Technology Discussion – A look at some of the latest technologies

Technology Discussion – A look at some of the latest technologies

Introduction

This section discusses various topics found on the web about the latest technologies (biotechnology, energy, physics, agriculture, chemistry, astronomy, brain science, quantum sensing, robotics, etc.) other than the artificial intelligence technologies that are the subject of this blog.

Intersection of Information Science and Physics

The exchange of information and energy – on Maxwell’s demon

The exchange of information and energy – on Maxwell’s demon. The exchange of information and energy, which was central to the discussion in Maxwell’s Demon, is an important concept in physics and information theory, as well as in biology and artificial intelligence. They are often closely interrelated and affect the efficiency of physical processes and information processing. Invisible information is seen as energy, and these are closely related to physical, chemical and biological brocesses.

Spin and AI algorithms

Spin and AI algorithms. Spin is a concept used in physics, particularly quantum mechanics and solid state physics, defined as Consider combining the quantum concept of spin with AI algorithms. This would be a new approach to utilising the capabilities of quantum computers to extend the capabilities of AI technologies. The field is evolving through quantum machine learning and quantum information theory, and the use of quantum phenomena, especially spin, has the potential to improve the efficiency and accuracy of AI algorithms.

Quantum Entanglement and Quantum Communication Technology

Quantum Entanglement and Quantum Communication Technology. Quantum information processing (Quantum Information Processing) is a field that uses the principles of quantum mechanics to process information. Unlike conventional classical information processing, quantum information processing uses basic units of information with quantum mechanical properties called qubits, and this is how quantum Unlike conventional classical information processing, quantum information processing uses qubits, which are basic units of information with quantum-mechanical properties. In this article, we will discuss quantum entanglement and quantum teleportation related to quantum communication.

Quantum Physics, Artificial Intelligence and Natural Language Processing

Quantum Physics, Artificial Intelligence and Natural Language Processing. Quantum physics is one of the fields of physics developed to elucidate phenomena and behaviors that cannot be explained within the framework of classical mechanics, and it is a theory that describes physical phenomena at microscopic scales (such as atoms and molecules). The connection between quantum physics and artificial intelligence technology has attracted much attention in recent years. In this article, I would like to discuss some of these perspectives.

Overview of Quantum Computers and Reference Information/Reference Books

Overview of Quantum Computers and Reference Information/Reference Books. A quantum computer is a form of computer that uses the principles of quantum mechanics to process information. The difference between a quantum computer and a conventional computer is that a quantum computer uses a unit of information with quantum mechanical properties called a “qubit” or “quantum bit,” whereas conventional computers process information in binary numbers called “bits.

Optical quantum computer

Optical quantum computer. Among the methods for realising quantum computers described in ‘Overview and Reference Information/Reference Books on Quantum Computers’ and ‘Quantum Computers Accelerate Artificial Intelligence’, the optical quantum computer (optical quantum computer) uses photons, which are light particles, to perform calculations, mainly using photons as quantum bits (qubits). It mainly uses photons as qubits and is expected to enable fast and efficient quantum computation. Optical quantum computers encode information using the state of photons (e.g. polarisation state and phase), perform calculations and execute quantum algorithms through operations known as optical quantum gates.

Semiconductor Technology and AI

Issues and trends in semiconductor technology and AI technology

Issues and trends in semiconductor technology and AI technology> Chip miniaturisation, the most significant challenge in semiconductor technology, involves a number of issues, including many challenges in the manufacturing process and design. As described in ‘Overview of semiconductor manufacturing technology and application of AI technology’, the structure of semiconductors is as follows: switching is controlled by applying a voltage to the gate section formed via an insulator above the current flowing in the channel section between the drain and the source.

Semiconductor Design Processes and Application of AI Technology and Semiconductor Chips for AI Applications

Semiconductor Design Processes and Application of AI Technology and Semiconductor Chips for AI Applications. I will discuss the process of designing semiconductor chips as described in “Computational Elements of Computers and Semiconductor Chips” and semiconductor chips specialized for AI applications, which is one step further from “Introduction to FPGAs for Software Engineers: Machine Learning”.

Semiconductor technology and GNN

Semiconductor technology and GNN. GNN is a deep learning technology for handling graph data, which learns the features of nodes and edges while considering directed/undirected relationships for graph structures represented by nodes (vertices) and edges (edges). This GNN technology is capable of capturing complex interdependencies between nodes and is being considered for application in various domains, making it a powerful machine learning method that can be applied to various aspects of semiconductor technology. In this article, specific applications of GNNs to semiconductor technology will be discussed.

AI Theory and Applications

Overview of Turing’s Theory of Computation, Reference Books and Neural Turing Machines

Overview of Turing’s Theory of Computation, Reference Books and Neural Turing Machines. Turing’s theory of computation, proposed by Alan Turing, is a theory that theorizes the fundamental concepts of computers. This theory provides the basis for understanding how computers work and what computation is, and consists of the following elements

Neural Turing machine refers to a model of computation that combines neural networks and Turing machines.

Overview of quantum neural networks and examples of algorithms and implementations

Overview of quantum neural networks and examples of algorithms and implementations. Quantum Neural Networks (QNN) are an attempt to utilise the capabilities of quantum computers to realise neural networks, as described in ‘Quantum Computers Accelerate Artificial Intelligence’, and exploit the properties of quantum mechanics to extend or improve conventional machine learning algorithms. It aims to extend or improve conventional machine learning algorithms by exploiting the properties of quantum mechanics.

Quantum Support Vector Machine Overview and Examples of Algorithms and Implementations

Quantum Support Vector Machine Overview and Examples of Algorithms and Implementations. Quantum Support Vector Machines (Q-SVMs) are an extension of the classical Support Vector Machines (SVMs) to quantum computing, as described in ‘Quantum Computing Overview and References/Reference Books’. SVMs are powerful algorithms for solving machine learning classification problems, and the power of quantum computing can be harnessed to improve their efficiency.

The various methods and implementations of Explainable Machine Learning

The various methods and implementations of Explainable Machine Learning. Explainable Machine Learning (EML) refers to methods and approaches that explain the predictions and decision-making results of machine learning models in an understandable way. In many real-world tasks, model explainability is often important. This can be seen, for example, in solutions for finance, where it is necessary to explain on which factors the model bases its credit score decisions, or in solutions for medical diagnostics, where it is important to explain the basis and reasons for predictions for patients.

In this section, we discuss various algorithms and examples of python implementations for this explainable machine learning.

Emotion extraction through speech recognition, image recognition, natural language processing and biometric analysis

Emotion extraction through speech recognition, image recognition, natural language processing and biometric analysis. Various models for emotion recognition have been proposed, as described in “Emotion recognition, Buddhist philosophy and AI”. In addition, a number of AI technologies such as speech recognition, image recognition, natural language processing and bioinformation analysis have been used to extract emotions. This section describes the details of these technologies.

Next Generation Energy Technology and AI

Micro Nuclear Power Plant

Micro Nuclear Power Plant. This is an article about a small nuclear power generation module called a “mini-reactor” being developed by a U.S. venture company called NuScale Power. The concept is to use a combination of modules (a few units are enough for a small city) that are 19.8 meters high, 2.7 meters in diameter, and have an output of 60 megawatts (1/10 of a typical small nuclear power plant).

Nuclear Fusion and AI Technology

Nuclear Fusion and AI Technology. Along with micro-atomic power generation, the fusion of nuclear fusion technology and AI technology has become a hot topic in recent years. The basic principle is to use a model generated by learning simulation data to control the fusion reactor through reinforcement learning by feeding back the impedance and current of the coils or the values of sensors installed in the reactor (optical sensors to measure the shape and temperature of the plasma?) The model is then controlled by reinforcement learning with feedback from the coil impedance, current, or sensor values installed in the furnace (optical sensors to measure the shape and temperature of the plasma?

Realisation of fusion by the laser method

Realisation of fusion by the laser method. Another ignition method, the inertial method, in which a laser or particle beam is irradiated onto the fuel sphere, is described here. The inertial method is also known as laser fusion. In the laser fusion method, small spherical pellets of a few millimetres in diameter containing deuterium (D) and tritium (T) are used as fusion fuel, and the surface of the pellet is irradiated with a high-power laser beam. The principle is that the inside of the pellet is rapidly compressed, creating a high-temperature (tens of millions of degrees Celsius) and high-pressure (several hundred atmospheres) state in the centre, and nuclear fusion is initiated.

Electricity storage technology, smart grids and GNNs

Electricity storage technology, smart grids and GNNs. Power storage technology is a generic term for technologies that temporarily store power and release it when required, and is mainly used when supply and demand for power do not match, or to regulate the fluctuating generation of renewable energy sources. Combined, this power storage technology can be used to regulate energy fluctuations within a smart grid, as described below.

Overview of Solar Cells, Challenges and Perovskite Solar Cells

Overview of Solar Cells, Challenges and Perovskite Solar Cells. Solar cell technology, which has flourished since the Sunshine Project started in Japan in 1973, has reached a plateau after several decades of technological accumulation, and is about to take the next step forward. In this article, I would like to give an overview of the solar cell and its future.

Bipolar Lithium-Ion Iron Phosphate Batteries

Bipolar Lithium-Ion Iron Phosphate Batteries. In this issue, we will discuss the lithium-ion iron phosphate battery, which has been the focus of much attention in recent years.

All-solid-state batteries and AI technology

All-solid-state batteries and AI technology. All-solid-state batteries are rechargeable batteries that use a solid electrolyte instead of the liquid electrolyte used in conventional lithium-ion batteries and consist of a ‘positive electrode’ containing lithium and oxides, a ‘solid electrolyte’ which is a solid material that conducts ions, and a ‘negative electrode’ made of lithium metal or graphite, which improves energy density. Lithium metal and graphite are used to improve energy density. The combination of all-solid-state batteries and AI technology has important potential for optimising next-generation energy systems and battery management. In particular, AI technology could make a significant contribution to improving and optimising the performance of all-solid-state batteries in the R&D and manufacturing processes, as well as in the operational phase, as described below.

Modeling and Simulation

Modelling and the human imagination – modelling in philosophy, religion, literature and AI technology

Modelling and the human imagination – modelling in philosophy, religion, literature and AI technology. In contrast to modelling in human endeavour, modelling with artificial intelligence (AI) technology aims to predict human behaviour, decision-making, knowledge, emotions and social interactions AI-based modelling is used to understand, optimise and improve complex systems and phenomena.

Shaky Proteins and Old Me: Data Science in the Age of Misfolding

Shaky Proteins and Old Me: Data Science in the Age of Misfolding. In animals, muscles contain a large amount of protein. In fact, proteins play the role of major components in all parts of life, whether in animals or plants, as components of catalysts (enzymes) for chemical reactions within organisms and receptors (receptors) on biological membranes. Also, “information is transcribed from DNA to RNA, from which the sequence of amino acids in a protein (one-dimensional structure) is determined,” and “the three-dimensional shape taken by a protein (higher-order structure) is essential to its function as a component” is the start of biology.

The next question, then, is how the higher-order structure is determined from the primary structure. For proteins that are not very large molecules, the correct answer is that they fold spontaneously to form higher-order structures. This is called protein folding.

To reproduce this phenomenon in a computer, realistic simulations of proteins have been constructed based on Newton’s equations of motion. This is a technique called molecular dynamics (MD), which is similar to the Hamiltonian MCMC in data science.

Reconstructing the shape of celestial objects from time-series data – Temporal Astronomy

Reconstructing the shape of celestial objects from time-series data – Temporal Astronomy. When I hear that the age of the universe is 13.8 billion years old, I feel that the time scale of the universe and celestial objects is incredibly long. Therefore, no one would expect that the sun, which set in the western sky yesterday evening, would come up a million times brighter in the morning today. But in the real universe, aside from ordinary stars like the sun, we frequently witness such astronomical observations that change on a human time scale.

The first reported explosion of V455 Andromeda was on September 4, 2007. The possibility of an explosion of this star had been pointed out for some time. Hiroshima University’s 1.5-meter telescope “Kanata” was pointed at this object to observe the oscillation phenomenon with a period of about 80 minutes, which is only observed in the early stages of the explosion due to its close distance from the earth. The brightness oscillation was successfully detected. The data obtained, however, showed that the temperature of the object becomes lower when it is brighter, which is unusual for a celestial body to show such fluctuations. However, the results were as expected for this phenomenon.

Here we describe the results of tomographic reconstruction of the shape of the accretion disc from the data obtained in these studies.

Meta-analysis in Medical Research Methods of Evidence Integration in Science-Based Medicine

Meta-analysis in Medical Research Methods of Evidence Integration in Science-Based Medicine. In a meta-analysis, data from multiple trials can be integrated to evaluate trial effects based on a larger amount of information, even when the amount of information in individual trials is insufficient to make inferences with sufficient precision.

The problem here is the assumption that “the relative risk in all trials is the same. In general, trials collected in this way are conducted at different times, in different regions (countries), and at different sites, and it is common for various factors, such as the background of the participants and the definitions of study drug doses and outcomes, to be strictly different across trials.

Next Generation Interfaces and Biocomputers

Soft machines and biocomputers

Soft machines and biocomputers. In the film Terminator, a transformable robot T-1000 made of fluid polycrystalline metal appears. This robot has many unclear points of principle, such as the fact that it has no skeleton, the location of its power source and the principle of its CPU are unknown. Soft machines are being considered.

Brain Machine Interface and OpenBCI

Brain Machine Interface and OpenBCI. Brain Machine Interface (BMI) is a generic term for devices that interface between the brain and computers by detecting brain waves, etc., or conversely by stimulating the brain. OPEN BCI is an open source BMI.

Creativity and AI

Dreams, Brain, and Machine Learning – From Dream Theory to Dream Data Science

Dreams, Brain, and Machine Learning – From Dream Theory to Dream Data Science. Although there seems to be no relationship between dreams and data science, dreams have been one of the sources of ideas in the development of machine learning and brain theory. Recently, data analysis using machine learning has made it possible to analyze (decode) the content of dreams from brain activity patterns during sleep. In this section, we trace the footsteps of dream research leading to dream decoding.

Combining 3D printers with generative AI and applying GNNs

Combining 3D printers with generative AI and applying GNNs. A 3D printer is a device for creating a three-dimensional object from a digital model, which is based on a computer-designed 3D model, which is then layered with materials to produce the object. This process is called additive manufacturing (additive manufacturing). The most common materials used are plastics, but metals, ceramics, resins, foodstuffs and even biomaterials are also used; the combination of GNNs, generative AI and 3D printers can enable complex structures and dynamic optimisation to create new design and manufacturing processes.

LIDAR (Light Detection and Ranging), generative AI and GNN

LIDAR (Light Detection and Ranging), generative AI and GNN. LIDAR (Light Detection and Ranging, LIDAR) is a technology that uses laser light to measure the distance to an object and to accurately determine the 3D shape of the surrounding environment and objects. . This technology is used in a variety of fields, including automated driving, topographical surveying, archaeology and construction.

History and Philosophy of Science

History of science for young readers

History of science for young readers. This book enables young readers to learn the principles of science along with its history. Science includes physics, biology, chemistry and geology in high school alone, and it is difficult for beginning students to understand how science has developed as a whole. The book vividly depicts the dynamic changes in science as once established theories from ancient times to the present day are successively overthrown. The book describes episodes from famous scientists such as Aristotle, Galen, Galileo, Harvey, Bacon, Newton, Einstein and Berners-Lee, and traces the trajectory of development from ancient civilisations to modern chemistry.

Can life be created?

Can life be created?. Given that life was ‘inevitably’ or ‘accidentally’ created, the natural next step is to consider whether humans can do it. While advances in science and technology have improved our understanding of the origins of life and its reproducibility, the question of what it means to ‘fully create’ life is still being debated.