Unit 1.01

The study of complex algorithms used in data compression and network security.

The science and engineering of making intelligent machines, especially intelligent computer programs.

A field that enables computers to perform tasks by following explicit, pre-programmed instructions.

A branch of software development focused on creating user-friendly interfaces.

Symbolic AI and Logic Programming

Big Data Analytics

Reinforcement Learning

Deep Learning and Neural Networks

Are powered by renewable energy sources, making them environmentally 'weak'.

Possess human-level intelligence across all cognitive tasks.

Are easily defeated in strategic games like chess or Go.

Can perform specific tasks intelligently but lack general cognitive abilities.

Herbert Simon

John McCarthy

Marvin Minsky

Alan Turing

The development of AI systems capable of operating in extreme cold environments.

Decreased funding and interest in AI research due to overly optimistic predictions and failed projects.

The emergence of new programming languages specifically designed for AI development.

Rapid advancements in AI algorithms and widespread commercial success.

The time complexity required to solve a problem using AI techniques.

The graphical user interface (GUI) of an AI application.

A set of all possible configurations or states that a problem can be in.

The physical memory available on a computer system for storing data.

Using a heuristic function to guide the search towards the most promising states.

Learning from past experiences to avoid generating incorrect solutions.

Systematically exploring all possible paths from the initial state until the goal is found.

Creating candidate solutions and then checking each one to see if it satisfies the problem's constraints.

Iterative Deepening Depth-First Search (IDDFS)

Breadth-First Search (BFS)

Depth-First Search (DFS)

Uniform-Cost Search

It can get stuck in infinite loops if cycles are present.

It requires a significant amount of memory to store all visited nodes at each level.

It is not guaranteed to find a solution if one exists.

It cannot handle problems with multiple goal states.

DFS typically requires more memory than BFS.

BFS typically requires more memory than DFS.

Memory requirements depend entirely on the specific problem instance and cannot be generalized.

Both DFS and BFS have similar memory requirements.