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• Artificial Intelligence (AI): Technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Source: IBM
• Chatbot: Chatbot is the most inclusive, catch-all term. Any software simulating human conversation, whether powered by traditional, rigid decision tree-style menu navigation or cutting-edge conversational AI, is a chatbot. Chatbots can be found across nearly any communication channel, from phone trees to social media to specific apps and websites. Source: IBM
• Conversational AI: Refers to technologies, such as chatbots or virtual agents, that users can talk to. They use large volumes of data, machine learning and natural language processing to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages. Source: IBM
• Decision Tree: A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes. Source: IBM
• Deep Learning: Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain. Source: IBM
• Generative AI: Refers to a class of artificial intelligence systems designed to generate new content or data based on the patterns and information they have learned from training datasets. Unlike traditional AI, which may simply analyze and classify existing data, generative AI creates new, original outputs, such as text, images, audio, or video. Source: IBM
• Large Language Models (LLMs): A category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs are a class of foundation models, which are trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications, as well as resolve a multitude of tasks. Source: IBM
• Machine Learning:  Involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.There are many types of machine learning techniques or algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines (SVMs), k-nearest neighbor (KNN), clustering and more. Source: IBM
• Natural Language Processing (NPL): Enables computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics—the rule-based modeling of human language—together with statistical modeling, machine learning and deep learning. NLP makes it easier for humans to communicate and collaborate with machines, by allowing them to do so in the natural human language they use every day. Source: IBM