I started my dive into AI in 2008 writing a Boid / Crowd system for my thesis while in art college, School of Visual Arts.
   It was an insane particle script + 3d animation cycles in Maya haha.
Then I did Boid movement, navigation, & obstacle detection in animated films for 5 years at Blue Sky Studios, using Houdini.

I dove into Style-Transfer AI & Long Short-Term Memory (LSTM) training in 2019-2020,
   Like making a Node.js server (web site) understand my voice & auto google search for me.

Since then, I've been developing different multi-media AI structures in my spare time.


In 2015 I decided I'd cram a machine learning AI into a single-board computer, a Jetson TK1, by the end of 2026.
   Something that could write down what I say,
   Use vision to understand an object simply went out of frame.
     Yet "knows" if it looks over, the object is still there; 'Attention'

At the end of 2023, this evolved into a deep learning AI crammed into, likely, a Jetson Nano.
   As something to infer what I mean, from what I say,
   Or give a "thought" on what it saw or heard in the world around it.

'Machine Learning' is AI that can learn basic patterns.
'Deep Learning' is Machine Learning,
But uses neural networks to form patterns of patterns.


Realistically, I'd just be happy to make something that can understand what I say and can give a semi coherent response without an internet connection.

As of May 24th 2025, I've started on the core of the AI,
   But still testing different structure's ability in adapting to stimuli.
   ... It really seems like any network could work for most things, but some are better than others per task.

You could guess,
All the recent AI hullabaloo (2019-...)
Has been quite serendipitous for my creation!
I'm an armchair AI researcher at best!
I'm not an authority on ai!!

These are my theories / thoughts / research on AI.

Tap the Entry Title above to open the Blog Entry List.

No LLMs here.
These are my own rambly-ass thoughts.



Neural Bundles

2025-08-02

2 - 3 min read

I've been looking into neural bundles in the brain. There is an implicit "delay" in the flow of information that I'm interested in.
   As signals move between neurons, some connections take a longer path than others to get to the same destination.
     For as much as I interpreted it.

There is 6 main layers of neurons in the cerebral cortex,
   Of these, the 4th seems to allow for delays in processing.
   The 5th layer then introduces a dense layer of pathways for the signals to travel through.
   This is where I think another form of delay is introduced.

I was comparing Mice and Wallaby brains,
   While Mice are likely more intelligent,
   Wallabies have more connections with denser pathways, it seems.

Wallabies have more glial cells within slices of the brain compared to Mice.
   But mice had more neurons in the same slices.

I'd like to believe, this doesn't mean there is a "better" brain here.
   But rather, different types of brains that are suited for different tasks.

Wallabies are known to be social animals when food is plentiful,
   Yet solitary when food is scarce.
Mice are known to be social animals,
   And have shown empathy towards other mice in distress,
   And share food with other mice when they are in need.

Why do I bring this up?
   I believe there is similar deductive reasoning, just at a different scale.
Both Wallabies and Mice are making a choice based on the environment and situation,
   While considering the well-being of others, just in different ways.

The delay in neural firing could be a factor in this.
   So I'd like to explore this in my own AI.

We all know size of the brain can determine intelligence,
   But so does the structure of the brain.

- August 2nd 2025

research, brain, structure