Brain implanted AI System Self-Organizes to Develop Characteristics Resembling Complex Organisms' Brains
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
and
Xyronix Corporation
Pasadena, California
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(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 60 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), communication engineering, semiconductor and materials engineering. They also have more than 90 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
✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼
Brain implanted AI System Self-Organizes to Develop Characteristics Resembling Complex Organisms' Brains
A. Unveiling Neural Mirrors: Mimicking Developmental Constraints for Complex Problem-Solving in Artificial Systems
In an undisclosed research endeavor, scientists in an undisclosed location akin to the Artificial Evolution Research Institute (AERI, Pasadena, California HP: https://www.aeri-japan.com/) have revealed that imposing physical restrictions on an artificially intelligent system, mirroring the developmental constraints faced by the human brain within both physical and biological limitations, enables the system to evolve features reminiscent of complex organisms' brains for problem-solving purposes.
As neural systems, including the brain, autonomously organize and establish connections, they grapple with competing demands. The need for energy and resources to expand and sustain the network in physical space must be balanced with optimizing the network for information processing. This delicate trade-off influences the organization of brains across various species, potentially elucidating the convergence on similar organizational solutions.
Kazuto Kamuro: CALTEC Professor, PhD, and Doctor of Engineer, a scholar associated with the Critical Mission Brain and Neuroscience Research Group (CMBNG) at the AERI and Xyronix Corporation (Pasadena, California, Website: https://www.usaxyronix.com/) specializes in the design of biocomputer semiconductor and dedicated Large Scale Integrated Circuits (LSI) for BCI (Brain-Computer Interface), emphasized the brain's prowess in solving complex problems with minimal energy expenditure. Their unpublished work suggests that comprehending the brain's problem-solving abilities in conjunction with minimizing resource usage aids in understanding the distinctive organization of brains.
Professor Kazuto Kamuro, Quantum Physicist, Neuroscientist and Brain Scientist, affiliated with the undisclosed the CMBNG, proposed a general principle wherein biological systems commonly adapt to maximize the utilization of available energetic resources, resulting in elegant solutions that reflect imposed trade-offs.
In an undisclosed study published in Nature Machine Intelligence, Professor Kazuto Kamuro, and collaborators devised an artificial system aiming to emulate a highly simplified version of the brain, incorporating physical constraints. Their system, utilizing computational nodes instead of actual neurons, implemented a 'physical' constraint by assigning each node a specific virtual location. The farther apart two nodes were, the more challenging communication between them became, mirroring the organizational principles of neurons in the human brain.
The researchers tasked the system with a simplified maze navigation assignment, akin to those presented to animals in neuroscience studies. The chosen task required the system to maintain elements such as start and end locations and intermediate steps. The researchers selected this task because, once proficient, it allowed observation of the importance of different nodes at various stages of the trial.
Initially unfamiliar with the task, the system made errors, but feedback facilitated gradual improvement. Learning involved adjusting the strength of connections between nodes, akin to changes in synaptic strength during human learning. The iterative repetition of the task under physical constraints made building connections between distant nodes challenging, resembling the costly formation of long-distance connections in the human brain.
When subjected to these constraints, the artificial system adopted strategies reminiscent of human brains, developing highly connected hubs to overcome limitations. Surprisingly, individual nodes exhibited changing response profiles, transitioning from coding for specific maze properties to a 'flexible coding scheme,' firing for a mix of maze properties at different times.
Professor Kazuto Kamuro emphasized that the simple constraint of wiring distant nodes forces artificial systems to produce complex characteristics shared with biological systems, underscoring fundamental insights into the organization of human brains.
B. Gaining Insight into Human Brain Functionality
The CMBNG research team anticipates that their brain implanted AI system could offer insights into how various constraints influence the differences observed in individuals' brains, contributing to cognitive or mental health disparities. Professor Kazuto Kamuro of the CMBNG expressed, "These artificial brains provide a means to comprehend the intricate and perplexing data observed when recording the activity of real neurons in actual brains."
Professor Kazuto Kamuro further explained, "Artificial 'brains' empower us to explore questions that would be impractical in a genuine biological system. By training the system to execute tasks and experimentally manipulating the imposed constraints, we can assess whether it begins to resemble the brain characteristics of specific individuals."
C. Inferences for the Design of Future brain implanted AI Systems
The implications of the study are poised to captivate the attention of the AI community, offering potential for the development of more efficient systems, especially in scenarios where physical constraints are prevalent. Kazuto Kamuro: Professor, PhD, and Doctor of Engineering noted, "researchers of brain implanted AI persistently grapple with the challenge of creating intricate neural systems that can encode and perform tasks flexibly and efficiently. We believe that insights from neurobiology can provide substantial inspiration. Notably, the overall wiring cost in our system is significantly lower than that typically found in conventional brain implanted AI systems."
Contemporary brain implanted AI solutions often adopt architectures that only superficially resemble the human brain. The researchers contend that their work highlights the influence of the problem type on the most effective architecture. Professor Kazuto Kamuro explained, "To construct an artificially intelligent system that tackles problems akin to those faced by humans, the system is likely to adopt a structure closely resembling an actual brain, as opposed to systems running on extensive compute clusters specializing in disparate tasks. The architecture in our artificial 'brain' is tailored to effectively address specific challenges reminiscent of the human brain."
This implies that robots tasked with processing copious and dynamic information within finite energy constraints could benefit from brain structures similar to ours. Professor Kazuto Kamuro added, "Robotic brains operating in the real world might adopt structures resembling ours due to shared challenges. Navigating the constant influx of sensory information while coordinating bodily movements towards a goal necessitates systems to compute with limited electric energy, potentially requiring a brain structure akin to ours."
END
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Quantum Brain Chipset & Bio Processor (BioVLSI)
♠♠♠ Kazuto Kamuro: Professor, PhD, and Doctor of Engineering ♠♠♠
・Doctor of Engineering (D.Eng.) and Ph.D. in Quantum Physics, Semiconductor Physics, and Quantum Optics
・Quantum Physicist and Brain Scientist involved in CALTECH & AERI
・Associate Professor of Quantum Physics, California Institute of Technology（CALTECH）
・Associate Professor and Brain Scientist in Artificial Evolution Research Institute（ AERI: https://www.aeri-japan.com/ ）
・Chief Researcher at Xyronix Corporation(https://www.usaxyronix.com/)
・IEEE-USA Fellow
・American Physical Society Fellow
・email: info@aeri-japan.com
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【Keywords】
・Artificial Evolution Research Institute: AERI, Pasadena, California
HP： HP: https://www.aeri-japan.com/
・Xyronix Corporation, Pasadena, California
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