Re: diffusion tech

Thank you very much Dave
it will be very interesting to learn more in depth about the topic
Having read a few PhD theses, I suspect they are not the easiest of reads
But maybe, we could put together a primer, with the help of others on this
list
on the topic. I d like to do something along those lines
PDM

On Sun, Dec 25, 2022 at 7:52 PM Dave Raggett <dsr@w3.org> wrote:

>
> On 25 Dec 2022, at 01:45, Paola Di Maio <paola.dimaio@gmail.com> wrote:
>
> Good read,
> would be nice to frame diffusion in the context of KR
>
>
> https://techcrunch.com/2022/12/22/a-brief-history-of-diffusion-the-tech-at-the-heart-of-modern-image-generating-ai/
>
>
> That article doesn’t really explain how image generators actually work, as
> “de-noising” is little more than a buzzword.  Image generators learn how to
> decompose images at different levels of abstraction, e.g. at a lower level,
> using Gabor filters for modelling textures, and at a higher level by
> knowing something about typical dogs and cats.  Text prompts are mapped to
> concepts and combined with noise to stochastically generate details in an
> iterative process that diffuses constraints across the image, working in
> the space of latent semantics, making design choices all the way, before a
> final stage which takes latent semantics as instructions to generate the
> image pixels.
>
> I recommend reading Steven Derby’s Ph.D thesis as he has done extensive
> work on determining what knowledge is exposed at different layers in neural
> networks, see:
>
>
> https://pure.qub.ac.uk/en/studentTheses/interpretable-semantic-representations-from-neural-language-model
>
> I am hoping to start work next year on ways to directly manipulate latent
> semantics in artificial neural networks.  In principle, this should pave
> the way to enabling artists to work collaboratively with image generators,
> allowing the artist to make suggestions to refine a compositions in an
> iterative creative process.
>
> A bigger challenge is to introduce richer knowledge, e.g. to ensure that
> image compositions embody causal constraints, and that people have four
> fingers and a thumb on each hand! How can we combine multiple sources of
> knowledge and ways of reasoning to support that? This is likely to require
> a paradigm shift that introduces sequential reasoning and continuous
> learning, and will introduce self-awareness along the way!
>
> Dave Raggett <dsr@w3.org>
>
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>