Artificial Intelligence Developments Overview

A test that assesses a computer's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

The period from 1952 to 1956 marked the beginning of Artificial Intelligence as a field of study.

The first AI program, created by Allen Newell and Herbert Simon, aimed to solve mathematical theorems. It successfully proved 38 out of 52 theorems.

The term "Artificial Intelligence" was coined by John McCarthy at the Dartmouth Conference in 1956. This event established AI as a distinct field.

This era, from 1956 to 1974, saw a surge of enthusiasm and progress in AI research, focusing on developing powerful algorithms capable of solving complex mathematical problems.

The first AI chatbot created by Joseph Weizenbaum in 1966, ELIZA, was designed to engage in human-like conversations.

WABOT-1, the first intelligent humanoid robot built in Japan in 1972, had simple cognitive abilities and could interact with humans.

The first AI winter, from 1974 to 1980, was a period of reduced funding and interest in AI research.

A period of reduced funding and interest in artificial intelligence research. This was caused by unrealistic expectations and limited computing power.

A type of AI system that uses rules and knowledge to solve specific problems, often in specialized domains.

A computer program designed to interact with users in a way that simulates a conversation, often using natural language processing.

A set of techniques that allow computers to learn from data without explicit programming, enabling them to perform complex tasks that were previously difficult for machines.

A software program designed to automate tasks, learn from experience, and adapt to changing conditions.

The use of AI in business to improve efficiency, optimize processes, and gain insights from data.

A branch of artificial intelligence that focuses on processing and analyzing large datasets to extract meaningful insights and patterns.

A type of AI that performs specific tasks by following pre-programmed rules or learning patterns from data, like image recognition or voice assistants. It operates within specific constraints and doesn't possess consciousness or understanding.

Hypothetical AI that can perform any intellectual task a human can, including learning, reasoning, problem-solving, and creativity. It's still under development, but concepts like ChatGPT-4 and self-driving cars show its potential.

A theoretical AI that surpasses human intelligence in all areas, possessing extraordinary problem-solving and creative abilities. It could be capable of solving problems and making innovations that are currently beyond human reach.

The simplest type of AI that can only react to immediate situations without learning or remembering past experiences. It's like a calculator that only performs calculations based on the current input.

The ability of an AI to adapt to new situations and apply its knowledge and skills to tasks it hasn't been specifically trained for. This is a crucial characteristic of AGI.

The ability of an AI to understand and reason about information, like a human can. It involves processing information, drawing inferences, and solving problems.

The ability of an AI to continuously learn and improve its skills over time, similar to how humans learn through experience. This is essential for AI to keep up with evolving environments.

The ability of an AI to adapt its behavior and strategies to new circumstances and changes in the environment. This is crucial for handling unexpected situations.

A function that assigns a score to each possible outcome based on how desirable it is for the agent. The agent then chooses the action that leads to the outcome with the highest utility score.

The agent chooses the action that leads to the outcome with the greatest utility value, as calculated by the utility function.

A type of AI agent that can learn and adapt from new experiences. This learning is integrated into its knowledge base, allowing it to perform better in unfamiliar environments.

The element of a learning agent that evaluates the quality of its responses and provides feedback on whether they meet the expected performance standards. It acts as a 'teacher' or 'quality control' mechanism for the agent's actions.

The process of improving an agent's knowledge through interaction with its environment. This learning involves analyzing information collected through the agent's sensors and precepts (observations).

The element of a learning agent responsible for selecting actions after learning and knowledge acquisition. It uses the new knowledge it's gained to make better decisions.

The element of a learning agent that generates different action proposals for the agent to try. It suggests new strategies for the agent to explore and learn from.

The balance a learning agent must achieve between Exploring new options to enhance its knowledge and exploiting its existing knowledge to perform well in its environment.

A search algorithm is complete if it guarantees to find a solution if one exists for any given input. It doesn't necessarily find the best solution.

A search algorithm is optimal if the solution it finds is the best possible solution, meaning the lowest path cost or shortest distance. It doesn't always have to find a solution.

The time complexity of a search algorithm measures how long it takes to complete its task, considering the input size. It describes how the time grows with the input.

Space complexity of a search algorithm is the maximum amount of storage space used during the search. This depends on the problem's complexity and how much data the algorithm stores.

An informed search algorithm utilizes additional knowledge about the problem domain, such as heuristics, to guide its search. It uses shortcuts or educated guesses to find the goal faster.

An uninformed search algorithm works blindly without any additional knowledge about the problem domain. It explores all possible options systematically and methodically.

Heuristics are functions estimating the cost or distance to reach the goal from a given state. They guide informed search algorithms in the right direction by providing shortcuts based on prior knowledge.

Uninformed search algorithms explore all possible paths without any knowledge about the problem, making them less efficient than informed search algorithms, which utilize prior knowledge or heuristics.

A data structure that follows the "First In, First Out" (FIFO) principle. Elements are added to the rear and removed from the front.

A data structure that follows the "Last In, First Out" (LIFO) principle. Elements are added and removed from the top.

A graph traversal algorithm that explores all nodes at the current level before moving to the next level. It uses a Queue to store the nodes to be visited.

A search algorithm that explores all possible paths from a starting node until the goal node is found. It is optimal, meaning it guarantees the shortest path.

A heuristic function that always underestimates the true cost to reach the goal. This ensures that the algorithm finds the optimal solution.

A search algorithm that uses a heuristic function to guide its search for the goal node. It can be more efficient than a complete search, but may not always find the optimal solution.

A branch of Artificial Intelligence (AI) that focuses on creating intelligent agents that can reason, learn, and act autonomously.

A measure of how efficiently an algorithm finds solutions. It considers both the computational cost and the performance.