Adversarial Attack: Malicious inputs designed to confuse AI models, especially deep learning systems, causing them to misbehave.
Algorithm: A set of rules or procedures that a computer follows to perform a task. In AI, algorithms are used to find solutions or make decisions based on data.
Alignment: Adjusting an AI to optimize its outcomes, ensuring it behaves as intended.
Anthropomorphism: Assigning human traits to non-human entities. In AI, it's perceiving a machine as having emotions or consciousness.
Artificial General Intelligence (AGI): AI systems that possess the ability to understand, learn, and perform any intellectual task that a human can.
Artificial Intelligence (AI): A branch of computer science aiming to create machines that can perform tasks requiring human-like intelligence.
AI Ethics: Guidelines to ensure AI operates without causing harm to humans, focusing on data collection and bias mitigation.
AI Safety: A study of AI's long-term effects, especially the potential emergence of a superintelligent AI that might be adversarial to humans.
Autonomous Systems: Machines or systems that can perform tasks without human intervention, often based on AI technologies.
Backpropagation: A supervised learning algorithm used for training artificial neural networks, especially in deep learning.
Bias in AI: When AI systems display prejudice or partiality due to flaws in their training data or algorithms.
Big Data: Extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations.
Capsule Networks: A type of deep learning algorithm that can recognize patterns in data hierarchically, improving the accuracy and robustness of neural networks.
Chatbot: A software application designed to simulate human conversation.
ChatGPT: A chatbot by OpenAI, powered by advanced language models.
Cognitive Computing: Systems that mimic human cognitive functions such as learning, reasoning, and language understanding.
Computer Vision: A field of AI that trains machines to interpret and make decisions based on visual data.
Convolutional Neural Network (CNN): A type of deep learning algorithm primarily used for image and video recognition.
Data Augmentation: Enhancing training data by modifying or adding diverse data points.
Data Mining: The process of discovering patterns and knowledge from large amounts of data.
Deep Learning: A subset of machine learning that uses neural networks with many layers to analyze data.
Diffusion: A machine learning technique that introduces noise to data and then attempts to reconstruct the original data.
Emergent Behavior: Unexpected capabilities shown by an AI model.
Ensemble Learning: Using multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent algorithms.
End-to-End Learning (E2E): A deep learning approach where a model learns to perform a task in its entirety, rather than in stages.
Ethical Considerations: Recognizing the moral implications of AI, including privacy, fairness, and safety concerns.
Evolutionary Algorithms: Optimization algorithms based on the process of natural selection.
Expert System: A computer system that emulates the decision-making ability of a human expert in specific domains.
Feature Engineering: The process of selecting and transforming variables when creating a predictive model.
Foom: The idea that once a true AGI is created, it might rapidly surpass human intelligence, potentially endangering humanity.
Fuzzy Logic: A computing approach based on "degrees of truth" rather than the usual true or false binary logic.
GAN (Generative Adversarial Network): A type of neural network where two networks are trained together. One generates content and the other evaluates its authenticity.
Generative AI: AI that produces content, such as text or images, based on patterns learned from training data.
Google Bard: Google's AI chatbot, similar to ChatGPT, but with real-time web data access.
Guardrails: Measures implemented to ensure AI operates responsibly and avoids generating harmful content.
Hallucination: When AI produces confident but incorrect outputs.
Heuristic: A problem-solving technique designed for solving a problem more quickly when classic methods are too slow.
Hidden Layer: In neural networks, layers between the input and output layers where artificial neurons process and transform inputs.
Hyperparameter: Parameters in machine learning models that are set before training, such as learning rate or batch size.
Image Recognition: The process of identifying and detecting objects or features in a digital image.
Inference: The process of making predictions using a trained machine learning model.
Information Retrieval: The process of obtaining information from a database or system based on a query.
Knowledge Graph: A structured representation of knowledge with entities, attributes, and relationships.
Large Language Model (LLM): An AI trained on vast text data to understand and generate human-like language.
Latent Variable: A variable that is not directly observed but inferred from other variables in a model.
Learning Rate: A hyperparameter that determines the step size at each iteration while moving towards a minimum in the loss function.
Loss Function: A function that measures the difference between the predicted output and the actual output in machine learning.
Machine Learning (ML): A subset of AI that allows systems to learn and improve from experience without being explicitly programmed.
Microsoft Bing: Microsoft's search engine that integrates ChatGPT-like technology for AI-enhanced search results.
Model: In machine learning, a representation of a system based on examples or data.
Multimodal AI: AI capable of processing various data types, such as text, images, and speech.
Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language.
Neural Network: A series of algorithms that attempts to recognize patterns in data.
Node: A basic unit of a data structure, such as a neuron in neural networks.
Overfitting: A modeling error that occurs when a function is too closely aligned to a limited set of data points.
Parameters: Numerical values that shape the behavior of LLMs, guiding their predictions.
Perceptron: A type of artificial neuron used in machine learning.
Prediction: The output of a machine learning model after being given an input.
Prompt Chaining: AI's capability to use past interactions to influence future responses.
Recurrent Neural Network (RNN): A type of neural network where connections between nodes form a cycle, allowing for sequential data processing.
Reinforcement Learning: A type of machine learning where an agent learns by interacting with an environment and receiving feedback.
Semi-supervised Learning: A machine learning method that uses both labeled and unlabeled data for training.
Stochastic Parrot: A metaphor for LLMs, highlighting their ability to mimic language without grasping its deeper meaning.
Style Transfer: Adapting the visual style of one image to another, like merging Rembrandt's techniques with Picasso's.
Supervised Learning: A machine learning method where the model is trained on labeled data.
Support Vector Machine (SVM): A supervised machine learning algorithm used for classification or regression.
Temperature: A setting that influences the randomness of an AI model's outputs.
Tensor: A mathematical object analogous to vectors and matrices, used in deep learning frameworks.
Text-to-Image Generation: Producing visual content based on textual descriptions.
Training Data: Datasets used to educate AI models
Transfer Learning: A machine learning method where a model developed for one task is reused as the starting point for another task.
Transformer Model: A neural network design that understands context by analyzing relationships in data.
Turing Test: A test to measure a machine's human-like behavior, named after Alan Turing.
Unsupervised Learning: A machine learning method where the model is trained on unlabeled data.
Validation Set: A subset of data used to evaluate the performance of a machine learning model during training.
Weak AI: AI specialized in a specific task, lacking the ability to learn beyond its initial programming.
Weight: The strength or value of a connection between two nodes in a neural network.
XGBoost: An optimized gradient boosting library used for supervised learning tasks.
Zero-shot Learning: A machine learning method where the model is trained to handle tasks it has never seen during training.