Re: diffusion tech

Hello,

I know it is, hence why statistics is so pointless. AI is going no where
because of it.
The machine doesn't even understand what it is learning.
You can't even explain the model, you have to build an explainability model
just to explain what is happening in a blackbox.
Uncertainty to explain the uncertainty - that just shows how brittle and
absurd the entire process is.

Thanks,

Adeel

On Mon, 26 Dec 2022 at 17:01, Dave Raggett <dsr@w3.org> wrote:

> Neural networks is all about statistical machine learning. Have a go ...
>
> On 26 Dec 2022, at 16:22, Adeel <aahmad1811@gmail.com> wrote:
>
> Hello,
>
> Wouldn't it just be simpler to define a representation model that is
> machine-readable and let the machine do the math.
> Why bother with all the statistics. Isn't that the whole point of
> advancing AI so the machine figures things out by itself.
> After all the statistics will still reduce to logic because that is all
> the machine can interpret anyway.
> So, just work at the granular-level of logics and let the machine figure
> out the statistical invariance through the axiomatizations.
> Statistics is what adds the unnecessary complexity and which is holding
> back AI.
>
> Thanks,
>
> Adeel
>
> On Mon, 26 Dec 2022 at 12:00, Dave Raggett <dsr@w3.org> wrote:
>
>> There are plenty of resources for learning how to work with neural
>> networks, but very little on manipulation of latent semantics, so a primer
>> would be premature.
>>
>> A likely way forward will be to train a secondary network to generate
>> detailed descriptions of an image composition from the latent semantics,
>> and to then use that in reverse to apply transformations. e.g. to change
>> the colour of a given disc from green to blue and to add a yellow square.
>> One complication is that the latent semantics are spread across layers.
>> There is a loose similarity to natural language translation in which
>> involves a stack of layers than start from syntactic details, transition to
>> semantics then to syntactic details, before the word tokens for the target
>> language.
>>
>> One of the challenges for working with neural networks is the
>> mathematics, so it is not for the faint hearted!
>>
>> On 25 Dec 2022, at 12:40, Paola Di Maio <paoladimaio10@gmail.com> wrote:
>>
>> 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>
>>>
>>>
>>>
>>>
>> Dave Raggett <dsr@w3.org>
>>
>>
>>
>>
> Dave Raggett <dsr@w3.org>
>
>
>
>