OBA Real Intelligence on AI - Glossary of Terms and Categories

AI TERMS

Algorithm Bias:Algorithm bias refers to the tendency of machine learning algorithms to favor certain outcomes or groups of people over others due to the nature of the training data or the design of the algorithm.

Deep Learning: Deep learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to learn from large amounts of data. Deep learning has been particularly successful in tasks such as image and speech recognition.

Generative AI: Generative AI is artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. (see definitions for other types of AI below under Types of AI)

Hallucination: Hallucination refers to the phenomenon where the AI model generates outputs that are not based on the input data or are highly imaginative, inaccurate or unrealistic. Hallucinations can occur when the AI model is overfitting to the training data, extrapolating beyond the patterns it has learned, or when there are errors or inconsistencies in the model's architecture or training process.

Machine Learning: Machine learning is a subset of AI that involves developing algorithms that allow computers to learn from and make predictions or decisions based on data without being explicitly programmed to do so.

Natural Language Processing (NLP): Natural language processing is a branch of AI that focuses on the interaction between computers and human language, enabling computers to understand, interpret, and generate human language.

Neural Network: A neural network is a computer system inspired by the structure of the human brain, composed of interconnected nodes (neurons) that process and transmit information. Neural networks are commonly used in deep learning, a subfield of machine learning.

Overfitting: Overfitting occurs when a machine learning model performs well on the training data but fails to generalize to new, unseen data. This can happen when the model is too complex or when there is not enough data to adequately train the model.

Predictive Analytics: Predictive analytics involves using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In the legal context, predictive analytics can be used to forecast case outcomes or determine the potential risks of legal decisions.

Prompting: In the context of generative AI, prompting refers to providing the AI model with a starting point or input to generate a desired output. The prompt – or question – can be a few words, a sentence, a paragraph, or even an image, depending on the type of AI model and the task at hand.

Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent receives feedback in the form of rewards or penalties based on its actions.

Underfitting: Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, leading to poor performance on both the training and test data.