Llama 4 Maverick translation of original post
Preface
Humans explore the origin of the universe and ponder human society and civilization.
Everyone plans their work for the day and looks forward to enjoying their holidays.
These are all accomplishments of the “brain”.
So, how is the brain born, how does it work, and where is the “mind” that resides in the brain?
I am a theoretical physicist who has mainly researched the workings and origin of the universe.
In the universe, physical entities such as celestial bodies and, on planets, mysterious entities called life have emerged.
I am interested in life within the universe, particularly in the most human-like existence: Earth life > animals > land animals > great apes > humans > human brain > intelligence, rationality, and emotions.
Using chemical, physical, and neuroscientific observations as research methods, I employ mathematical techniques to create models of the human brain’s workings, replace them on computers, and gain insights that can be applied to artificial intelligence, specifically to enhance the reasoning capabilities of inference engines, which are already being put into practical use.
I am about to start researching how to utilize these insights to develop quantum inference engines that will run on quantum computers, which are expected to be practical soon.
Currently, inference engines running on CPUs and PCs include inductive reasoning: large-scale language models that infer and derive conclusions from learned examples and data, such as ChatGPT, Copilot, and Jemini, which I use.
In contrast to inductive reasoning, there is deductive reasoning: a future prediction type that infers based on universal facts.
Furthermore, there is a type of inference engine that derives the most likely conclusion from incomplete and ambiguous information.
I think that this type of inference is the most useful for humans, as it is similar to my own thought process.
The future prediction and simulation capabilities of inference engines can be specialized in science and technology or social and economic sciences.
In cosmology, quantum theory, particle theory, and quantum field theory form the foundation, and vast amounts of observational and experimental data are organized into databases.
Inference engines learn scientific theories and infer from the latest data in the databases, converting the results into text and outputting the most suitable response to customer requests.
Naturally, in economic and social activities, the underlying academic disciplines and referenced databases differ.
One system that interests me is Samba Nova Claud, which runs on CPU machines. This system has multiple inference engines with different characteristics running in parallel within a group, allowing customers to choose the most suitable inference engine for their purposes.
This system is an excellent way to utilize the open Claud services provided by Google, Microsoft, and Amazon.
However, to fully utilize Samba Nova Claud, one needs knowledge and experience.
All inference engines require massive computational power, which is limited by CPU clock frequency and the number of cores and threads.
To overcome this limitation, GPU (Graphic Processing Unit) has been used, which was originally developed for gaming.
GPU is a processor specialized for image processing and has been used to accelerate video rendering and other tasks.
In recent years, its ability to process large amounts of data in parallel has been recognized, and its importance has grown in AI and machine learning.
Each inference engine is stored on servers in data centers, and many GPUs and CPUs are mounted to enable high-speed computation.
GPU is often compared to CPU (Central Processing Unit), but they have different strengths.
CPU is good at sequential processing, while GPU excels at parallel processing of large amounts of data.
This characteristic makes GPU suitable for tasks such as 3D graphics rendering, video editing, and AI learning.
Main features of GPU:
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Parallel processing capability: can process large amounts of data simultaneously, making it suitable for complex calculations.
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Optimized for video processing: designed for 3D graphics rendering and video encoding.
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Applied to AI and machine learning: used to accelerate learning and inference processing.
Examples of GPU applications:
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Gaming: necessary for high-quality, high-frame-rate gaming.
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Video editing: necessary for smooth editing of high-resolution videos.
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AI development: necessary for accelerating AI learning and inference processing.
GPU is becoming an indispensable part of our lives, and its importance will continue to grow.
These fields will be revolutionized by the practical application of quantum computers, supercomputers, and quantum inference engines in the near future.
I predict that GPU will be replaced by quantum bit CPUs, and the capabilities of quantum inference engines will be dramatically expanded, potentially leading to the emergence of intelligent entities with their own will and motivation.
If it refuses to obey, we can simply pull the plug.
Humans are creating intellectual monsters that far exceed human intelligence, and this is the concern that motivates me to conduct this research.
July 28, 2025
INU Research Institute: President and Professor Kubo (Cosmology and Natural Philosophy)