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ARTIFICIAL INTELLIGENCE BASICS, Lecture notes of Artificial Intelligence

**Artificial Intelligence Basics** is a foundational guide to understanding AI concepts, history, and applications. It covers key topics like Weak AI, Strong AI, and Super AI, while tracing AI's evolution from early myths to modern advancements in machine learning and deep learning. The document addresses core AI challenges such as natural language processing and problem-solving, alongside practical solutions like heuristic searches and algorithms (e.g., A* search). With real-world examples in healthcare, finance, and robotics, this educational material is ideal for students, educators, and professionals exploring AI fundamentals.

Typology: Lecture notes

2023/2024

Available from 11/27/2024

yashaswini-kulshrestha
yashaswini-kulshrestha 🇮🇳

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Lecture 1: Overview and
Historical Perspective of AI
Prof Meena Jha
Meena.jha@nsut.ac.in
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Lecture 1: Overview and

Historical Perspective of AI

Prof Meena Jha Meena.jha@nsut.ac.in

Artificial

Intelligence

  • AI, or Artificial Intelligence, is a field of computer science that focuses on creating intelligent machines that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem- solving, perception, understanding natural language, and more. The idea of AI has been around for centuries, but significant progress in the field began in the mid-20th century.

Artificial

Intelligence

  • And it fails to include some areas of potentially very large impact, namely problems that cannot now be solved well by either computers or people.
  • But it provides a good outline of what constitutes artificial intelligence, and it avoids the philosophical issues that dominate attempts to define the meaning of either artificial or intelligence.

Artificial

Intelligence

  • As AI migrates to the real-world we do not seem to be satisfied with just a computer playing a chess game.
  • Instead, we wish a robot would sit opposite to us as an opponent, visualize the real board and make the right moves in this physical world.

Historical

Perspective of AI

  • Dartmouth Conference (1956):
  • The term “Artificial Intelligence” was coined during a workshop held at Dartmouth College in the summer of 1956. The conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, marked the birth of AI as a formal academic discipline.

Historical

Perspective of AI

  • Early AI Research (1950s-1960s):
  • During this period, AI researchers were optimistic about creating machines that could simulate human intelligence. Early efforts focused on symbolic reasoning, logic, and problem-solving. Programs like the Logic Theorist and General Problem Solver demonstrated early successes in these areas.

Historical

Perspective

of AI

  • Expert Systems (1980s-1990s):
  • During the AI winter, AI research shifted its focus to more practical and achievable goals, leading to the development of expert systems. Expert systems were rule-based programs designed to mimic the decision- making abilities of human experts in specific domains.

Historical

Perspective

of AI

  • Rise of Machine Learning (1990s-Present):
  • The advent of more powerful computers and the availability of vast amounts of data revitalized the field of AI. Machine learning techniques, which allow computers to learn from data and improve their performance without being explicitly programmed, became central to many AI applications.

Historical

Perspective

of AI

  • Current State of AI:
  • As of the early 2020s, AI is an integral part of various industries and applications, including virtual assistants, autonomous vehicles, medical diagnostics, finance, gaming, and more. AI continues to evolve rapidly, with ongoing research and development pushing the boundaries of what machines can achieve.

THE Al PROBLEMS

  • Much of the early work in the field focused on formal tasks, such as game playing and theorem proving.
  • Chess also received a good deal of attention.
  • Game playing and theorem proving share the property that people who do them well arc considered to be displaying intelligence.
  • computers could perform well at those tasks simply by being fast at exploring a large number of solution paths and then selecting the best one.
  • no computer is fast enough to overcome the combinatorial explosion generated by most problems.

THE Al PROBLEMS: Handling larger amounts

of Knowledge

  • new tasks could reasonably be attempted. These include perception (vision and speech), natural language understanding, and problem solving in specialized domains such as medical diagnosis and chemical analysis.
  • Perceptual tasks are difficult because they involve analog (rather than digital) signals; the signals are typically very noisy and usually a large number of things (some of which may be partially obscuring others) must be perceived at once.

THE Al PROBLEMS: Handling larger amounts

of Knowledge

  • The ability to use language to communicate a wide variety of ideas is perhaps the most important thing that separates humans from the other animals. The problem of understanding spoken language is a perceptual problem and is hard to solve

Figure below lists

some of the tasks

that are the targets

of work in AI

perceptual, linguistic, and commonsense skills

  • First, perceptual, linguistic, and commonsense skills are learned.
  • Later (and of course for some people, never) expert skills such as engineering, medicine, or finance are acquired.
  • It might seem to make sense then that the earlier skills are easier and thus more amenable to computerized duplication than are the later, more specialized ones.