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Join Jeff Crume from IBM as he dives into the distinctions between Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Foundation Models and how these technologies have evolved over time.
Artificial Intelligence (AI) is any intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. These machines use mathematical models that identify and encode patterns in data sets, which can then perform predictions on new situations which they haven’t encountered before.
Bias in AI models refers to output errors caused by skewed training data. Such bias can cause models to produce inaccurate, offensive, or misleading predictions. Biased AI models arise when algorithms prioritize irrelevant or misleading data traits over meaningful patterns (Smith, 2019).
Chatbots (or “bots”) are programs designed to help human users with simple tasks and communicate via voice or text commands in order to feel like a human-to-human conversation.
Generative AI is a broad label that's used to describe any type of artificial intelligence that uses learning algorithms to create new digital images, video, audio, text, or code.
Hallucinations happen when large language models generate inaccurate or illogical answers due to data and architecture constraints.
Large Language Models work by forecasting word sequences. Large language models’ capabilities have rapidly advanced in the last year and continue to evolve with increased use. They can now hold dialogues, write prose, and scrutinize enormous text quantities from the internet.
Machine learning involves creating models by training an algorithm to make predictions or decisions based on data. One of the most popular types of machine learning algorithm is called a neural network.
AI terms + education A glossary from TurnItIn
