Re: the intersection between AIKR and COGAI

Thank you Dave, I hope we can address these issues during a panel discussion
There is work to be done

DR Knowledge representation in neural networks is not transparent,
*PDM I d say that either is lacking or is completely random*

DR but that is also the case for human brains.
*PDM I d say that it is implici/.tacit*

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

This is what I d like to talk about, I may tweak my contribution to the
agenda as I have forgotten to mention some things, will email you separate
PDM


On Thu, Oct 27, 2022 at 5:39 PM Dave Raggett <dsr@w3.org> wrote:

>
> On 27 Oct 2022, at 00:30, Paola Di Maio <paoladimaio10@gmail.com> wrote:
>
> I am suggesting that In order to evaluate and replicate /reproduce the
> (very fun) image outcome of Stable Diffusion he shared
> https://huggingface.co/spaces/stabilityai/stable-diffusion
> we need to know the exact prompt, we also need to repeat the prompt
> several times and find out whether the outcome is reproducible (I have been
> unable to reproduce dave's images- dave, pls send the prompts for us to
> play with)
> ...
> This brings me to the point that KR is absolutely necessary to
> understand/evaluate/reverse engineer/reproduce * that means make
> reliable *any type of reasoning in AI (*cogAI or otherwise)
> Avoiding KR is resulting in scaring absurdity in machine learning,
> (becauses of  its power to distort reality, depart from truth).
>
>
> [dropping CogAI to avoid cross posting]
>
> Please note that text to image generators won’t generate the same image
> for a given prompt unless you also use the same seed for the random number
> generator, and furthermore use the exact same implementation and run-time
> options.
>
> The knowledge is embedded in the weights for the neural network models,
> which comes to around 4GB for Stable Diffusion.  Based upon the images
> generated, it is clear that these models capture a huge range of visual
> knowledge about the world.  Likewise, the mistakes made show the
> limitations of this knowledge, e.g. as in the following image that weirdly
> merges two torsos whilst otherwise doing a good job with details of the
> human form.
>
>
> Other examples include errors with the number of fingers on each hand.
> These errors show that the machine learning algorithm has failed to acquire
> higher level knowledge that would allow the generator to avoid such
> mistakes. How can the machine learning process be improved to acquire
> higher level knowledge?
>
> My hunch is that this is feasible with richer neural network architectures
> plus additional human guidance that encourages the agent to generalise
> across a broader range of images, e.g. to learn about the human form and
> how we have five fingers and five toes. A richer approach would allow the
> agent to understand  and describe images at a deeper level. An open
> question is whether this would benefit from explicit taxonomic knowledge as
> a prior, and how that could be provided. I expect all this would involve
> neural network architectures designed for multi-step inferences,
> syntagmatic and paradigmatic learning.
>
> Knowledge representation in neural networks is not transparent, but that
> is also the case for human brains. We are used to assessing human knowledge
> via examinations, and I don’t see why we can’t adapt this to assessing
> artificial minds. That presumes the means to use language to test how well
> agents understand test images, and likewise, to test the images they
> generate to check for the absence of errors.
>
> I don’t think we should limit ourselves to AI based upon manually authored
> explicit symbolic knowledge. However, we can get inspiration for improved
> neural networks from experiments with symbolic approaches.
>
> Best regards,
>
> Dave Raggett <dsr@w3.org>
>
>
>
>