Module 1: Introduction to AI

A branch of science that helps machines solve complex problems like humans.

Combining human intelligence features as algorithms in a computer-friendly way to create intelligent behavior in machines.

Finding solutions to complicated issues using human-like logic in machines. The solution methods may vary.

The ability of a system to act as intelligently as a human or to perform optimally.

The practical ways AI systems affect different areas of life.

Different methods used to create intelligent machines.

Current boundaries and challenges in AI development.

The timeline of artificial intelligence developments.

The debate in AI centers on whether to mimic human thought processes or focus solely on the observable actions produced by intelligent systems.

This view aims to create AI systems by understanding and replicating human thought processes.

A test proposed by Alan Turing to determine if a machine can exhibit intelligent behavior indistinguishable from a human.

An interrogator communicates with two entities, one human and one machine, through text-only interaction. The goal is for the interrogator to identify which is the machine.

For a machine to pass the Turing Test, it must convincingly fool the interrogator into believing it is human.

This approach to AI focuses on studying the underlying reasoning processes used by intelligent systems. It emphasizes how a system reaches a conclusion or chooses an action.

This perspective in AI prioritizes the reasoning mechanism behind a system's conclusions or actions.

One of the core goals of AI research is to create systems that can think and act at a level comparable to human intelligence.

This approach aims to create machines that can truly reason and solve problems just like humans. They aim to achieve human-level intelligence, indistinguishable from our own.

This approach focuses on creating machines that simulate human intelligence, but they don't necessarily possess true reasoning ability. They can act like they're intelligent.

This approach aims to develop commercially viable AI systems that can solve real-world problems. Examples include security systems that recognize authorized faces.

This approach uses computers to test theories about how human minds work. It investigates areas like facial recognition and problem-solving.

Current AI excels in narrowly defined domains and specific tasks. They need specialized knowledge but struggle with common sense and understanding complex language.

A critical element for AI to operate in complex, open-ended environments. It requires a nuanced understanding of the world around us, something current AI systems lack.

Another challenge for AI. It needs to comprehend complex language, including slang, idioms, and context to fully interact with humans.

Programs designed to monitor your online activities, search for information, and learn what you find most useful.

A mathematical system that represents logical relationships using binary values (0 and 1) and operations like AND, OR, and NOT.

Early pioneers in developing programmable mechanical calculating machines, laying the groundwork for modern computers.

A branch of logic using symbols and formal systems to represent and analyze logical arguments and deductions, building upon Boole's work.

A pioneering theory that modeled the brain as a network of interconnected units called neurons, each performing Boolean logic operations.

The first randomly wired neural network learning machine, developed by Minsky and Edmonds, used 3000 vacuum tubes and a network of 40 neurons.

Articulated a complete vision of AI in his paper 'Computing Machinery and Intelligence', emphasizing problem-solving using heuristics and machine learning.

A landmark conference that brought together the founding fathers of AI and officially coined the term 'Artificial Intelligence'.

Considered the first AI program, developed by Newell & Simon in 1956, capable of proving mathematical theorems.

The first collection of articles about artificial intelligence published in 1959.

A mechanical proof procedure invented in 1965 that allowed programs to efficiently work with formal logic as a representation language.

The first successful knowledge-based program for scientific reasoning, demonstrated in 1967. It could interpret mass spectra on organic chemical compounds.

An SRI robot demonstrated in 1969 that combined locomotion, perception, and problem-solving.

A rule-based system demonstrated in 1974 that showed the power of rule-based systems for knowledge representation and inference in medical diagnosis and therapy.

Computers specialized for running Lisp, a programming language well-suited for AI development, developed and marketed in the 1980s.

Computational models inspired by the structure and function of the brain, becoming popular around 1985.

Approaches that use probability and decision theory to make decisions under uncertainty, seeing a resurgence in the late 1980s.

Artificial intelligence (AI) refers to the intelligence of machines and the computer science field dedicated to creating it. It involves designing systems that can perceive their environment and make decisions to maximize their chances of success.

AI aims to simulate human intelligence in machines by precisely defining and replicating cognitive functions, such as learning, problem-solving, and decision-making.

An intelligent agent is a system that can interact with its surroundings and make decisions based on its perception and goals to achieve optimal outcomes.

AI's foundation rests on the belief that human intelligence can be described so meticulously that it can be replicated in a machine.

The creation of artificial intelligence raises philosophical questions about the nature of consciousness and the moral implications of developing artificial beings.

AI has become an integral part of the technology industry, solving complex problems and driving advancements in various fields.

Current AI systems still have limitations, such as difficulty with tasks requiring common sense reasoning, adaptability to new environments, and understanding the nuances of human language.

AI is continually advancing, with researchers exploring new approaches and expanding its capabilities. The future holds potential for breakthroughs and applications that could transform various aspects of our lives.