Adversarial Learning: A technique where a machine learning model is trained to withstand intentional attacks or deception.

AGI (Artificial General Intelligence): AI that could successfully do any intellectual task that can be done by any human being. This is sometimes referred to as strong AI.

AI (Artificial Intelligence): The simulation of human intelligence processes by machines or computer systems. AI can mimic human capabilities such as communication, learning, and decision-making.

AI Alignment: The process of trying to get an AI system to function as intended. Alignment covers both small, direct goals, such as writing a sentence, and large conceptual ones, such as conforming to certain values and moral standards.

AI Ethics: Refers to the issues that AI stakeholders such as engineers and government officials must consider, including fairness, transparency, and privacy.

Algorithm: A set of rules that a machine can follow to learn how to do a task.

ANI (Artificial Narrow Intelligence): AI that can solve narrow problems, such as facial recognition on a smartphone.

API (Application Programming Interface): A set of rules that allow programs to communicate with each other.

Artificial Neural Network (ANN): A computational model inspired by the human brain's neural network structure, used in AI research and applications.

ASR (Automatic Speech Recognition): A type of natural language processing that is associated with recognizing human speech.

Autoencoder: A type of neural network used for unsupervised learning, used to learn efficient representations of data.

AutoML (Automated Machine Learning): The process of automating the process of applying machine learning to real-world problems.

Autonomous: A machine is described as autonomous if it can perform its task or tasks without needing human intervention.

Backward Chaining: A method in which a machine must work from a problem to find a potential solution. By analyzing a range of hypotheses, the AI must determine those that are relevant to the problem.

BERT (Bidirectional Encoder Representations from Transformers): A transformer-based machine learning technique for natural language processing pre-training.

Bias in AI: A phenomenon where an AI system reflects the implicit values of the humans who created it, including favoring certain groups or outcomes over others.

Bias-Variance Tradeoff: The balance between the errors caused by oversimplifying or overcomplicating a model in machine learning.

Big Data: Large and complex data sets that cannot be easily managed or processed using traditional data processing methods.

Black Box Model: A model or system whose internal workings or decision-making process is not transparent or understandable.

Brute Force Search: A search that isn’t limited by clustering/approximations; it checks every possible solution to find the best one.

Capsule Networks (CapsNets): A type of deep learning model that processes data by encapsulating it in a nested set of layers, each of which captures specific features of the data.

Chatbot: A computer program or AI system designed to interact with humans in a conversational manner.

Clustering: A machine learning technique that groups similar data points together based on their similarities and differences.

Cognitive Computing: Systems that learn at scale, reason with purpose, and interact with humans naturally.

Computer Vision: An interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

Conversational AI: A form of AI that involves systems capable of engaging in natural and interactive conversations with humans.

Convolutional Neural Network (CNN): A specialized type of neural network commonly used in image and video recognition tasks.

Data Augmentation: The process of artificially increasing the size or diversity of a dataset by adding variations or modifications to existing data.

Data Labeling: The process of attaching meaningful information to the data, which can then be used for machine learning.

Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

Decision Tree: A flowchart-like structure used in machine learning to make decisions or predictions based on a set of conditions.

Deep Learning: A subfield of machine learning that focuses on using neural networks with multiple layers to learn and extract complex features from data.

Deep Reinforcement Learning: A combination of deep learning and reinforcement learning used to train agents to make complex decisions in dynamic environments.

Disambiguation - The process of resolving confusion around terms by determining the intended meaning or sense of a word or phrase based on its context.

Edge AI: A system that uses machine learning algorithms to process data generated by a hardware device at the local level.

Emergent Behavior: When an AI system shows unpredictable or unintended capabilities.

Ensemble Learning: A machine learning concept in which multiple models are trained to solve the same problem and combined to get better results.

Ethical AI: The practice of developing and using AI systems in a way that is fair, unbiased, and respects ethical principles.

Expert System: A computer program that emulates the decision-making ability of a human expert in a specific domain.

Explainable AI (XAI): AI systems that offer insights into their inner workings, making their results understandable and justifiable.

Feature Engineering: The process of using domain knowledge to create features that make machine learning algorithms work.

Federated Learning: A machine learning approach that allows for decentralized data location, where the data remains on the original device and only model updates are shared.

Fuzzy Logic: A mathematical logic that deals with uncertainty and imprecision by assigning degrees of truth to statements.

GAN (Generative Adversarial Network): A class of machine learning systems invented by Ian Goodfellow and his colleagues in 2014, where two neural networks contest with each other in a game.

Gaussian Mixture Model: A probabilistic model used for clustering and density estimation, assuming that the data points are generated from a mixture of Gaussian distributions.

Generative AI: Refers to a category of artificial intelligence (AI) that is capable of generating new content, such as text, images, audio, or code, based on patterns and structures learned from existing data

Gradient Descent: An optimization algorithm used in machine learning to minimize the error or loss function of a model.

Grid Search: The process of scanning the data to configure optimal parameters for a given model.

Hallucinations: Incorrect or misleading outputs generated by AI models that may appear to be plausible but are factually incorrect or unrelated to the context.

Heuristics: Mental shortcuts or rules of thumb that humans use to simplify problem-solving and decision-making processes. They are cognitive strategies that allow individuals to make quick judgments or reach approximate solutions without extensive analysis or computation.

Hyperparameter: A parameter in a machine learning algorithm that is not learned from the data but set before training.

Image Recognition: The ability of a computer or AI system to identify and classify objects or patterns in images.

Internet of Things (IoT): The network of physical devices, vehicles, home appliances, and other objects embedded with sensors, software, and connectivity, enabling them to exchange data.

Inverse Reinforcement Learning: A type of machine learning where an agent learns to infer an unknown reward function from observed behavior.

Large Language Model (LLM): A type of advanced artificial intelligence (AI) algorithm that is trained on massive amounts of text data to understand and generate human-like language.

Long Short-Term Memory (LSTM): A type of recurrent neural network (RNN) architecture used to model sequential data with long-term dependencies.

Machine Learning: A type of AI that allows a system to learn from data rather than through explicit programming.

Massive LLM (MLLM): Refers to the large size and high parameter count of certain language models, which allows them to capture complex language patterns and generate coherent responses.

Multimodal LLM (MLLM): Refers to language models that combine text with other types of information, such as images, videos, audio, and other sensory data.

Natural Language Processing (NLP): The ability of a computer or AI system to understand, interpret, and generate human language.

Neural Network: A computational model inspired by the structure and function of the human brain, used in machine learning and AI applications.

Overfitting: When a machine learning model performs well on the training data but fails to generalize to new, unseen data.

Predictive Analytics: The use of historical data and statistical algorithms to predict future outcomes or behaviors.

Quantum Computing: A type of computation that utilizes quantum bits (or qubits) to process data in ways that classical computers cannot.

Random Forest: An ensemble learning method that combines multiple decision trees to make more accurate predictions.

Recall: A measure of how well a model retrieves all the relevant positive instances from the total actual positive instances.

Regression Analysis: A statistical method used to model the relationship between a dependent variable and one or more independent variables.

Reinforcement Learning: A type of machine learning where an agent learns to take actions in an environment to maximize a reward signal.

Robotics: A field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently.

Semi-Supervised Learning: A learning approach in which a model is trained on a combination of labeled and unlabeled data.

Sentiment Analysis: The process of determining the sentiment or opinion expressed in a piece of text, often used to analyze customer reviews or social media posts.

Supervised Learning: A learning approach where a model is trained on labeled data, with each data point associated with a target output.

Time Series Analysis: The process of analyzing and modeling data points collected over time to identify patterns or make predictions.

Token: The basic unit of text that a large language model uses to understand and generate language. It may be a word or parts of a word.

Transfer Learning: A machine learning technique that allows a model trained on one task to be applied to a different but related task.

Underfitting: A modeling error in AI and machine learning that occurs when a machine learning model is not complex enough to accurately capture relationships between a dataset's features and a target variable.

Unsupervised Learning: A learning approach where a model is trained on unlabeled data, with the goal of finding patterns or relationships in the data.

Unsupervised Learning: A type of machine learning that trains algorithms on data without specific guidance on what to look for.

Variational Autoencoder (VAE): A type of autoencoder that can learn and generate new data points by sampling from a learned distribution.

Virtual Assistant: An AI-powered software or application that can perform tasks or provide information for users through voice or text interactions.

ZSL (Zero-Shot Learning): A problem setup in machine learning, where at test time, a learner sees classes that were not observed during training.