Artificial Intelligence with PHP
  • Getting Started
    • Introduction
    • Audience
    • How to Read This Book
    • Glossary
    • Contributors
    • Resources
    • Changelog
  • Artificial Intelligence
    • Introduction
    • Overview of AI
      • History of AI
      • How Does AI Work?
      • Structure of AI
      • Will AI Take Over the World?
      • Types of AI
        • Limited Memory AI
        • Reactive AI
        • Theory of Mind AI
        • Self-Aware AI
    • AI Capabilities in PHP
      • Introduction to LLM Agents PHP SDK
      • Overview of AI Libraries in PHP
    • AI Agents
      • Introduction to AI Agents
      • Structure of AI Agent
      • Components of AI Agents
      • Types of AI Agents
      • AI Agent Architecture
      • AI Agent Environment
      • Application of Agents in AI
      • Challenges in AI Agent Development
      • Future of AI Agents
      • Turing Test in AI
      • LLM AI Agents
        • Introduction to LLM AI Agents
        • Implementation in PHP
          • Sales Analyst Agent
          • Site Status Checker Agent
    • Theoretical Foundations of AI
      • Introduction to Theoretical Foundations of AI
      • Problem Solving in AI
        • Introduction
        • Types of Search Algorithms
          • Comparison of Search Algorithms
          • Informed (Heuristic) Search
            • Global Search
              • Beam Search
              • Greedy Search
              • Iterative Deepening A* Search
              • A* Search
                • A* Graph Search
                • A* Graph vs A* Tree Search
                • A* Tree Search
            • Local Search
              • Hill Climbing Algorithm
                • Introduction
                • Best Practices and Optimization
                • Practical Applications
                • Implementation in PHP
              • Simulated Annealing Search
              • Local Beam Search
              • Genetic Algorithms
              • Tabu Search
          • Uninformed (Blind) Search
            • Global Search
              • Bidirectional Search (BDS)
              • Breadth-First Search (BFS)
              • Depth-First Search (DFS)
              • Iterative Deepening Depth-First Search (IDDFS)
              • Uniform Cost Search (UCS)
            • Local Search
              • Depth-Limited Search (DLS)
              • Random Walk Search (RWS)
          • Adversarial Search
          • Means-Ends Analysis
      • Knowledge & Uncertainty in AI
        • Knowledge-Based Agents
        • Knowledge Representation
          • Introduction
          • Approaches to KR in AI
          • The KR Cycle in AI
          • Types of Knowledge in AI
          • KR Techniques
            • Logical Representation
            • Semantic Network Representation
            • Frame Representation
            • Production Rules
        • Reasoning in AI
        • Uncertain Knowledge Representation
        • The Wumpus World
        • Applications and Challenges
      • Cybernetics and AI
      • Philosophical and Ethical Foundations of AI
    • Mathematics for AI
      • Computational Theory in AI
      • Logic and Reasoning
        • Classification of Logics
        • Formal Logic
          • Propositional Logic
            • Basics of Propositional Logic
            • Implementation in PHP
          • Predicate Logic
            • Basics of Predicate Logic
            • Implementation in PHP
          • Second-order and Higher-order Logic
          • Modal Logic
          • Temporal Logic
        • Informal Logic
        • Semi-formal Logic
      • Set Theory and Discrete Mathematics
      • Decision Making in AI
    • Key Application of AI
      • AI in Astronomy
      • AI in Agriculture
      • AI in Automotive Industry
      • AI in Data Security
      • AI in Dating
      • AI in E-commerce
      • AI in Education
      • AI in Entertainment
      • AI in Finance
      • AI in Gaming
      • AI in Healthcare
      • AI in Robotics
      • AI in Social Media
      • AI in Software Development
      • AI in Adult Entertainment
      • AI in Criminal Justice
      • AI in Criminal World
      • AI in Military Domain
      • AI in Terrorist Activities
      • AI in Transforming Our World
      • AI in Travel and Transport
    • Practice
  • Machine Learning
    • Introduction
    • Overview of ML
      • History of ML
        • Origins and Early Concepts
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Terms and Principles
      • Machine Learning Life Cycle
      • Problems and Challenges
    • ML Capabilities in PHP
      • Overview of ML Libraries in PHP
      • Configuring an Environment for PHP
        • Direct Installation
        • Using Docker
        • Additional Notes
      • Introduction to PHP-ML
      • Introduction to Rubix ML
    • Mathematics for ML
      • Linear Algebra
        • Scalars
          • Definition and Operations
          • Scalars with PHP
        • Vectors
          • Definition and Operations
          • Vectors in Machine Learning
          • Vectors with PHP
        • Matrices
          • Definition and Types
          • Matrix Operations
          • Determinant of a Matrix
          • Inverse Matrices
          • Cofactor Matrices
          • Adjugate Matrices
          • Matrices in Machine Learning
          • Matrices with PHP
        • Tensors
          • Definition of Tensors
          • Tensor Properties
            • Tensor Types
            • Tensor Dimension
            • Tensor Rank
            • Tensor Shape
          • Tensor Operations
          • Practical Applications
          • Tensors in Machine Learning
          • Tensors with PHP
        • Linear Transformations
          • Introduction
          • LT with PHP
          • LT Role in Neural Networks
        • Eigenvalues and Eigenvectors
        • Norms and Distances
        • Linear Algebra in Optimization
      • Calculus
      • Probability and Statistics
      • Information Theory
      • Optimization Techniques
      • Graph Theory and Networks
      • Discrete Mathematics and Combinatorics
      • Advanced Topics
    • Data Fundamentals
      • Data Types and Formats
        • Data Types
        • Structured Data Formats
        • Unstructured Data Formats
        • Implementation with PHP
      • General Data Processing
        • Introduction
        • Storage and Management
          • Data Security and Privacy
          • Data Serialization and Deserialization in PHP
          • Data Versioning and Management
          • Database Systems for AI
          • Efficient Data Storage Techniques
          • Optimizing Data Retrieval for AI Algorithms
          • Big Data Considerations
            • Introduction
            • Big Data Techniques in PHP
      • ML Data Processing
        • Introduction
        • Types of Data in ML
        • Stages of Data Processing
          • Data Acquisition
            • Data Collection
            • Ethical Considerations in Data Preparation
          • Data Cleaning
            • Data Cleaning Examples
            • Data Cleaning Types
            • Implementation with PHP
          • Data Transformation
            • Data Transformation Examples
            • Data Transformation Types
            • Implementation with PHP ?..
          • Data Integration
          • Data Reduction
          • Data Validation and Testing
            • Data Splitting and Sampling
          • Data Representation
            • Data Structures in PHP
            • Data Visualization Techniques
          • Typical Problems with Data
    • ML Algorithms
      • Classification of ML Algorithms
        • By Methods Used
        • By Learning Types
        • By Tasks Resolved
        • By Feature Types
        • By Model Depth
      • Supervised Learning
        • Regression
          • Linear Regression
            • Types of Linear Regression
            • Finding Best Fit Line
            • Gradient Descent
            • Assumptions of Linear Regression
            • Evaluation Metrics for Linear Regression
            • How It Works by Math
            • Implementation in PHP
              • Multiple Linear Regression
              • Simple Linear Regression
          • Polynomial Regression
            • Introduction
            • Implementation in PHP
          • Support Vector Regression
        • Classification
        • Recommendation Systems
          • Matrix Factorization
          • User-Based Collaborative Filtering
      • Unsupervised Learning
        • Clustering
        • Dimension Reduction
        • Search and Optimization
        • Recommendation Systems
          • Item-Based Collaborative Filtering
          • Popularity-Based Recommendations
      • Semi-Supervised Learning
        • Regression
        • Classification
        • Clustering
      • Reinforcement Learning
      • Distributed Learning
    • Integrating ML into Web
      • Open-Source Projects
      • Introduction to EasyAI-PHP
    • Key Applications of ML
    • Practice
  • Neural Networks
    • Introduction
    • Overview of NN
      • History of NN
      • Basic Components of NN
        • Activation Functions
        • Connections and Weights
        • Inputs
        • Layers
        • Neurons
      • Problems and Challenges
      • How NN Works
    • NN Capabilities in PHP
    • Mathematics for NN
    • Types of NN
      • Classification of NN Types
      • Linear vs Non-Linear Problems in NN
      • Basic NN
        • Simple Perceptron
        • Implementation in PHP
          • Simple Perceptron with Libraries
          • Simple Perceptron with Pure PHP
      • NN with Hidden Layers
      • Deep Learning
      • Bayesian Neural Networks
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
    • Integrating NN into Web
    • Key Applications of NN
    • Practice
  • Natural Language Processing
    • Introduction
    • Overview of NLP
      • History of NLP
        • Ancient Times
        • Medieval Period
        • 15th-16th Century
        • 17th-18th Century
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Concepts in NLP
      • Common Challenges in NLP
      • Machine Learning Role in NLP
    • NLP Capabilities in PHP
      • Overview of NLP Libraries in PHP
      • Challenges in NLP with PHP
    • Mathematics for NLP
    • NLP Techniques
      • Basic Text Processing with PHP
      • NLP Workflow
      • Popular Tools and Frameworks for NLP
      • Techniques and Algorithms in NLP
        • Basic NLP Techniques
        • Advanced NLP Techniques
      • Advanced NLP Topics
    • Integrating NLP into Web
    • Key Applications of NLP
    • Practice
  • Computer Vision
    • Introduction
  • Overview of CV
    • History of CV
    • Common Use Cases
  • CV Capabilities in PHP
  • Mathematics for CV
  • CV Techniques
  • Integrating CV into Web
  • Key Applications of CV
  • Practice
  • Robotics
    • Introduction
  • Overview of Robotics
    • History and Evolution of Robotics
    • Core Components
      • Sensors (Perception)
      • Actuators (Action)
      • Controllers (Processing and Logic)
    • The Role of AI in Robotics
      • Object Detection and Recognition
      • Path Planning and Navigation
      • Decision Making and Learning
  • Robotics Capabilities in PHP
  • Mathematics for Robotics
  • Building Robotics
  • Integration Robotics into Web
  • Key Applications of Robotics
  • Practice
  • Expert Systems
    • Introduction
    • Overview of ES
      • History of ES
        • Origins and Early ES
        • Milestones in the Evolution of ES
        • Expert Systems in Modern AI
      • Core Components and Architecture
      • Challenges and Limitations
      • Future Trends
    • ES Capabilities in PHP
    • Mathematics for ES
    • Building ES
      • Knowledge Representation Approaches
      • Inference Mechanisms
      • Best Practices for Knowledge Base Design and Inference
    • Integration ES into Web
    • Key Applications of ES
    • Practice
  • Cognitive Computing
    • Introduction
    • Overview of CC
      • History of CC
      • Differences Between CC and AI
    • CC Compatibilities in PHP
    • Mathematics for CC
    • Building CC
      • Practical Implementation
    • Integration CC into Web
    • Key Applications of CC
    • Practice
  • AI Ethics and Safety
    • Introduction
    • Overview of AI Ethics
      • Core Principles of AI Ethics
      • Responsible AI Development
      • Looking Ahead: Ethical AI Governance
    • Building Ethics & Safety AI
      • Fairness, Bias, and Transparency
        • Bias in AI Models
        • Model Transparency and Explainability
        • Auditing, Testing, and Continuous Monitoring
      • Privacy and Security in AI
        • Data Privacy and Consent
        • Safety Mechanisms in AI Integration
        • Preventing and Handling AI Misuse
      • Ensuring AI Accountability
        • Ethical AI in Decision Making
        • Regulations & Compliance
        • AI Risk Assessment
    • Key Applications of AI Ethics
    • Practice
  • Epilog
    • Summing-up
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On this page
  • The Further History of Machine Learning
  • The Early History (Pre-1940)
  • The Era of Stored Program Computers
  • Computer Machinery and Intelligence
  • The First “AI Winter” (1974-1980)
  • The Second “AI Winter” (1987–1993)
  • The Expert Systems of the 80s: A Resurrection
  • The Rise of the Internet and the Era of Machine Learning
  1. Machine Learning
  2. Overview of ML
  3. History of ML

20th Century

The Further History of Machine Learning

In the 20th century, machine learning began to develop rapidly due to advancements in mathematics, statistics, and computer science. The first algorithms for data processing emerged, such as the perceptron, and the foundations of neural networks and other key methods started to be developed. With the advancement of computers and the increase in the volume of available data in the 1990s, machine learning became more practical and widely used in various fields, such as image recognition, natural language processing, and recommendation systems.

The Early History (Pre-1940)

  • 1913: Andrey Markov introduced Markov chains, which later became essential in various machine learning applications, including natural language processing and speech recognition.

  • 1925: Ronald Fisher published "Statistical Methods for Research Workers," introducing many of the statistical concepts that underpin machine learning, such as maximum likelihood estimation.

  • 1936: Alan Turing introduced the concept of the Turing machine in his paper "On Computable Numbers," which explained how machines could follow a set of instructions (algorithms) to solve problems. This was foundational for the development of machine learning algorithms.

  • 1937: Claude Shannon's master's thesis at MIT demonstrated that Boolean algebra could be used to simplify the design of electrical switching circuits. This work was crucial for the development of digital computers, which would later enable complex machine learning computations.

  • 1938: Claude Shannon published "A Symbolic Analysis of Relay and Switching Circuits," further developing the application of Boolean algebra to circuit design, which would be essential for building computers capable of running machine learning algorithms.

The Era of Stored Program Computers

  • 1940: The invention of the first general-purpose computer, ENIAC, was the beginning of computers that could perform various tasks. It was manually operated, but nevertheless it was a breakthrough in electronic computing.

  • 1949-1951: The development of stored-program computers such as the EDSAC and EDVAC allowed computers to store data and programs, giving them the ability to perform more complex operations automatically.

  • 1943: A neural network model was created using electrical circuits, which was one of the earliest studies of how machines could mimic human brain activity. It was a key concept in machine learning, later applied to tasks such as pattern recognition and others.

Computer Machinery and Intelligence

  • 1950: Alan Turing published his famous paper, "Computing Machinery and Intelligence," which explored the question, "Can machines think?" This is one of the earliest papers in the field of artificial intelligence, and it had a huge impact on the entire field.

  • 1952: Arthur Samuel developed a checkers-playing program for IBM computers that improved its performance the more it played. This was one of the first self-learning programs, an early example of machine learning.

  • 1959: Arthur Samuel coined the term “Machine Learning” to describe the process by which computers could learn and improve from experience without being explicitly programmed for every task.

  • 1959: The first neural network was successfully applied to solve a real-world problem—removing echoes in phone lines using an adaptive filter, a foundational application of early neural networks.

  • 1964: ELIZA is an early natural language processing computer program developed from 1964 to 1967 at MIT by Joseph Weizenbaum.

  • 1974: Kaissa Soviet chess machine, was developed in the early 1970s at the Institute of Control Sciences in Moscow, Soviet Union. It was one of the first chess programs to achieve notable success in computer chess competitions. It won the first World Computer Chess Championship in Stockholm, marking a significant milestone in the history of artificial intelligence and game-playing algorithms.

The First “AI Winter” (1974-1980)

  • 1974-1980: This was a tough period for AI and machine learning researchers. There were high expectations, but many AI projects failed to deliver results, leading to a reduction in government funding and a general lack of interest. This time of stalled progress was called the “AI winter.”

The Second “AI Winter” (1987–1993)

  • 1987–1993: The second “AI winter” took place when interest and funding in artificial intelligence dropped significantly once again. This decline was largely due to inflated expectations that were not met, a lack of notable progress, and the high costs of AI projects at the time. Many ambitious projects were either unsuccessful or failed to deliver the results anticipated, which led to a reduction in government and private sector funding. The hype surrounding AI in the 1980s gave way to disappointment, causing this second period of stagnation in AI development.

The Expert Systems of the 80s: A Resurrection

  • In the 1980s, AI experienced a revival with the development of expert systems, which mimicked the decision-making process of specialists in specific fields. While these systems proved useful in certain areas, their application remained limited.

The Rise of the Internet and the Era of Machine Learning

  • With the advent of the Internet and the exponential increase in data availability, machine learning algorithms and neural networks gained a new life. The ability to analyze and learn from large data sets has opened up a world of possibilities for AI applications in everyday life.

  • 1985: Researchers Terry Sejnowski and Charles Rosenberg created NETtalk, a neural network that taught itself how to pronounce 20,000 words in just one week, showcasing the potential of machine learning for language processing tasks.

  • 1997: IBM’s Deep Blue computer made headlines by beating world chess champion Garry Kasparov, becoming the first computer to defeat a human champion in chess. This showed the world that machine learning could be applied to strategic, decision-based games.

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