人工進化研究所（AERI）
- 3月18日
- 読了時間: 6分

Exploring Novel Principles in Brain-Inspired Learning for Advancements in Artificial Intelligence

Professor Kamuro's near-future science predictions

Exploring Novel Principles in Brain-Inspired Learning for Advancements in Artificial Intelligence

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics,

California Institute of Technology

IEEE-USA Fellow

American Physical Society-USA Fellow

PhD. & Dr. Kazuto Kamuro

AERI：Artificial Evolution Research Institute

Pasadena, California

HP: https://www.aeri-japan.com/

and

Xyronix Corporation

Pasadena, California

HP: https://www.usaxyronix.com/

Foreword

A. Professor Kamuro's near-future science predictions, provided by CALTECH professor Kazuto Kamuro(Doctor of Engineering (D.Eng.) and Ph.D. in Quantum Physics, Semiconductor Physics, and Quantum Optics), Chief Researcher at the Artificial Evolution Research Institute (AERI, https://www.aeri-japan.com/) and Xyronix Corporation(specializing in the design of a. Neural Connection LSI, b. BCI LSI（Brain-Computer Interface LSI） (Large Scale Integrated Circuits) , and c. bio-computer semiconductor technology that directly connects bio-semiconductors, serving as neural connectors, to the brain's nerves at the nano scale, https://www.usaxyronix.com/), are based on research and development achievements in cutting-edge fields such as quantum physics, biophysics, neuroscience, artificial brain studies, intelligent biocomputing, next-generation technologies, quantum semiconductors, satellite optoelectronics, quantum optics, quantum computing science, brain computing science, nano-sized semiconductors, ultra-large-scale integration engineering, non-destructive testing, lifespan prediction engineering, ultra-short pulses, and high-power laser science.

The Artificial Evolution Research Institute (AERI) and Xyronix Corporation employ over 160 individuals with Ph.D.s in quantum brain science, quantum neurology, quantum cognitive science, molecular biology, electronic and electrical engineering, applied physics, information technology (IT), data science, communication engineering, semiconductor and materials engineering. They also have more than 190 individuals with doctoral degrees in engineering and over 230 engineers, including those specializing in software, network, and system engineering, as well as programmers, dedicated to advancing research and development.

Building on the outcomes in unexplored and extreme territories within these advanced research domains, AERI and Xyronix Corporation aim to provide opportunities for postgraduate researchers in engineering disciplines. Through achievements in areas such as the 6th generation computer, nuclear deterrence, military unmanned systems, missile defense, renewable and clean energy, climate change mitigation, environmental conservation, Green Transformation (GX), and national resilience, the primary objective is to furnish scholars with genuine opportunities for learning and discovery. The overarching goal is to transform them from 'reeds that have just begun to take a step as reeds capable of thinking' into 'reeds that think, act, and relentlessly pursue growth.' This initiative aims to impart a guiding philosophy for complete metamorphosis and to provide guidance for venturing into unexplored and extreme territories, aspiring to fulfill the role of pioneers in this new era.

B. In the cutting-edge research domain, the Artificial Evolution Research Institute (AERI) and Xyronix Corporation have made notable advancements in various fields. Some examples include:

1. AERI･HEL (Petawatt-class Ultra-High Power Terawatt-class Ultra-High Power

Femtosecond Laser)

◦ Petawatt-class ultra-high power terawatt-class ultra-short pulse laser (AERI･HEL)

2. 6th Generation Computer&Computing

◦ Consciousness-driven Bio-Computer

◦ Brain Implant Bio-Computer

3. Carbon-neutral AERI synthetic fuel chemical process

(Green Transformation (GX) technology)

◦ Production of synthetic fuel (LNG methanol) through CO₂ recovery system (DAC)

4. Green Synthetic Fuel Production Technology(Green Transformation (GX) technology)

◦ Carbon-neutral, carbon-recycling system-type AERI synthetic fuel chemical process

5. Direct Air Capture Technology (DAC)

◦ Carbon-neutral, carbon-recycling carbon dioxide circulation recovery system

6. Bio-LSI・Semiconductors

◦ Neural connection element directly connecting bio-semiconductors and brain nerves

on a nanoscale

◦ Brain LSI Chip Set, Bio-Computer LSI, BMI LSI, BCI LSI, Brain Computing LSI,

Brain Implant LSI

7. CHEGPG System (Closed Cycle Heat Exchange Power Generation System with

Thermal Regenerative Binary Engine)

◦ Power generation capability of Terawatt (TW), annual power generation of

　　　　10,000 TWh (terawatt-hour) class

◦ 1 to 0.01 yen/kWh, infinitely clean energy source, renewable energy source

8. Consciousness-Driven Generative Autonomous Robot

9. Brain Implemented Robot･Cybernetic Soldier

10. Generative Robot, Generative Android Army, Generative Android

11. High-Altitude Missile Initial Intercept System, Enemy Base Neutralization System,

　 Nuclear and Conventional Weapon Neutralization System, Next-Generation

　　Interception Laser System for ICBMs, Next-Generation Interception Laser System

　　　 for Combat Aircraft

12. Boost Phase, Mid-Course Phase, Terminal Phase Ballistic Missile Interception System

13. Volcanic Microseismic Laser Remote Sensing

14. Volcanic Eruption Prediction Technology, Eruption Precursor Detection System

15. Mega Earthquake Precursor and Prediction System

16. Laser Degradation Diagnosis, Non-Destructive Inspection System

　 17. Ultra-Low-Altitude Satellite, Ultra-High-Speed Moving Object

　　　 Non-Destructive Inspection System

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

Exploring Novel Principles in Brain-Inspired Learning for Advancements in Artificial Intelligence

Abstract: This research paper, presented by the Artificial Evolution Research Institute (AERI) in Pasadena, California(HP: https://www.aeri-japan.com/) and Xyronix Corporation in Pasadena, California (https://www.usaxyronix.com/), endeavors to unveil groundbreaking insights into the brain's modulation of neuron connections during artificial learning. The potential impact of these discoveries on the development of faster and more resilient artificial learning algorithms within brain networks is explored.

1. Introduction: Understanding Artificial Learning Principles

The essence of artificial learning lies in identifying components within the information-processing pipeline responsible for output errors. While widely adopted in artificial intelligence, backpropagation adjusts model parameters to minimize errors. AERI and Xyronix researchers argue that the human brain employs a distinct learning principle, surpassing current machine systems in its ability to learn new information efficiently.

2. Uncovering the Brain's Guiding Principle

Motivated by the superior learning capabilities of the human brain, the AERI and Xyronix research team embarked on a quest to reveal its fundamental guiding principle during artificial learning. Mathematical equations describing neuron behavior and synaptic connections were analyzed, leading to the identification of a fundamentally different learning principle compared to artificial neural networks. The human brain, as proposed by researchers, initially settles neuron activity into an optimal configuration before adjusting synaptic connections.

3. Efficient Human Learning: Prospective Configuration

This settling process, termed "prospective configuration," is argued to be an efficient feature of human learning. It reduces interference, preserves existing knowledge, and expedites artificial learning. The AERI laboratory report of Brain & Neuro Science Research introduces this new learning principle. Computer simulations demonstrate that models employing prospective configuration learn tasks faster and more effectively than artificial neural networks, mirroring challenges faced by animals and humans in nature.

4. Real-Life Example: Bear Fishing for Salmon

To illustrate the prospective configuration principle, a real-life example of a bear fishing for salmon is provided. In an artificial neural network model, a damaged ear affecting the bear's hearing could inaccurately result in a lack of smell, impacting the bear's ability to catch salmon. However, the animal brain's lack of sound does not interfere with the knowledge of the salmon's smell, preserving the likelihood of catching it. Mathematical theory developed by AERI and Xyronix scientists demonstrates that allowing neurons to settle into a prospective configuration reduces interference during artificial learning, surpassing artificial neural networks in various experiments.

5. Future Directions and Challenges

Lead researcher Professor Kamuro emphasizes the existing gap between abstract models performing prospective configuration and detailed knowledge of brain network anatomy. Future research aims to bridge this gap, understanding how the prospective configuration algorithm is implemented in anatomically identified cortical networks. Professor Kamuro acknowledges the slow simulation of prospective configuration on existing computers due to fundamental differences from the human brain. A call for the development of a new type of computer or dedicated brain-inspired hardware is made to implement prospective configuration rapidly and efficiently.

6. Expanding Perspectives on Brain-Inspired Learning

In conclusion, this research paper offers a comprehensive exploration of the proposed principle, shedding light on the intricacies of brain-inspired learning for the advancement of artificial intelligence. The potential implications of these findings are vast, promising to reshape the landscape of artificial learning and inspire the development of more sophisticated algorithms at the Artificial Evolution Research Institute (AERI) in Pasadena, California. The exploration into prospective configuration opens avenues for further research, pushing the boundaries of our understanding of brain-inspired learning principles and their applications in artificial intelligence.

END

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Quantum Brain Chipset & Bio Processor (BioVLSI)