Artificial Intelligence (AI): The simulation of human intelligence in machines that can perform tasks, learn from experience, and adapt to new inputs without explicit programming.
Big Data: Extremely large and complex datasets that require advanced computational and analytical techniques, often used in AI training and decision-making.
Chatbot: A computer program that uses AI to conduct a conversation with human users through natural language processing.
Deep Learning: A subset of machine learning where artificial neural networks model and process data, allowing AI systems to learn and make decisions without explicit programming.
Ethical AI: The implementation of AI systems and algorithms that adhere to ethical principles, ensuring fairness, transparency, and responsible decision-making.
Feature Engineering: The process of selecting and transforming relevant variables from raw data to prepare them for input into machine learning algorithms.
General AI (AGI): Artificial Intelligence capable of understanding, learning, and performing any intellectual task that a human being can do.
Hyperparameter: Parameters in machine learning algorithms that are set before the learning process begins and influence the model’s performance.
Intelligent Agent: An autonomous entity that perceives its environment and takes actions to achieve specific goals.
Jupyter Notebook: An interactive computing environment that allows users to create and share documents containing code, visualizations, and explanatory text.
Knowledge Graph: A structured representation of information that helps AI systems understand the relationships between entities and concepts.
Machine Learning (ML): A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Natural Language Processing (NLP): The ability of AI systems to understand, interpret, and generate human language.
Overfitting: Occurs when a machine learning model is too complex and performs well on the training data but poorly on new, unseen data.
Pretrained Model: A machine learning model that has been trained on a large dataset and can be fine-tuned for specific tasks.
Quantum AI: The application of quantum computing principles to solve complex AI problems.
Reinforcement Learning: A type of machine learning where an agent learns to achieve goals in an environment through trial and error.
Supervised Learning: A type of machine learning where the algorithm is trained on labeled data, making predictions based on known outcomes.
Transfer Learning: The process of using knowledge from one task to improve learning and performance on a related task.
Unsupervised Learning: A type of machine learning where the algorithm is trained on unlabeled data, finding patterns and relationships without explicit guidance.
Virtual Assistant: An AI-powered program that provides assistance and performs tasks for users based on natural language input.
Weak AI (Narrow AI): AI systems designed to perform specific tasks without the ability to exhibit general intelligence.
XAI (Explainable AI): AI systems with transparency, allowing users to understand how decisions are made and providing explanations for their outputs.
Yield Curve Predictions: The use of AI algorithms to forecast future changes in yield curves in financial markets.
Zero-Shot Learning: A machine learning approach where AI systems can recognize and classify objects or data for which they have not been explicitly trained.
