Federal Vision – Nanotechnology – Neuromorphic Computing – Patents – Biorobots

Neuromorphic Chip – Microtubules – Mind Control – Consciousness Emulated on a Neuromorphic Chip – DNA for Mass Surveillance – Scalar Waves

A Federal Vision for Future Computing: A Nanotechnology-Inspired Grand Challenge

Collaborating Agencies: Department of Energy (DOE), National Science Foundation (NSF), Department of Defense (DOD), National Institute of Standards and Technology (NIST), I ntelligence Community (IC)

White House announced “A Nanotechnology-Inspired Grand Challenge”

On October 20, 2015, the White House announced “A Nanotechnology-Inspired Grand Challenge” to develop transformational computing capabilities by combining innovations in multiple scientific disciplines. The Grand Challenge addresses three Administration priorities—the National Nanotechnology Initiative (NNI), the National Strategic Computing Initiative (NSCI), and the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to:

Create a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain.

Many of these breakthroughs will require new kinds of nanoscale devices and materials integrated into three-dimensional systems. These nanotechnology innovations will have to be developed in close coordination with new computer architectures (Recombinant RNA and DNA technology – ApiJect – Vaccines with RFID microchip and Nanotechnology – GPS – Contact Tracing Surveillance – Bill Gates – 5G BioBigData).

5G Systems works with Cognitive Models, Synthetic Biology, Nanotechnology and Neuromorphic Chips – Create Biorobots and Hive Mind

Patent US20180082207A1Cognitive modeling system

The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model (Patents – From PsyOps to MindWars – Monitoring a Patient using a Global Network, e.g. Telephone Networks, Internet – Neurological Warfare).

Digital Super Intelligence has fused with Biological Intelligence turning humans into Biorobots

Patent US20190228287A1Neuromorphic chip for updating precise synaptic weight values (2018)

A neuromorphic chip includes synaptic cells including respective resistive devices, axon lines, dendrite lines and switches. The synaptic cells are connected to the axon lines and dendrite lines to form a crossbar array. The axon lines are configured to receive input data and to supply the input data to the synaptic cells. The dendrite lines are configured to receive output data and to supply the output data via one or more respective output lines. A given one of the switches is configured to connect an input terminal to one or more input lines and to changeably connect its one or more output terminals to a given one or more axon lines (Synthetic Biology – Morgellons – Patent US20030141189 – Dangerous Sequencing of RNA and DNA – Patent US20100090180A1 – Self-replicating Materials – Operating System inside Nanomachines – US20030138777 – Activation via Frequency and 5G).

Neuromorphic Computing

The fabrication of microchips is chemistry based. The structure of Neuromorphic chips is based off of neurons. Researchers are taking biological inspiration and then applying it to an inorganic system that they could use for computing. The idea was first invented by Carver Mead who founded Cal Tech’s computer science department.

People multi processing of Information and Low Energy Consumption

When people process information, people take tons of multi sensory inputs – visual, hearing, smell, sight, touch, wave or holographic inputs and more … People are processing a lot of the time without even consciously thinking about processing. People have background activity and brains are very low-energy compared to a standard microchip.

Building Neuromorphic Chip

Researchers start with standard microchip fabrication techniques. Photolithography is what computer or any microchips and electrical devices are made from today. So all they do is they coat a silicon wafer with some sort of polymer and shine UV light on it and depending on what pattern this light has been shown through – a mask, then you can get these highly patterned electrodes or whatever you’re putting down. Following that, so we have a basis of very controlled and ordered system and researchers end up putting copper posts down.

Seeds for Neuromorphic Network in the Chip

This is a specific to this process, but you can use different metals for different forms of this technology. You have a copper grid essentially. Tiny little posts and these are the seed sites that you would then put in a solution. – It’s a seed from which everything is going to grow … It’s completely synthetic and follows kind of the laws of thermodynamics where it’s taking this path based on the amount of ions in solution. Also follows quantum physics.

Each Neuromorphic Network is different

Every single neuromorphic network that researchers grow is completely different than the last. So every single one is very unique much in the way that humans brain structures would be.

Short and Long Connections lead to Short-term and Long-term Memory

The important is you have short and long connections in the network and these can lead to short-term or long-term memory and distribute an activity throughout the network. The memory is combined with the processing unit.

What makes the Short-term Memory versus the Long-term Memory?

Any overlapping wires in the network can have a junction and where these are crossing, you would have an insulating layer in between them and from the silver or the metal you build up atom by atom to form a filament between these two. There is essentially a billion per square centimeter of these connections

Analogy of storing Data and working with them

This would be more of physical training in terms of memory. No ones or zeros, researches call this analog computing. It’s like a muscle memory in a way. If it’s a short-term memory it’s kind of a thin filament and will dissolve quickly. If there’s no reinforced electrical impulse in there – Like people if we don’t have repetition it fades … Literally the exact same thing … And when you have that repetition you get a thicker filament that forms and that is more robust and will have a longer lifetime. Or when we learn something you build stronger connection to it – similar to human brain …

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