Re: Next generation ANNs

I concur with the idea that we need new cognitive architectures (and
topologies).

The fields of cognitive neuroscience and neuromorphic computing are
evolving so rapidly that even the most current reference textbooks become
rapidly outdated.

Are there any online resources where I can find the current
state-of-the-art literature (references) on (1) regions of the brain,
neural networks and substructures and types of cells and (2) cognitive
processes, types of memory, sensory processing and (triggered) physical
processes?

Milton Ponson
Rainbow Warriors Core Foundation
CIAMSD Institute-ICT4D Program
+2977459312
PO Box 1154, Oranjestad
Aruba, Dutch Caribbean

On Wed, Jul 9, 2025, 04:07 Dave Raggett <dsr@w3.org> wrote:

> I am on my way back from the Edge AI summer school in Pisa, where I gave a
> lecture on cognitive approaches for low-code control in swarms of digital
> twins. See:
> https://github.com/w3c/cogai/blob/master/demos/Swarms/tasks/README.md
>
> Discussion on the massive electrical demand of AI got me to look at field
> of photonics as a game changer for AI:
>
> Today’s ANNs use a lot of electrical power especially for training. This
> is creating a big demand on the national grid and acts as a downside for
> the expanded use of AI. The brain uses just a few watts, perhaps 20W, and
> is five orders of magnitude more power efficient than GPUs. The brain
> features local analog processing and spikes for long range communication.
> Can we match that?
>
> A very promising approach is to use light pulses conveyed via optical
> waveguides as a replacement for electrical conductors within and between
> integrated circuits. This is faster and uses less power. We can build upon
> the extensive experience with CMOS fabrication for hybrid photonic devices
> that combine electronic and optical components in the same chip.
>
> Silicon quantum dots (SiQD) can be used for emitting and detecting light
> in a wide range of wavelengths from the near infrared to the near
> ultraviolet. SiQDs can further be used for beam splitters and other optical
> devices. Waveguides, just nanometres across, can efficiently transport
> photons, even around tight bends, enabling a high density of communication
> paths. Resistive random access memory (RRAM) is based upon memristors, and
> can be integrated alongside CMOS electronic gates and optical waveguides.
> Memristors can also be used to mimic synapses for neuromorphic computing.
>
> Of course there are many challenges to address before this technology can
> become widespread. This includes connectivity to photonic chips compared to
> the ease of bonding wired connections. We are also likely to need new
> architectures for neural networks that feature continual learning through
> continual prediction and weight adjustments at a layer by layer basis,
> replacing today’s gradient-based back propagation. Another motivation is
> that adversarial attacks suggest that today’s AI is significantly different
> from human cognition, e.g. adding some carefully chosen noise to an image
> of a panda causes the model to misclassify it as a gibbon with a high
> degree of confidence, despite the noise being imperceptible to the human
> eye. Similar considerations apply to jail breaks that evade the alignment
> training for LLMs.
>
> In Pisa, I presented my recent work on extending chunks and rules to
> support messaging and task synchronisation. This is a symbolic approach
> that emerged well before recent advances in generative AI.  I now want to
> recast this in terms of artificial neural networks with fuzzy rules and
> explore how to integrate machine learning to mimic the complementary role
> of the cortico-basal ganglia circuit for deliberative cognition and the
> cortico-cerebellar circuit for motor skills. Before that however, I need to
> extend the chunks & rules test suite and spec to cover the recent
> extensions. As always, offers of help would be much appreciated.
>
> Dave Raggett <dsr@w3.org>
>
>
>
>