Lesson 2.4

• Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. It involves the development of algorithms and computer programs that can perform tasks that typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation.
• Intelligence: The ability to learn and solve problems. This definition is taken from webster’s Dictionary.
• Intelligence, as we know, is the ability to acquire and apply knowledge. Knowledge is the information acquired through experience. Experience is the knowledge gained through exposure(training). Summing the terms up, we get artificial intelligence as the “copy of something natural(i.e., human beings) ‘WHO’ is capable of acquiring and applying the information it has gained through exposure.”
• Intelligence is composed of:

  • Reasoning
  • Learning
  • Problem-Solving
  • Perception
  • Linguistic Intelligence
• Machine Learning: A subfield of AI that uses algorithms to enable systems to learn from data and make predictions or decisions without being explicitly programmed.
• Natural Language Processing (NLP): A branch of AI that focuses on enabling computers to understand, interpret, and generate human language.
• Computer Vision: A field of AI that deals with the processing and analysis of visual information using computer algorithms.
• Robotics: AI-powered robots and automation systems that can perform tasks in manufacturing, healthcare, retail, and other industries.
• Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain.
• Expert Systems: AI systems that mimic the decision-making ability of a human expert in a specific field.
• Chatbots: AI-powered virtual assistants that can interact with users through text-based or voice-based interfaces.
• Reinforcement Learning: is an interesting field of Artificial Intelligence that focuses on training agents to make intelligent decisions by interacting with their environment.
• Explainable AI: this AI techniques focus on providing insights into how AI models arrive at their conclusions.
• Generative AI: Through this technique AI models can learn the underlying patterns and create realistic and novel outputs.
• Edge AI:AI involves running AI algorithms directly on edge devices, such as smartphones, IoT devices, and autonomous vehicles, rather than relying on cloud-based processing.
• Quantum AI: Quantum AI combines the power of quantum computing with AI algorithms to tackle complex problems that are beyond the capabilities of classical computers.
• “Artificial Intelligence is a TOOL not a THREAT” Rodney Brooks