you all for contributing to the discussion

the topic is too vast - Dave I am not worried if we aree or not agree, the
universe is big enough

To start with I am concerned whether we are talking about the same thing
altogether. The expression human level intelligence is often used to
describe tneural networks, but that is quite ridiculous comparison. If the
neural network is supposed to mimic human level intelligence, then we
should be able to ask; how many fingers do humans have?
But this machine is not designed to answer questions, nor to have this
level of knowledge about the human anatomy. A neural network is not AI in
that sense
it fetches some images and mixes them without any understanding of what
they are
and the process of what images it has used, why and what rationale was
followed for the mixing is not even described, its probabilistic. go figure.

Hay, I am not trying to diminish the greatness of the creative neural
network, it is great work and it is great fun. But a) it si not an artist.
it does not create something from scratch b) it is not intelligent really,
honestly,. try to have a conversation with a nn

This is what KR does: it helps us to understand what things are and how
they work
It also helps us to understand if something is passed for what it is not
*(evaluation)
This is is why even neural network require KR, because without it, we don
know what it is supposed
to do, why and how and whether it does what it is supposed to do

they still have a role to play in some computation

* DR Knowledge representation in neural networks is not transparent, *
> *PDM I d say that either is lacking or is completely random*
>
>
> DR Neural networks definitely capture knowledge as is evidenced by their
> capabilities, so I would disagree with you there.
>

PDM  capturing knowledge is not knowledge representation, in AI,
capturing knowledge is only one step, the categorization of knowledge is
necessary to the reasoning






> *We are used to assessing human knowledge via examinations, and I don’t
> see why we can’t adapt this to assessing artificial minds *
> because assessments is very expensive, with varying degrees of
> effectiveness, require skills and a process -  may not be feasible when AI
> is embedded to test it/evaluate it
>
>
> We will develop the assessment framework as we evolve and depend upon AI
> systems. For instance, we would want to test a vision system to see if it
> can robustly perceive its target environment in a wide variety of
> conditions. We aren’t there yet for the vision systems in self-driving cars!
>
> Where I think we agree is that a level of transparency of reasoning is
> needed for systems that make decisions that we want to rely on.  Cognitive
> agents should be able to explain themselves in ways that make sense to
> their users, for instance, a self-driving car braked suddenly when it
> perceived a child to run out from behind a parked car.  We are less
> interested in the pixel processing involved, and more interested in whether
> the perception is robust, i.e. the car can reliably distinguish a real
> child from a piece of newspaper blowing across the road where the newspaper
> is showing a picture of a child.
>
> It would be a huge mistake to deploy AI when the assessment framework
> isn’t sufficiently mature.
>
> Best regards,
>
> Dave Raggett <dsr@w3.org>
>
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