Towards autonomous dexterity

Powered by symbolic reasoning and machine learning

Towards
autonomous dexterity

Powered by symbolic reasoning and machine learning

Trained by humans

on large diverse real life datasets

  An ER (Emergent Reality) kit will supercharge humans with the ability to train Eli over the internet, through a latency compensation system. It is well known that neural networks simply love massive amounts of diverse high-quality data.

We use machine learning and symbolic algorithms to map inverse kinematics or forward kinematics human poses. The decision tree will also use computer vision in conjunction with mmWave sensors and others, such as MEMS, capacitive, resitive or inductive sensors.

Make simple things simple, and complex things possible.

Alan Kay

  Human trainers can live anywhere in the world as long as the round trip internet latency between themselves and Eli does not exceed 600ms - the latency compensation limit of our VR system.



Training takes place in VR, in a reconstructed 3D scene - a game where they can choose the dexterity task they will perform. Payment will be made in real time, over a streaming payment channel in the Lighting Network.

Trained by humans

Brainpower

Scarcest resource

...love, too...

Brainpower

the scarcest resource

...love, too...

Unprecedented scaling capabilities

machines building machines...

Unprecedented
scaling capabilities

machines building machines...

...and collaborate with other machines

robot to robot
collaboration

...and collaborate
with other machines

robot to robot
collaboration

Scarcity can be fundamentally managed by human creativity.

Scarcity can be fundamentally managed by human creativity.

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