Robot on table

CamJam success!

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!

CamJam video from the raspberry Pi official youtube channel

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EDeN Update: Prepping for early beta testing!

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
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EDeN Update: Licensing, subscription and preparing user managmenet portals

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.

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Latest GUI of EDeN Training Environment

EDeN Update: Significant client optimisation, connectome render improvements & Input Link template generation

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.

Optimisation And Connectome Rendering

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TexturePipelinePreviewHeaderImg

EDeN Update: Dataset Texture processing overview and next steps

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!

Texture dataset processing overview

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EDeN Update: Unity volumetric connectome rendering

Significant enhancements to the backend dlls, rendering methods, and other interfaces have now allowed for the following:

  • Jeffbot – Is now created by the user from the python interface client side (Or any other asset via the ‘TraniningMetaData settings)
  • Neuron renderer: Rendering 10,000 neurons, each with up to 20,000 Axons/Dendrites, this will improve drastically once again given compute shader processing!
    That’s up to 134,217,728 neurons rendered on the active entity!-Special thanks to Joel Rowney from Mjolnir software in his shader mastery!

Volumetric rendering preview

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Update: EDeN Unity engine rendering updates

Work progresses well in developing an entity training environment.
Included in the follow GIF, we see:

  • Jeffbot – a representation of test entities where input and output neural probes are attached
  • Training environment which spawns different challenges and all essential stimulus, either in the form of food or direct neural inputs
  • Neuron renderer: rendering a few hundred neurons just starting to grow from initial conditions.
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EDeN Server comes to unity!

EDeN hooks up to Unity!
Both EDeNServer EDeNCore projects previously interfaced to a python runtime through a dll.
With an experimental DLL into the lesser known Unigine engine for the server.
Now with an asynchronous (DLL Internally managed) connection from the EDeNServer, Unity is now fully compatible to host both training and inference of your EDeN entities!
– This something ML Agent through tensor flow (https://github.com/Unity-Technologies/ml-agents) has yet to achieve!

I will producing an experimental package on the unity store in the future!
For now I simply ensure both ‘EDeNServerInterfaceDLL’ and ‘EDeNDataServerNETUnityDLL.dll’ are under a Plugins folder.

This is early days for a commercial product, but I hope to build up demonstrations for all to play with sooner rather than later.

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