Glossary
- Mike Deskis
- Ioana Kocurova
Glossary of basic AI terms and definitions as a foundational knowledge for working with Pricefx GenAI chatbot.
- 1 AI (Artificial Intelligence)
- 2 AI (Strong)
- 3 AI (Weak)
- 4 Algorithm
- 5 Algorithm Bias
- 6 Automation
- 7 Big Data
- 8 Chatbot
- 9 Cognitive Computing
- 10 Contextual Understanding
- 11 Data Mining
- 12 Data Science
- 13 Deep Learning
- 14 Ethical AI
- 15 Expert Systems
- 16 Generative AI
- 17 Inference
- 18 Large Language Model (LLM)
- 19 Machine Learning
- 20 Natural Language Generation (NLG)
- 21 Natural Language Processing (NLP)
- 22 Neural Networks
- 23 Predictive Analytics
- 24 Prompt Engineering
- 25 Tokenization
- 26 Transfer Learning
AI (Artificial Intelligence)
Refers to the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve like humans. It involves the development of algorithms and computer systems that can perform tasks that typically require human intelligence. AI encompasses a wide range of technologies and applications, from simple rule-based systems to complex neural networks that can recognize patterns, process natural language, make decisions, and adapt to new information.
AI (Strong)
Hypothetical AI with the ability to understand, learn, and apply knowledge across a broad range of tasks, similar to human intelligence. General AI is a concept that has not been fully realized.
AI (Weak)
Designed for a specific task, such as image recognition or language translation. Narrow AI excels in its predefined domain but lacks general cognitive abilities.
Algorithm
Refers to a set of rules or a step-by-step procedure designed to perform a specific task, solve a particular problem, or achieve a desired outcome within an artificial intelligence system. Algorithms are the core components that enable AI systems to process data, make decisions, and learn from experiences.
Algorithm Bias
Presence of unfair or discriminatory outcomes in the decisions made by machine learning algorithms. This bias can result from various factors, including biased training data, flawed model design, or inherent biases in the features used for decision-making.
Automation
Process of using intelligent algorithms and systems to perform tasks, processes, or functions without direct human intervention. AI-driven automation goes beyond traditional automation by incorporating elements of machine learning, natural language processing, and other AI techniques to enable systems to adapt, learn, and make decisions in response to changing conditions.
Big Data
Refers to extremely large and complex datasets that traditional data processing applications are inadequate to deal with. AI techniques are often used to analyze, process, and derive insights from big data, leveraging machine learning, natural language processing, and other AI methods to extract valuable information, patterns, and correlations that can be used for decision-making, predictive modeling, and other applications.
Chatbot
Computer program designed to simulate conversation with human users, especially over the Internet. Chatbots leverage natural language processing (NLP) and machine learning algorithms to understand and respond to user inputs, creating a conversational interaction that mimics human conversation.
Cognitive Computing
Cognitive computing involves creating computer systems that can mimic human thought processes, such as learning and problem-solving. In relation to AI, cognitive computing utilizes various AI techniques, including machine learning, natural language processing, and pattern recognition, to simulate human-like intelligence and enhance decision-making, data analysis, and interaction with users. The goal is to create systems that can understand, reason, and learn in a manner that resembles human cognition.
Contextual Understanding
Ability of an AI system to comprehend and interpret information in its surrounding context, taking into account the relationships and dependencies between elements. Contextual understanding goes beyond recognizing individual components and involves grasping the broader meaning and nuances inherent in a given situation or environment.
Data Mining
Data mining in relation to AI involves the process of discovering patterns and extracting useful information from large datasets using artificial intelligence techniques. This may include machine learning algorithms, statistical analysis, and other AI methods to uncover insights, correlations, and trends within the data that can be used for decision-making and predictive modeling.
Data Science
Involves the interdisciplinary field of study and practice that focuses on extracting insights and knowledge from data using various techniques, methodologies, and tools. While data science and AI are distinct fields, they are closely interconnected, with data science often providing the foundational data and insights that fuel the development and improvement of AI models.
Deep Learning
A subset of machine learning involves neural networks with multiple layers. Deep learning algorithms are particularly effective in tasks such as image recognition, natural language processing, and speech recognition.
Ethical AI
The practice of developing and using AI systems in a manner that aligns with ethical principles, human values, and societal norms. The goal of ethical AI is to ensure that AI technologies are designed, implemented, and deployed responsibly, with consideration for the potential impact on individuals, communities, and society as a whole.
Expert Systems
These are systems that emulate the decision-making ability of a human expert in a specific domain and were early precursors to AI applications. These systems utilize knowledge representation, inference engines, and a rule-based approach to provide reasoning and advice in complex problem-solving scenarios. Expert systems are designed to capture and apply expertise from human specialists, making them valuable for tasks such as diagnosis, troubleshooting, and decision support.
Generative AI
A category of artificial intelligence that focuses on creating or generating new content, such as text, images, audio, or other forms of data. Unlike traditional AI systems that are designed for specific tasks, generative AI is capable of producing original and diverse outputs by learning patterns from existing data.
Inference
Process of using a trained model to make predictions or generate outputs based on new, unseen data. During inference, an AI model applies the knowledge it acquired during the training phase to make decisions, classifications, or generate responses for specific inputs.
Large Language Model (LLM)
In the context of AI (Artificial Intelligence) refers to a sophisticated and highly capable natural language processing model that is trained on vast amounts of textual data. These models, often based on transformer architectures, have the ability to understand and generate human-like text across a wide range of contexts and topics.
Machine Learning
A subfield of artificial intelligence (AI) that focuses on developing algorithms and models that enable computer systems to learn and improve from experience. The core idea behind machine learning is to create systems that can automatically learn patterns, make predictions, and make decisions based on data, without being explicitly programmed for each task.
Natural Language Generation (NLG)
A subfield of artificial intelligence that focuses on the automatic generation of human-like language. NLG involves algorithms and models capable of producing coherent and contextually relevant text in various forms, ranging from simple sentences to complex documents.
Natural Language Processing (NLP)
The field of study and development that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. NLP involves the interaction between computers and natural language, allowing machines to process and analyze large volumes of textual or spoken data.
Neural Networks
Computational model inspired by the structure and functioning of the human brain. Neural networks are a fundamental component of machine learning and are widely used in various AI applications.
Predictive Analytics
Field of data analytics that leverages machine learning and statistical algorithms to analyze historical data and make predictions about future events or trends. Predictive analytics involves the use of AI models to identify patterns, relationships, and potential outcomes based on past data, enabling organizations to make informed decisions and optimize their strategies.
Prompt Engineering
The intentional design and formulation of prompts or queries given to language models or other AI systems. It involves crafting input instructions in a way that elicits the desired output or response from the AI model. Prompt engineering is crucial for obtaining accurate and relevant results, especially when working with large language models that rely on user-provided input.
Tokenization
The process of breaking down a sequence of text into individual units, known as tokens. Tokens are typically words, sub words, or characters, and tokenization is a fundamental step in preparing textual data for natural language processing (NLP) tasks, including those involving AI models.
Transfer Learning
A machine learning technique where a model trained on one task is leveraged and adapted for a different but related task. Instead of training a new model from scratch, transfer learning allows the reuse of knowledge acquired from one domain to improve the performance of a model in a different domain.