This blog will be a collection of knowledge for creating artificial intelligence systems from scratch, based on the principles described in DEUS EX MACHINA – How to Create a Machine-Guided God. These are not mere lists of information, but rather pieces of each technology pieced together, with the aim of enabling engineers to find hints for solving real-world problems.
The contents cover a wide range of topics, starting from applications of machine learning and AI technologies, programming techniques, IT infrastructure basics, practical applications for digital transformation, and sometimes even life tips and miscellaneous notes.
If you understand each of these areas and can freely combine them, creating an artificial intelligence system from scratch is not a dream. I hope this blog will serve as a compass to help you along the way.
Navigate this blog
Machine Learning Technology
Machine Learning Technology is a comprehensive page on the categories of the following areas for understanding and applying machine learning technology.<Basic Theory and Mathematics>
The mathematics required for machine learning (linear algebra, probability statistics, and calculus) and algorithms and data structures are explained. Fundamentals of problem setting and quantification and data analysis are also covered.
<Data Processing and Preprocessing>
Includes noise reduction, data cleansing, missing value interpolation, and small data applications. Approaches to both large and small data are organized, including parallel and distributed processing techniques.
<Models and Algorithms>
Covers a wide variety of models, including supervised and unsupervised learning, deep learning (CNN, RNN, Transformers), reinforcement learning, online learning, and probabilistic approaches (Bayesian inference, nonparametric Bayesian).
<Applications to Special Data>
Explains how to handle data commonly used in practice, such as graph neural networks (GNNs) for handling graph data, time series data analysis, and recommendation techniques, along with specific examples.
<Explicability and Optimization>
Explores understanding and optimizing models by covering techniques such as explainable machine learning (XAI), causal inference, sparse learning, kernel methods, submodular optimization, and bandit problems
Artificial Intelligence Technology
Artificial Intelligence Technology is a comprehensive page on the categories of areas shown below for understanding and applying machine learning techniques.<Artificial Intelligence Theory and Algorithms>
Theoretical background and basic algorithms related to artificial intelligence technologies in general are explained. Covers graph data algorithms, automata, state transitions, Petri nets, automatic programming, counting problems, and more.
<Hardware and AI>
Introduces the latest machine learning and AI technologies that leverage hardware, such as FPGAs, optical computing, and quantum computing.
<Natural Language Processing and Knowledge Utilization>
Provides examples of AI applications using natural language processing technology, knowledge data, ontology technology, Semantic Web technology, and inference technology.
<Agents and Autonomous AI>
Summarizes areas related to the autonomous behavior of AI, including artificial life, agents, autonomous artificial intelligence, self-expanding machines, chatbots, and question-and-answer technology.
<Data Processing & Visualization>
Explains technologies for AI to process and visualize data, including visualization & UX, workflow & services, image information processing, speech recognition, geospatial information processing, sensor data & IoT, anomaly detection, and stream data technologies.
<AI Research and Latest Trends>
A resource of the latest AI conference papers, featuring algorithms, implementation examples, and specific applications, providing a springboard for learning about cutting-edge technologies.
Programming Technology
Programming Technology provides a comprehensive overview of the programming techniques and applications of various languages required to create an artificial intelligence system from scratch.<Overview of Programming Technology>
Starting with the basic concepts of programming, the course cultivates efficient development skills by understanding the characteristics and roles of each language. This section also touches on programming paradigms such as functional, object-oriented, and procedural.
<Programming languages and their applications>
- Clojure and Functional Programming
Students learn the characteristics of Clojure, which emphasizes data orientation and immutability, and the concept of functional programming. - Python and Machine Learning
Explains methods for data analysis and model building using machine learning libraries (TensorFlow, scikit-learn, etc.). - Java, Scala, Kotlin and general purpose application building
Compares Java’s robustness in large systems, Scala’s flexible type system, and Kotlin’s modern development style, and introduces practical application development methods. - PHP and Web Frameworks
Explains efficient web app development by utilizing PHP frameworks such as Laravel. - Prolog and Knowledge Information Processing
Explores how to build knowledge representation and inference engines through logic programming. - LISP and Artificial Intelligence Technology
Learn to implement AI algorithms utilizing LISP’s flexible syntax and recursive approach. - R Language and Machine Learning
Work on understanding and implementing machine learning models through statistical analysis and data visualization. - C/C++ and Rust
Handle system programming in C/C++ for situations where high-speed processing is required and in Rust for safety. - Front-end development with JavaScript and React
Practice building dynamic and intuitive user interfaces using React. - Web Design with CSS
Explains the use of CSS for modern web design, responsive design, and animation effects.
ICT Technology
A deep understanding of ICT technology is essential for building artificial intelligence systems, and ICT technology covers the following areas in detail
<Infrastructure Technology (Infrastructure, OS, Hardware)>
- IT infrastructure technology
(network, server, cloud technologies) - Operating systems (Linux, etc.)
- Hardware in computers
(CPU, memory, storage, etc.)
<Software development and operations (DevOps and architecture)>
- DevOps
(CI/CD, infrastructure automation) - Microservices and multi-agent systems
<Web technology and UI/UX>
- Web Technologies
(Web app development, HTTP/HTTPS, REST API) - User interface and data visualization
(D3.js, Chart.js)
<Data management and processing>
- Database technology
(RDBMS: MySQL, PostgreSQL, NoSQL: MongoDB, Cassandra) - Search technology
(ElasticSearch, full-text search engines) - Stream data technology
(Apache Kafka)
<Security and data processing>
- Encryption and security techniques and data compression techniques
(AES, RSA, data transfer, compression algorithms)
<IoT and Geospatial Information>
- Geospatial information processing
(GIS, mapping technology) - Sensor data and IoT and WoT technologies
(Sensor data processing, WoT)
Overview of DX Technology
DX (Digital Transformation) technology is a key element for companies to innovate their business processes and gain competitive advantage through the use of digital technology. DX technology provides an in-depth look at the following topics
- What is DX Technology Define and clarify the purpose of digital transformation and explore how companies can leverage digital technology to optimize their business processes.
- Issue setting and quantification In order to promote DX, clear issues must be set and methods to quantitatively evaluate them are required. This section explains how to set KPIs and utilize data-driven approaches.
- Specific Application Cases of Artificial Intelligence Technology for DX Use This section introduces specific application cases such as customer behavior prediction, manufacturing line optimization, and anomaly detection using AI.
- Application of ICT Frameworks Explains how to apply ICT frameworks (cloud infrastructure, API management, data integration) that are indispensable for DX realization.
- Data (electronic) conversion of unstructured information Describes the process of digitizing unstructured data such as documents, images, and voice, and the technologies used to handle them.
- Linking Electronic Data with Knowledge Information This section describes the integration of electronic data into knowledge graphs and other forms of knowledge graphs and their application to knowledge discovery and decision support by AI.
Life Tips & Miscellaneous Notes
Life Tips & Miscellaneous explores a wide range of ways of thinking and thinking outside of technology.- Problem Solving Methods, Thinking and Design of Experiments
This section introduces approaches to effectively solving problems using logical thinking, hypothesis testing, and design of experiments. - Thesis/Report Writing
Explores techniques for writing research papers and business reports in a clear and persuasive manner. - Zen Thought and History, Mahayana Buddhism, Path Thought, Christianity, Philosophy
Students learn the historical background of ideas and religions and how they can be applied to modern thinking and behavior. - Travel and History, Art, Sports and Gourmet
Explore experiences that stimulate the senses and the new perspectives that can be gained from them. - Economics, Financial Engineering, Business and Artificial Intelligence Technology
Examines economic theory, financial models, and business applications of AI. - Technology Miscellany
This section will be a free discussion of the latest technological trends and reflections on them. - Physics, Chemistry, Biology, Space, Mathematics and Artificial Intelligence
Exploring the intersection of science and AI and the possibilities of the future. - Books, TV, Movies and Music
Sharing thoughts and ideas learned from creative works.