AI Final Keywords

Population: A group of candidate solutions to the problem.

Chromosome: A representation of a candidate solution (often as a string or array).

Fitness Function: A function used to evaluate the quality or "fitness" of a candidate solution. Is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. It is used in genetic programming and genetic algorithms to guide simulations towards optimal design solutions.

Selection: The process of choosing parent solutions based on their fitness to pass their traits to the next generation.

Crossover: A genetic operator that combines two parent solutions to create offspring solutions.

Mutation: A genetic operator that introduces random changes in offspring to maintain diversity and avoid local optima.

Generation: One iteration of the algorithm, including selection, crossover, and mutation steps.

Optimization: The process of finding the best solution or set of solutions.

Convergence: When the algorithm stabilizes, and no further improvement is observed in the fitness of the population.

Search Space: The domain of all possible solutions to the problem.

Elitism: Preserving the best individuals in the population to ensure their traits are carried forward.

Agent- A system with at least some form of intelligence considering the main 6 factors.

Agent Function- What at agent is suppose to do, its purpose.

Agent Program- An internal absolute implementation of code.

Rationality- What the agent knows about the environment and a self judgement on how it performed.

Autonomy- The ability to act on its own. Knowing where it is, what it has to do, etc.

Reflex Agent- Responding to percepts in the environment in the right way (based on its knowledge)

Model Based Agent- Has knowledge of the workings of the world.

Goal Based Agent- Has knowledge of the goal and decides what actions to take in order to reach it.

Utility Based Agent- Determines the best way to reach the goal, to keep its (own goal)

Learning Agent- Analyzes information to make improvements.

Keywords in AI, Note 2

Algorithms: A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own; classification, clustering, recommendation, and regression are four of the most popular types.

Artificial intelligence: A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior.

Artificial neural network (ANN): A learning model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to solve.

Autonomic computing: A system's capacity for adaptive self-management of its own resources for high-level computing functions without user input.

Chatbots: A chat robot (chatbot for short) that is designed to simulate a conversation with human users by communicating through text chats, voice commands, or both. They are a commonly used interface for computer programs that include AI capabilities.

Classification: Classification algorithms let machines assign a category to a data point based on training data.

Cluster analysis: A type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data; clusters are modelled with a measure of similarity defined by metrics such as Euclidean or probabilistic distance.

Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics.

Cognitive computing: A computerized model that mimics the way the human brain thinks. It involves self-learning through the use of data mining, natural language processing, and pattern recognition.

Convolutional neural network (CNN): A type of neural networks that identifies and makes sense of images.

Data mining: The examination of data sets to discover and mine patterns from that data that can be of further use.

Data science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomenon via either structured or unstructured data.

Decision tree: A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.

Deep learning: The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.

Fluent: A type of condition that can change over time.

Game AI: A form of AI specific to gaming that uses an algorithm to replace randomness. It is a computational behavior used in non-player characters to generate human-like intelligence and reaction-based actions taken by the player.

Genetic algorithm: An evolutionary algorithm based on principles of genetics and natural selection that is used to find optimal or near-optimal solutions to difficult problems that would otherwise take decades to solve.

Heuristic search techniques: Support that narrows down the search for optimal solutions for a problem by eliminating options that are incorrect.

Knowledge engineering: Focuses on building knowledge-based systems, including all of the scientific, technical, and social aspects of it.

Logic programming: A type of programming paradigm in which computation is carried out based on the knowledge repository of facts and rules; LISP and Prolog are two logic programming languages used for AI programming.

Machine intelligence: An umbrella term that encompasses machine learning, deep learning, and classical learning algorithms.

Machine perception: The ability for a system to receive and interpret data from the outside world similarly to how humans use our senses. This is typically done with attached hardware, though software is also usable.

Natural language processing: The ability for a program to recognize human communication as it is meant to be understood.

Recurrent neural network (RNN): A type of neural network that makes sense of sequential information and recognizes patterns, and creates outputs based on those calculations.

Supervised learning: A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student; more common than unsupervised learning.

Swarm behavior: From the perspective of the mathematical modeler, it is an emergent behavior arising from simple rules that are followed by individuals and does not involve any central coordination.

Unsupervised learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses. The most common unsupervised learning method is cluster analysis.

Backpropagation (BP):  An algorithm used to train ANN. BP effectively solve problem by making the training of multi-layer networks feasible and efficient. Backpropagation distributed the error term back up through the layers, by modifying the weights at each node. Backpropagation is shorthand for "the backward propagation of errors," since an error is computed at the output and distributed backwards throughout the network’s layers. It is commonly used to train deep neural networks.

 NLP: Gives machines the ability to read and understand human language.

Machine Learning:  Is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

 Deep learning: In this advanced version of machine learning, computers actually teach themselves with minimal programming by humans. Marketers can use deep learning to identify data and make predictions related to how consumers might behave.

ANN: is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

Genetic Algorithm: It is usually considered as Unsupervised learning algorithm. It is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation.

Swarm intelligence: Are computing systems inspired by the biological neural networks that constitute animal brains. The neural network is a framework for many different machine learning algorithms to work together and process complex data inputs.

Recurrent neural network (RNN): is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Learning rate: The amount that the weights are updated during training is referred to as the step size for learning.

Cognitive science: The broader form of study that includes AI in addition to philosophy, linguistics, psychology, neuroscience, and anthropology. All of these combine together to learn how the mind functions and, when applied to AI, how machines can simulate human thought and action.

The ant colony algorithm: Is an algorithm for finding optimal paths that is based on the behavior of ants searching for food.

AI programming language: Python, R, Java, C, Java Script, C++