Nonlinear Dynamics

New website!

Nonlinear Dynamics — Artificial General Intelligence Research Nonlinear Dynamics is a research company pursuing the creation of true Artificial General Intelligence. Founded by Ronald Stesiak.

Officially obtained a renewed Nvidia Inception Program grant for 2023; sponsored by the United States Air Force and Nvidia.

We are honored to be extended such an amazing opportunity.

https://www.airforce.com

Join NVIDIA Inception for Startups Join NVIDIA Inception’s global network of over 13,000 technology startups.

It appears I need to re-apply to the Nvidia Inception Program as my grant has expired. While on the surface this seems like horrible news, in academia grants typically expire after one year. I shall re-apply and see what awaits! I am not deterred!

We have acquired more nvidia GPUs inspite of the shortage including:

RTX 3080ti
RTX 3060ti
RTX A4000
RTX A2000

We are ready to resume research infused with this new hardware and hopeful grant extension.

NVIDIA: World Leader in Artificial Intelligence Computing We power the world’s fastest supercomputers.

Hoping to get extension to prestigious Nvidia Inception Program……

Stay tuned and join us on this amazing journey!

We are upgrading our old slow, power hungry workstations to liquid cooled small form factor: very fast AI local processing via Nvidia Ampere GPU, and a 10G fiber connection to our AI cluster.

This should reduce our carbon footprint. Every little bit helps! More urgently, this equipment will allow us to process more complex algorithms and greatly increase ability to perform 5x more proof of concept modeling in general; allowing us to much more quickly test our theories as we go.

We hope to have a meaningful early stage AI by the 3rd quarter of 2022.

cases from:
www.louqe.com

This is a very odd update:

Long story short, early benchmarking results are actually showing the gaming gpu, nvidia RTX 3080 TI is literally *twice* as fast as the same ampere generation RTX A4000.

Go figure! Literally!

Just an fyi for any scientific colleagues. The only downside is the gaming card occupies two slots air cooled, consumes a lot more power, and has 12GB vram vs the 16GB in the A4000.

https://www.nvidia.com/en-us/design-visualization/rtx-a4000/

https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/

NVIDIA RTX A4000 Graphics Card Engineer Next-Generation Products, Design Cityscapes of the Future.

With our transition to nvidia’s ampere platform, we hope to connect the various regions of our AI together into a larger and more complex model; not unlike the different regions of the brain.

With this work, the AI will be able to interact verbally and visually with objects it “sees” from its “eyes” (video camera ), as well as recognize people who are “close” to “him”. when finished, he should be at the level of a toddler.

At this point, if you’ve read this far, I welcome suggestions on what to name our AI!?

Reply back to the post with names!

Upgraded GPUs to nvidia Ampere technology; allowing us to solve problems and perform machine learning algorithms significantly faster!

NVIDIA: World Leader in Artificial Intelligence Computing We power the world’s fastest supercomputers.

Here is a still screen shot taken after just a few minutes of the AI trying to figure out and “see” the man in the live video feed on the left; with a beta parameter adjustment window in the middle, and most compellingly, the video the AI “sees” while “thinking” about the contents of the video. I apologize for the poor cropping but I wanted to get the only copy of a recent session I had out into
the world.
At this point, main features are easily observable as well as the notion it is of an adult Caucasian male.

In the coming weeks, I look
Forward to posting live video sessions as Phase I evolves and Phases II and III as described in my previous post. Stay Tuned!

If you’re interested in this research; and at a minimum are performing graduate level to post-graduate level work/research, I welcome potential volunteers to collaborate with. I have an nVidia grant as well as won various NSF super computer grants, and most compellingly a specialized lab with a rack of computers and video feed dedicated to this project. Just send a message using the the long blue “send message” button at top of page and we can at least see if we can work together. This is a very rare and fascinating opportunity!

We have an alpha stage model successfully running in real time; which takes your portrait into it’s mind via live color video feed (you are encouraged to look around, turn your head, etc to give the AI more data) and then processes it much like we do and then near simultaneously, in a second video frame it outputs what and how it has *learned* to see your live video portrait in it’s surrounding world. To briefly explain, when you first “look” at our AI, a very blurry picture in the right frame appears… but it “learns” fairly quickly and the blurs and splashes of color start to resemble you! It is the differences in your actual portrait to what it “sees and processes” that are the most fascinating!

I intend on uploading a recorded live session in the weeks to follow… stay tuned!

More detailed background for those interested in reading further:

We have a proof of concept model actively running in real time which takes color video as input, and first passes it through a proprietary GAN network which is modeled after the human visual cortex. From there, images or video the AI wants to remember are then stored in another proprietary LSTM (Long Term Short Term [memory]) neural net which at this time is loosely modeled after human short term memory in the neocortex. If the AI “likes” and “approves” what it sees, the video is automatically stored in the AI’s long term memory (also stored in the LSTM).

At this moment, the neural nets are first trained with 400 preprocessed still images of people with varying ages and ethnic backgrounds as well as some wearing glasses and others wearing hats to give the AI a starting “long term memory”. We next perform testing with only 100 images. A starting set of only 500 training and test images is considered very good progress and speed as most commercial, very large “weak” or non-conscious AI’s require literally millions and sometimes more than millions of training images as compared to our 500.

NEXT PHASE aka PHASE II:
Our next goal is very large: to provide absolutely zero training or test images and allow the AI to truly *learn* about first the main people “taking care of it” much like an infant learns who is mommy and who is daddy…. We are very excited about the direction our work is heading and thoroughly enjoy the work we do!

PHASE III:
To model the audio portion of the human brain and start to learn words and sounds and associate those words and sounds with live video sessions. Our hope
Is the AI will learn who “mommy” and “daddy” are and any other people it encounters and then verbally say what/whom it “thinks” it sees. Very exciting research.

If you’ve read this far, and at a minimum are performing graduate level to post-graduate level work/research, I welcome potential volunteers to collaborate with. I have an nVidia grant as well as won various NSF super computer grants. Please just send a message and we can at least see if we are a match. This is a very rare and fascinating opportunity!

High Level Architecture of a GAN