Artificial Intelligence (AI)
is a broad and complex field, full of specialized jargon. This article provides a glossary of essential AI terms, offering business executives a handy reference guide to the world of AI.
FOUNDATIONAL TERMS
Artificial Intelligence (AI): A branch of computer science aiming to build machines capable of performing tasks that normally require human intelligence.
Machine Learning (ML): A subset of AI that involves computers learning from data without being explicitly programmed.
Deep Learning: A type of machine learning based on artificial neural networks with multiple layers.
Neural Networks: Computational models inspired by the human brain.
Generative Artificial Intelligence: A type of AI that can create new content, such as images, text, or music, often based on learning from a set of training data.
Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language.
AI Governance: The process of managing and monitoring the use, performance, and outcomes of AI systems within an organization.
AI Ethics: A field of study and practice that seeks to ensure that AI technologies are developed and used in a manner that is ethical, fair, and beneficial to all segments of society.
MODELING & TECHNICAL TERMS
ChatGPT: An AI language model developed by OpenAI that uses machine learning to produce human-like text.
Large Language Models: These are AI models trained on vast amounts of text data.
AutoGPT: A model architecture proposed by OpenAI for both unsupervised and supervised learning.
Chatbots: Computer programs designed to simulate human conversation.
Multi-modal AI: AI systems capable of integrating and processing two or more types of input, such as text and image data.
AI Plugins: Extensions to software systems that add AI capabilities like predictive analytics, natural language processing, or image recognition, enhancing functionality without major redevelopment.
Embeddings: In machine learning, embeddings are a representation of data in a lower-dimensional space, often used for words or phrases in natural language processing.
Tokens: In language models like GPT, a token is typically a word or part of a word.
Supervised Learning: A type of machine learning where models are trained using labeled data.
Unsupervised Learning: A type of machine learning where models are trained using unlabeled data.
Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.
Transfer Learning: A machine learning method where a pre-trained model is adapted for a different but related problem.
Bias (in AI): Pre-existing beliefs or behaviors the AI has "learned" from its training data.
Computer Vision: A field of AI that trains computers to interpret and understand the visual world.
Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Decision Trees: A machine learning algorithm used for classification and regression.
Ensemble Methods: Machine learning techniques that combine several base models to produce one optimal predictive model.
Federated Learning: A machine learning approach where the training process is distributed among many users.
GAN (Generative Adversarial Network): A type of neural network where two models are trained together.
Image Recognition: The ability of an AI system to identify objects, places, people, writing, and actions in images.
Overfitting: A modeling error in machine learning that occurs when a function is too closely fit to a limited set of data points.
Precision and Recall: Precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of the total amount of relevant instances that were retrieved.
Semi-Supervised Learning: A type of machine learning that uses a combination of a small amount of labeled data and a large amount of unlabeled data for training.
Transformer Models: A type of model used in natural language processing. Transformer models, like GPT and BERT, pay attention to different parts of the input when producing an output, allowing them to generate more accurate and contextually nuanced responses.
Turing Test: A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Developed by the British mathematician Alan Turing.
BUSINESS APPLICATION TERMS
AIaaS (Artificial Intelligence as a Service): A business model where service providers offer third-party AI outsourcing so that companies can experiment with AI without significant initial investment.
AI-First: A strategic approach that prioritizes the development and application of AI in product development or business strategies.
Algorithmic Bias: Systematic errors in the output of an algorithm that create unfair outcomes, such as privileging one arbitrary group of users over others.
Automated Decision-Making: Using AI systems to make decisions without human intervention, often used in loan approvals, recruitment, and more.
Business Intelligence (BI): The strategies and technologies used by enterprises for data analysis of business information to support better business decision-making.
Conversational AI: Technology that enables computers to understand, process, and respond to voice or text inputs in natural ways, often used in customer service scenarios.
Customer Analytics: The processes and technologies that give organizations the customer insight necessary to deliver offers that are anticipated, relevant, and timely.
Data-Driven Decision Making: An approach to business governance that values decisions backed up with verifiable data over those based on intuition or observation.
Digital Transformation: The integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers.
Explainable AI (XAI): AI systems that can clearly explain their actions and decision-making processes to the average user, fostering trust and understanding.
Intelligent Automation: The application of AI and automation to perform tasks without human intervention, from simple tasks like data entry to complex ones like decision-making.
Knowledge Graph: A knowledge base that uses a graph-structured data model or topology to integrate data and information from different sources.
Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
Personalization: The process of creating customized experiences for individuals based on their preferences and behavior, often used in marketing.
Prescriptive Analytics: A type of data analytics that uses machine learning to help businesses decide a course of action, based on predictions of future scenarios.
Real-Time Analytics: The analysis of data as soon as it becomes available. This allows businesses to react without delay, for example, to stop fraudulent transactions before they are processed.
Recommender Systems: Algorithms used to suggest products or decisions to customers based on past behavior, common in online shopping sites.
Robotic Process Automation (RPA): The use of software with AI and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans.
Sentiment Analysis: A type of data mining that measures the inclination of people's opinions through natural language processing, text analysis, and computational linguistics.
Text Mining: The process of deriving significant information from text through specific data mining methods.
User Experience (UX): A person's emotions and attitudes about using a particular product, system or service, which can be greatly enhanced through personalized and intelligent AI solutions.
Virtual Assistant: An application that understands natural language voice commands and completes tasks for the user, a common application of AI in businesses.
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