Artificial Intelligence(AI) is a hot topic in the worldwide. many terms are used in AI. Here are 62 Artificial Intelligence Terms You Need to Know
- Machine learning: A type of machine learning that allows computers to learn from data without explicitly being programmed.
- Neural network: A type of machine learning algorithm that is inspired by the way the human brain works. It consists of multiple layers of interconnected nodes that can process and analyze data.
- Deep learning: A type of machine learning that uses neural networks with many layers to learn patterns in data. It often use for image and speech recognition.
- Natural language processing: A branch of artificial intelligence that deals with the ability of computers to understand, interpret, and generate human language.
- Robotics: The study of robots, their design, construction, operation, and application.
- Expert system: A type of artificial intelligence that uses a knowledge base of domain-specific information and rules to solve complex problems.
- Cognitive computing: A type of artificial intelligence that aims to replicate the human brain’s ability to learn and adapt.
- Computer vision: A branch of artificial intelligence that deals with the ability of computers to interpret and analyze visual data from the world around them.
- Natural language generation (NLG): A type of artificial intelligence that allows computers to generate human-like text based on input data.
- Chatbot: A computer program designed to communicate with humans through natural language.
- Sentiment analysis: A type of artificial intelligence that analyzes text or speech to determine the underlying emotions or attitudes.
- Predictive analytics: A type of artificial intelligence that uses statistical models and machine learning algorithms to predict future events or outcomes.
- Data mining: A process of extracting useful information from large datasets using machine learning and statistical techniques.
- Decision tree: A machine learning algorithm that creates a decision-making model based on a series of binary splits.
- Clustering: A type of machine learning that groups data points into clusters based on their similarity.
- Regression: A type of machine learning that predicts a continuous numerical value based on input data.
- Classification: A type of machine learning that categorizes data points into predetermined classes based on input data.
- Supervised learning: A type of machine learning where the algorithm is trained on labelled data, meaning it has input and output data.
- Unsupervised learning: where the algorithm is not given any labelled data and must discover patterns and relationships in the data on its own.
- Reinforcement learning: A type of machine learning where the algorithm learns by taking actions in a simulated environment and receiving rewards or punishments based on its actions.
- Adaptive learning: A type of machine learning where the algorithm adjusts its parameters based on feedback from the data.
- Rule-Based Systems: a type of artificial intelligence that follows predetermined rules to make decisions.
- Transfer learning: A type of Artificial Intelligence Terms You Need to Know where a model trained on one task is used to improve performance on a related task.
- Online learning: A type of machine learning where the algorithm continually updates its model as new data becomes available.
- Batch learning: A type of machine learning where the algorithm processes all the available data at once rather than updating its model incrementally.
- Neural network architecture: The design and structure of a neural network, including the number of layers, number of nodes per layer, and type of activation function used.
- Activation function: A function used in neural networks to determine whether a node should be activated based on the input data.
- Backpropagation: A method used in neural networks to adjust the weights and biases of the nodes to improve the model’s accuracy.
- Artificial neural network (ANN): A machine learning algorithm that simulates the structure and function of the human brain.
- Deep reinforcement learning: A type of machine learning that combines deep learning with reinforcement learning. It allow for more complex and accurate decision-making.
- Random Forests: an ensemble learning algorithm that combines multiple decision trees to make more accurate predictions.
- Support Vector Machines (SVMs): a machine learning algorithm that uses linear algebra to classify data points.
- Classification: a machine learning technique to predict categorical values based on past data.
- Reinforcement Learning: a machine learning type involving an agent taking actions to maximize a reward.
- Markov Decision Processes (MDPs): a mathematical framework used in reinforcement learning to model decision-making in uncertain environments.
- Q-Learning: a reinforcement learning algorithm that uses a table or function to map states to actions.
- Evolutionary Algorithms: a type of artificial intelligence that uses the principles of natural selection to optimize a solution.
- Ant Colony Optimization: a type of optimization algorithm inspired by ants’ behaviour in finding the shortest path to food.
- Ontology alignment is aligning two or more ontologies to allow for easier communication and understanding between systems.
- Nearest Neighbor: a machine learning algorithm that classifies data points based on their similarity to the nearest data point in the training set.
- K-Means Clustering: a clustering algorithm that divides a dataset into k clusters based on the mean distance to the centroid of each cluster.
- Ontology engineering: The process of creating and maintaining an ontology.
- Latent Dirichlet Allocation (LDA): an unsupervised machine learning algorithm that discovers topics in a dataset.
- Collaborative Filtering: a recommendation system that uses the ratings of similar users to make recommendations.
- Matrix Factorization is a machine-learning technique to decompose a matrix into lower-dimensional matrices.
- Deep Belief Networks (DBNs): a type of deep learning algorithm that uses multiple layers of neural networks to learn patterns and features in data.
- Convolutional Neural Networks (CNNs): a neural network that processes and analyses images and videos.
- Recurrent Neural Networks (RNNs): A neural network that processes sequential data, such as time series or natural language.
- Long Short-Term Memory (LSTM)
- Support vector machine (SVM): A machine learning algorithm that uses a hyperplane to classify data points into different categories.
- Artificial general intelligence (AGI): A type of artificial intelligence that can perform any intellectual task that a human can.
- Genetic algorithm: A machine learning algorithm that uses principles of natural selection and genetics to optimize solutions to problems.
- Fuzzy logic: A type of artificial intelligence that allows for uncertainty and imprecision in decision-making.
- Knowledge-based system: A type of artificial intelligence that uses a database of expert knowledge to make decisions.
- Expert system shell: A software program that allows for the creation of an expert system.
- Ontology: A representation of a knowledge domain, including the relationships between concepts and terms.
- Semantic web: A vision for the future of the internet that includes using artificial intelligence to interpret and understand the meaning of web content.
- Speech recognition: The ability of artificial intelligence to recognize and interpret spoken language.
- Text analysis using artificial intelligence to process and analyze written text.
- Computer-aided design (CAD): Artificial intelligence is also use to assist in design products and structures.
- Expertise location: The use of artificial intelligence to identify individuals or organizations with specific expertise.
- Case-based reasoning: A type of artificial intelligence that uses past experiences and solutions to solve new problems.
Conclusion
In this article, we explain only 62 Artificial Intelligence terms. as Artificial Intelligence expand, many new terms discover to use.
In the end, there are many Artificial Intelligence Terms You Need to Know.
