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Updated 6/15/25
New & Trending AI Terms for 2025
- Agentic AI
AI systems capable of autonomous decision-making and action without human intervention. These agents can adapt to changing environments and are increasingly utilized in areas like cybersecurity, customer support, and workflow automation. - Vibe Coding
An AI-assisted programming approach where developers describe desired outcomes in natural language prompts, allowing large language models (LLMs) to generate the corresponding code. This method shifts the developer’s role from manual coding to guiding and refining AI-generated code. - Generative Engine Optimization (GEO)
A strategy focused on enhancing content visibility within AI-generated responses, such as those from ChatGPT or Google’s Gemini. GEO involves optimizing digital content to be more accessible and favorable to generative AI systems, distinguishing it from traditional SEO practices. - Living Intelligence
A concept describing the convergence of AI, biotechnology, and advanced sensors to create systems that can sense, learn, adapt, and evolve. These systems aim to mimic aspects of living organisms, leading to applications in healthcare, environmental monitoring, and adaptive technologies. - Neuro-Symbolic AI
An AI paradigm that combines neural networks’ learning capabilities with symbolic reasoning’s interpretability. This hybrid approach aims to enhance AI’s ability to reason, learn from fewer examples, and provide more explainable outcomes.
| Term | Definition |
|---|---|
| Agentic AI | A form of artificial intelligence that can make autonomous decisions and take actions based on goals, without human intervention. |
| Vibe Coding | An emerging practice of programming or prompting AI systems based on emotional tone or creative ‘vibes’ rather than strict logic. |
| Prompt Engineering | The process of crafting effective prompts to guide AI models, especially language models, to produce desired outputs. |
| LLM (Large Language Model) | A type of AI model trained on vast amounts of text data to understand and generate human-like language. |
| Neural Networks | A set of algorithms modeled after the human brain, used to recognize patterns and make predictions in machine learning. |
| AGI (Artificial General Intelligence) | A theoretical AI that can understand, learn, and apply intelligence across a wide range of tasks at a human level. |
| NLP (Natural Language Processing) | A field of AI focused on enabling machines to understand, interpret, and generate human language. |
| Multimodal AI | AI systems that can process and understand multiple types of input, such as text, images, audio, and video simultaneously. |
| Generative Pre-Trained Transformers (GPT) | A family of LLMs trained to generate human-like text, such as ChatGPT, by predicting the next word in a sentence. |
| Chain of Thought Reasoning | A prompting technique where the AI is encouraged to explain its reasoning step-by-step, leading to more accurate results. |
| Human-in-the-Loop | An approach where human oversight or input is integrated into the AI decision-making process to improve accuracy and safety. |
| Transformers | A deep learning architecture that processes sequential data using attention mechanisms, foundational to models like GPT and BERT. |
| Reinforcement Learning | A type of machine learning where agents learn by receiving rewards or penalties based on their actions in an environment. |
| Few-shot Learning | The ability of an AI model to learn a task from only a few examples or prompts. |
| Zero-shot Learning | The ability of an AI model to perform tasks without any prior training examples by leveraging its general knowledge. |
| Self-Supervised Learning | A training method where AI models learn from data without explicit labels by predicting parts of the input data itself. |
| Foundation Model | A large AI model trained on broad data at scale and adaptable to a wide range of downstream tasks. |
| Fine-Tuning | Adjusting a pre-trained AI model with additional training on specific data to improve performance on a targeted task. |
| Alignment | Ensuring that AI systems act in accordance with human values, goals, and ethical standards. |
| Hallucination (in AI) | When an AI model generates false or fabricated information that appears plausible but is not based on real data. |
| Bias in AI | Systematic errors in AI predictions or behavior due to prejudiced training data or flawed model design. |
| Ethical AI | The practice of designing and deploying AI systems that prioritize fairness, transparency, accountability, and respect for human rights. |
| Explainable AI (XAI) | AI systems designed to make their decision-making processes transparent and understandable to humans. |
| Diffusion Models | A class of generative models that iteratively refine noise into data (e.g., images), often used in tools like Stable Diffusion. |
| Retrieval-Augmented Generation (RAG) | An AI approach that combines information retrieval with text generation to produce accurate, fact-based outputs. |
| Model Interpretability | The degree to which a human can understand the reasoning behind an AI model’s decisions or predictions. |
| Federated Learning | A decentralized approach to training AI models across multiple devices or locations without sharing raw data. |
| Synthetic Data | Artificially generated data used to train or test AI models when real data is scarce, sensitive, or expensive. |
| Model Compression | Techniques used to reduce the size of AI models to improve efficiency, speed, or deployment feasibility. |
| Inference | The process of using a trained AI model to make predictions or generate outputs based on new input data. |
| Tokenization | The process of breaking down text into smaller units (tokens) that AI models can process. |
| Embeddings | Numerical representations of words, phrases, or concepts that capture their meaning and relationships. |
| Latent Space | A multidimensional representation within AI models where abstract features of data are mapped and manipulated. |
| Vector Database | A specialized database designed to store and search high-dimensional vectors, often used for embeddings. |
| Semantic Search | A search method that understands the meaning and context of queries rather than relying on keyword matching. |
| Knowledge Graph | A network of real-world entities and their relationships, used to enhance AI understanding and reasoning. |
| Causal AI | AI models designed to understand cause-and-effect relationships, not just correlations, enabling better decision-making. |
| AutoML | Automated machine learning that simplifies model selection, training, and tuning for users with limited expertise. |
| Few-shot Prompting | Providing a model with a few examples within the prompt to guide its understanding of a task. |
| Prompt Chaining | Linking multiple prompts together so that the output of one prompt becomes the input for the next. |
| Temperature (AI Parameter) | A parameter that controls the randomness of AI-generated text; lower values make output more focused, higher values more creative. |
| Top-K Sampling | A sampling technique where the model chooses the next word from the top K most likely options, adding diversity to output. |
| Top-P Sampling | Also known as nucleus sampling, it selects the next word from the smallest set of words whose cumulative probability exceeds a threshold P. |
| LoRA (Low-Rank Adaptation) | A technique for fine-tuning large models efficiently by adjusting only a small subset of parameters. |
| Attention Mechanism | A component in transformer models that allows the AI to focus on relevant parts of the input data for each output decision. |
| Cross-Attention | A type of attention where one input sequence (like a question) attends to another (like a passage), used in translation or Q&A models. |
| Self-Attention | An attention mechanism where each part of a sequence considers every other part, key to how transformers understand context. |
| Positional Encoding | Adds information about the position of tokens in a sequence, helping transformers process word order. |
| Context Window | The amount of input text an AI model can consider at one time; limited by the model’s architecture and memory. |
| Red Teaming (AI Safety) | A practice where experts intentionally probe AI systems for flaws, biases, or vulnerabilities to improve safety. |
| Guardrails | Predefined rules or constraints placed on AI outputs to ensure safe and appropriate behavior. |
| Safety Layer | An additional system or process that oversees and moderates AI behavior to prevent harmful or unsafe actions. |
| Chain of Density Prompting | A prompting method that guides AI to generate increasingly informative responses in structured stages. |
| System Prompt | A hidden instruction given to an AI model to guide its behavior and tone throughout a session. |
| Persona Prompt | A prompt designed to make the AI adopt a particular personality, role, or point of view in its responses. |
| Function Calling | A capability that allows AI to call software functions or APIs during its execution for enhanced interactivity. |
| Agents | AI systems that can perform tasks autonomously, often coordinating actions and tools based on goals and feedback. |
| Autonomous Agents | Advanced AI systems capable of setting and executing goals independently, often used in simulations or planning. |
| Open-Source LLMs | Large language models whose code and training data are publicly available for research or modification. |
| Closed-Source LLMs | Proprietary large language models developed by companies with restricted access to their code or data. |
| Chatbot | A software application that simulates human conversation through text or voice interactions, often used in customer service. |
| Conversational AI | AI systems designed to engage in dialogue with users, understanding context and generating natural, coherent responses. |
| Voice Cloning | A technique that replicates a person’s voice using AI models trained on audio samples. |
| Text-to-Image | AI models that generate images based on textual descriptions, e.g., DALL·E. |
| Text-to-Video | AI models that create video content from written prompts, still in early development. |
| Text-to-Speech | Technology that converts written text into spoken voice output using synthetic voices. |
| Speech Recognition | AI that converts spoken language into text, enabling voice commands and transcription. |
| Emotion AI | AI systems that detect and respond to human emotions using data such as facial expressions, voice tone, or text. |
| Anthropomorphism in AI | The tendency to attribute human traits, emotions, or intentions to AI or machines. |
| AI Alignment Problem | The challenge of ensuring that AI systems’ goals and behaviors are aligned with human values and intentions. |
| AI Governance | Policies, regulations, and oversight frameworks designed to guide the ethical development and use of AI. |
| AI Auditing | The process of evaluating AI systems to ensure they meet standards for fairness, accuracy, transparency, and safety. |
| Data Drift | A change in the input data over time that can cause a model’s performance to degrade. |
| Concept Drift | A shift in the underlying relationship between input and output variables over time, impacting model accuracy. |
| Model Monitoring | Ongoing tracking of AI system performance in real-world settings to detect issues like drift or errors. |
| Explainability Dashboard | A tool or interface that helps visualize and explain how AI models make decisions. |
| AI Literacy | The understanding of key AI concepts and implications, enabling informed use and oversight of AI technologies. |
| Prompt Library | A curated collection of effective prompts used to guide AI models in generating specific outputs. |
| Few-shot Examples | A set of example inputs and outputs included in a prompt to show an AI model how to perform a task. |
| Prompt Injection | A security risk where malicious input is designed to manipulate or override an AI model’s behavior. |
| Context Injection | A technique to insert relevant background or situational data into a prompt to improve AI response accuracy. |
| Semantic Segmentation | An image analysis task where each pixel is classified into a category, often used in computer vision applications. |
| YOLO (You Only Look Once) | A real-time object detection algorithm known for its speed and accuracy in identifying objects in images or video. |
| GANs (Generative Adversarial Networks) | A framework where two neural networks compete to improve the quality of generated data like images or music. |
| Diffusion Transformers | Models that combine diffusion processes with transformer architectures to improve generative capabilities. |
| Stable Diffusion | A popular open-source AI model for generating detailed images from text, based on a diffusion process. |
| CLIP Model | A model developed by OpenAI that understands images and text together, enabling cross-modal tasks like image generation and search. |
| BERT | Bidirectional Encoder Representations from Transformers, a model that understands context in language by looking at words in both directions. |
| RoBERTa | A robustly optimized version of BERT that improves performance through better training practices. |
| T5 | Text-To-Text Transfer Transformer, a model that converts all NLP tasks into a text-to-text format for unified training and use. |
| DALL·E | An AI model by OpenAI that generates images from textual descriptions, combining creativity and control. |
| Whisper (OpenAI) | A speech recognition model by OpenAI capable of transcribing and translating audio with high accuracy. |
| Gemini (Google) | Google DeepMind’s suite of advanced multimodal AI models designed for diverse applications. |
| Claude (Anthropic) | A family of conversational AI models developed by Anthropic, focused on safety and constitutional principles. |
| Grok (xAI) | An AI assistant created by Elon Musk’s xAI company, integrated with X (formerly Twitter). |
| Mistral AI | An AI company and open-source model family from Europe focused on high-performance language modeling. |
| LLaMA (Meta) | Large Language Model Meta AI, Meta’s family of open-source LLMs built for accessibility and transparency. |
| Falcon | A set of powerful open-source LLMs developed by the Technology Innovation Institute (TII) in the UAE. |
| GPT-4 | A multimodal large language model from OpenAI known for its improved reasoning, accuracy, and contextual understanding. |
| GPT-4o | An optimized, faster version of GPT-4 that integrates vision, text, and audio for real-time multimodal interactions. |
| ChatGPT | A conversational AI application developed by OpenAI, based on the GPT family of models, designed for dialogue and task assistance. |
| LangChain | An open-source framework for building AI applications that use LLMs, enabling chaining of prompts, tools, and memory. |
| Pinecone | A vector database optimized for similarity search and real-time retrieval of embeddings used in AI applications. |
| Chroma | An open-source vector database for building AI apps that need memory, such as chatbots or recommendation systems. |
| Weaviate | A cloud-native vector database that enables semantic search and uses AI models to store and retrieve unstructured data. |
| LlamaIndex | A tool that connects LLMs to external data sources like PDFs, databases, and APIs to create context-aware AI applications. |
| AI Orchestration | Coordinating multiple AI models, APIs, and tools to work together for complex, multi-step tasks in applications. |
| Prompt Templating | Creating reusable prompt structures with placeholders to streamline and standardize AI interactions. |
| LangGraph | A framework for building AI agents using stateful, multi-step workflows structured as graphs of interactions. |
| Auto-GPT | An experimental open-source application where GPT models autonomously complete tasks by generating and following goals. |
| BabyAGI | A lightweight autonomous AI agent that uses task management and memory to achieve objectives with minimal input. |
| CrewAI | A multi-agent framework for managing and assigning roles to different AI agents working collaboratively on tasks. |
| AgentOps | A toolkit for managing, deploying, and monitoring AI agents in production environments. |
| AI Plugins | Extensions that enhance AI capabilities by connecting to external services, tools, or data sources via APIs. |
| Text Embedding Models | AI models that convert text into numerical vectors that capture semantic meaning for comparison or search. |
| OpenAI API | A commercial interface for accessing OpenAI’s models like GPT, Whisper, and DALL·E through web-based requests. |
| Anthropic API | A service providing programmatic access to Claude, Anthropic’s AI assistant, for text generation and interaction. |
| Open Source Weights | Publicly released AI model parameters that allow developers to run and modify models on their own infrastructure. |
| Model Card | A documentation framework that outlines an AI model’s capabilities, limitations, training data, and ethical considerations. |
| Inference Cost | The computational and financial cost of generating responses from an AI model, often tied to model size and usage. |
| Token Limit | The maximum number of tokens (words or word fragments) an AI model can process in a single prompt and response cycle. |
| Context Length | The amount of prior text an AI model can remember and reference in generating its responses. |
| Model Latency | The time delay between sending a request to an AI model and receiving a response, often influenced by model size and hardware. |
| Hardware Acceleration | The use of specialized hardware like GPUs or TPUs to speed up AI model training and inference. |
| TPU (Tensor Processing Unit) | A custom AI accelerator developed by Google designed to speed up machine learning computations. |
| GPU (Graphics Processing Unit) | A processor well-suited for parallel tasks like AI model training and inference, widely used in deep learning. |
| Alan Turing | A pioneer of computer science and artificial intelligence, best known for the Turing Test and foundational theoretical work. |
| Turing Test | A test proposed by Alan Turing to determine if a machine exhibits human-like intelligence in conversation. |
| Geoffrey Hinton | A key figure in AI and deep learning, known for work on neural networks and co-winning the 2018 Turing Award. |
| Yoshua Bengio | AI researcher and Turing Award winner recognized for contributions to deep learning and neural network architectures. |
| Yann LeCun | A pioneer in convolutional neural networks and deep learning, also a Turing Award winner and Meta’s Chief AI Scientist. |
| Deep Blue | A chess-playing supercomputer developed by IBM that defeated world champion Garry Kasparov in 1997. |
| AlphaGo | An AI developed by DeepMind that defeated top human players in the complex board game Go using deep reinforcement learning. |
| Watson (IBM) | An AI system by IBM that gained fame by winning Jeopardy! and was later used in medical and business applications. |
| DeepMind | An AI research lab owned by Alphabet (Google’s parent) known for breakthroughs in deep learning and reinforcement learning. |
| John McCarthy | Credited with coining the term ‘Artificial Intelligence’ and a key figure in early AI research and programming languages. |
| Eliza (Chatbot) | One of the first natural language processing programs, simulating a psychotherapist through scripted responses. |
| SHRDLU | An early AI system that could interact with and manipulate objects in a virtual world using natural language commands. |
| Perceptron | A type of neural network and one of the earliest models for binary classifiers, foundational in AI history. |
| Expert System | An early form of AI that used rule-based logic and a knowledge base to mimic human decision-making in specific domains. |
| Rule-Based System | An AI system that uses manually defined rules to make decisions or solve problems based on logical inference. |
| Symbolic AI | An early AI approach focused on manipulating symbols and rules to represent knowledge and reasoning processes. |
| Connectionism | An approach in AI that models mental or behavioral phenomena as the emergent processes of interconnected networks, like neural nets. |
| Cyborg | A being with both organic and biomechatronic parts, often used metaphorically in AI discussions to describe human-AI augmentation. |
| Turk (Mechanical) | A historical hoax from the 18th century that appeared to be a chess-playing machine but was operated by a hidden human. |
| Singularity | A theoretical point where technological growth becomes uncontrollable and irreversible, often linked with superintelligent AI. |
| Moore’s Law | An observation that the number of transistors on a chip doubles approximately every two years, driving computing progress. |
| DARPA Grand Challenge | A U.S. government-sponsored competition that advanced autonomous vehicle technology through real-world obstacle courses. |
| MIT Media Lab | A research laboratory at MIT known for interdisciplinary innovation in AI, media, and human-computer interaction. |
| Neuralink | A neurotechnology company founded by Elon Musk aiming to develop brain to computer interfaces to connect humans with AI. |
| Deep Thought | A fictional supercomputer in Douglas Adams’ ‘Hitchhiker’s Guide to the Galaxy’ known for calculating the meaning of life. |
| Marvin Minsky | A cognitive scientist and co-founder of MIT’s AI Lab, influential in early symbolic AI and robotics. |
| Norbert Wiener | A mathematician and the founder of cybernetics, a field foundational to control systems and AI theory. |
| Claude Shannon | Known as the father of information theory, whose work laid the groundwork for digital computing and AI. |
| Joseph Weizenbaum | A computer scientist who created ELIZA and later became a critic of AI’s societal impacts. |
| LISP (Language) | One of the oldest programming languages used in AI development, particularly in symbolic reasoning systems. |
| Prolog | A logic programming language commonly used in AI for tasks involving symbolic reasoning and knowledge representation. |
| Bayesian Networks | Probabilistic models that represent a set of variables and their conditional dependencies via a directed graph. |
| Backpropagation | An algorithm used to train neural networks by minimizing error through gradient descent and weight adjustments. |
| 6/13/25 06:42 |
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