AI

Raspberry PI CamJam went amazing well!

At first I was quite nervous to say the least, however the nerves quickly dissipated after seeing so many enthusiastic people!
I learned my robotics design needs improvement, however the code was somewhat more advanced.

Sadly, not many were interested in the EDEN research accompanying it, oh well!
And as a nice bonus: my creation has made it to the Raspberry pi youtube channel. woo!

I will be demoing my work with the robot as a demo at ‘CamJam’ – celebrating the birthday of Raspberry Pi! this saturday (02/03/2024)

Please purchase tickets HERE!

With limited free time since the last update, I have completed the bulk of the portal to handle downloads, payments and subscription updates.

Whilst functional, there is a lot to be desired – however it more important to focus on the core project and higher value aspects of the AGI for end users.

That being:

• Training
• Examples
• Ease of use!

Therefore I am working on creating an installer that will download via the portal, and will be invite a lucky few for beta testing soon.

The goal is continue gathering feedback whilst building up examples and optimising until a future user can easily:

• Login
• signup
• Get started with ease.
• Run examples
• Feel capable of producing their own results using the available tools

I am hard at work on four mini-projects that allow EDeN to used by all!
This includes:

• .License and feature management system inside the main c++ dll
• .User management and portal for product/feature selection
• .Backend of payment processing/user management
• .Backend of subscription renual/initiation for each product selection

After these systems are working and confidence is high, I will resuming testing od EDeN against the simulation interface for various demo’s and tests of it’s functionality.
Each test will also be bundled in as an example.
As users increase, I will release updates to each version.

I’m looking forward to others playing and testing out it’s capabiltiies!
Also in the works will be a generic ic2, and other data stream interface – for real world application.

In the below video, I demonstrate various connectome rendering and GUI Improvments in conjuction with huge speedups to entity creation using TBB and .

The next stage wiill more focused towards results from training via entity behaviour.

In the below video, I demonstrate the latest update which has been on hiatus for three months due to personal issues (moving home and taking care of family):

• The client interface can now request a root folder located on the server for sourced datasets, this is now automatically loaded into memory and selected for training
• A new command structure can set position of the entity and training object for stimulating input links.
• Improved GUI allows inspection of the connectome seperately, whilst providing a ‘mini-map’ type view.
• Various bug fixes and speed up improvments in the backend. Not shown in the video is a 60x speed up in connectome rendering, especially useful for large brained entities.The results of this will then:
• Be used as evidence in the next paper : Multi agent learning -working title.
• And if stable: release to AWS Marketplace!