- From: Paola Di Maio <paoladimaio10@gmail.com>
- Date: Sun, 25 Dec 2022 20:40:21 +0800
- To: Dave Raggett <dsr@w3.org>
- Cc: W3C AIKR CG <public-aikr@w3.org>
- Message-ID: <CAMXe=Sr1nXzHn2bu94VFnx5NJ8wo2iM6tX-FDxzVVD08dRayqw@mail.gmail.com>
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> > > > >
Received on Sunday, 25 December 2022 12:44:01 UTC