CORPORATE AMERICA BEHIND THE WALLS OF EA NS GHTS I I
AI TERMS
Deep learning: A type of machine learning that uses neural networks with many layers to analyze various levels of data abstraction Explainability/Interpretability: Techniques and practices that help humans understand how and why an AI system makes its decisions Few-shot learning: An AI model's ability to learn and make predictions from a limited number of examples Fine-tuning: The process of further training a pre-trained AI model on a smaller, specialized dataset to improve performance on specific tasks Foundation model: A large, pre-trained AI model (often multimodal) that can be adapted for a wide range of downstream tasks Generative AI: AI models that can create new content— such as text, images, audio, or code—based on learned patterns Graphics processing unit (GPU): A specialized processor that accelerates the training and operation of AI models by handling many calculations simultaneously Hallucinations: Instances where an AI model generates incorrect or nonsensical information that appears plausible Human-in-the-loop: A process where humans are involved in reviewing or guiding AI decisions to ensure accuracy and fairness Hyperparameter tuning: The process of optimizing the parameters that govern the training of an AI model to improve its performance Inference: The process of using a trained AI model to make predictions or decisions based on new data Large language model (LLM): A type of AI that can understand and generate human-like text based on vast amounts of data Multimodal model: An AI model that can process and generate content across multiple data types, such as text, images, and audio
Natural language processing (NLP): A field of AI focused on enabling machines to understand, interpret, and respond to human language Neural network: A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process information Overfitting: A modeling error where an AI model learns the training data too well, including noise and outliers, resulting in poor performance on new data Predictive analytics: Using AI to analyze historical data and predict future outcomes or trends Reinforcement learning: A type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions Retrieval-augmented generation (RAG): An AI technique that combines retrieving relevant information from a database with generating text to provide accurate and informative responses Robotic Process Automation (RPA): Software that automates repetitive digital tasks by mimicking human actions Single-shot learning: An AI model's ability to learn from just one example or a very small number of examples Supervised learning: A type of machine learning where the model is trained on labeled data, meaning the input data is paired with the correct output Tensor processing unit (TPU): A specialized processor designed by Google to accelerate machine learning tasks Tokens: The smallest units of text (like words or characters) that an AI model processes Training data: The dataset used to teach an AI model to recognize patterns and make decisions Unsupervised learning: A type of machine learning where the model is trained on unlabeled data and must find patterns and relationships on its own Zero-shot learning: An AI model's ability to make predictions on tasks it has never seen before without any specific training
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