What is Artificial Intelligence?

Artificial Intelligence (AI) is a rapidly evolving field that focuses on creating machines capable of performing tasks that typically require human intelligence. This includes activities like learning, reasoning, problem-solving, understanding natural language, and perceiving environments. AI has become a transformative force in various industries, from healthcare to finance, and its applications continue to expand as technology advances.

History and Evolution

AI has roots in early computer science and mathematics, evolving from simple algorithms to complex neural networks and machine learning models.

Types of AI

AI can be categorized into narrow AI, which performs specific tasks, and general AI, which aims to replicate human cognitive functions more broadly.

Machine Learning

A core component of AI, machine learning involves training algorithms on data so that they can make predictions or decisions without being explicitly programmed for the task.

Ethical Considerations

As AI grows more powerful, it raises ethical questions around privacy, bias, and the potential impact on jobs and society.

Introduction to AI

  1. Basic Concepts and Terminology.
    1. Elements Of Intelligence.
    2. Difference Between Human and Machine Intelligence.
  2. Sub-areas of AI.
    1. Machine Learning.
    2. Deep Learning.
    3. Neural Networks.
    4. Fuzzy Logic.
    5. Cognitive Computing.
    6. Natural Language Processing.
    7. Computer Vision.
  3. Successful Use Cases.
    1. Facial Recognition.
    2. Plate Recognition.
    3. Traffic Signs Recognition.
    4. Deep-Fakes in Media.
    5. Go and Chess Playing.
    6. Protein Folding.

  1. History of Computer Science.
    1. What is a Computer.
    2. Ancient and Old Machines.
    3. First Computers.
    4. Current Computers and Internet.
    5. Smartphones.
  2. Origins of Artificial Intelligence.
    1. The Golden Age of AI.
    2. The Turing test.
    3. Information and Knowledge.
    4. Rational Machines.
    5. Current AI.
  3. Future of AI.
    1. Current trends in AI.
    2. Impact on Society.
    3. Breakthroughs in AI.
    4. What May Go Wrong.
    5. Consciousness.

  1. Introduction.
  2. Sciences.
    1. Chemistry and Biology.
    2. Health.
    3. Physics.
    4. Materials Science.
    5. Energy System.
    6. Telecommunications.
    7. Agriculture.
  3. Finances.
    1. Banking.
    2. Phishing.
    3. Brokers and Traders.
    4. Risk Assessment.
    5. Fraud Prevention and Detection.
  4. Industry.
    1. Autonomous Vehicles.
    2. Route Planning.
    3. Environmental monitoring.
    4. Early-warning systems.
    5. Military.
  5. ICT.
    1. Programming Assistance.
    2. Neural Network Design.
    3. Quantum Computing.
    4. Data Mining.
    5. Gaming.
    6. Internet and e-Commerce.
  6. Humanities.
    1. Law.
    2. Social.
    3. Arts.
    4. Education.

  1. Lifecycle of the data.
    1. The Source Data.
    2. Features of the Model.
    3. Model Selection.
    4. Data Locality.
    5. Trust and Lineage.
  2. Legal issues.
    1. Data Protection.
    2. AI and the European Union.
    3. National Strategies on Artificial Intelligence.
    4. National Agencies to Supervise AI.
  3. Ethical principles of AI.
    1. Observe Human Decisions.
    2. Prevent Damages.
    3. Equity.
    4. Explainability.
  4. Cultural and social implications.
    1. Arts & Culture.
    2. Employment.
    3. Social Networks.
    4. Responsibility.
    5. Risks.
Introduction to Artificial Intelligence

Methods and Tools in AI

Develop Artificial Intelligence skills in your field with our free course!

Our course is free and designed to develop skills as an applied AI engineer