AI Glossary
A comprehensive guide to artificial intelligence terminology, concepts, and acronyms used throughout the AI industry.
Applications (8 terms)
AI Agents
Autonomous AI systems that can perceive their environment, make decisions, and take actions to achieve goals. Agents can use tools, browse the web, and complete multi-step tasks.
Autonomous Systems
Systems capable of operating independently without human intervention. Includes self-driving vehicles, autonomous drones, and robotic systems.
Drug Discovery AI
Application of AI to accelerate pharmaceutical research, including protein folding prediction (AlphaFold), molecular screening, and clinical trial optimization.
Fintech AI
AI applications in financial services including fraud detection, algorithmic trading, credit scoring, robo-advisory, and customer service automation.
Healthcare AI
AI applications in healthcare including medical imaging analysis, clinical documentation, drug discovery, patient monitoring, and administrative automation.
Robotics AI
AI systems that control robots for tasks like manufacturing, warehouse automation, surgery, and domestic assistance. Combines computer vision, planning, and motor control.
Speech Recognition / ASR
AI that converts spoken language into text. Used in voice assistants, transcription services, and accessibility tools. ASR stands for Automatic Speech Recognition.
Text-to-Speech (TTS)
AI that converts written text into natural-sounding speech. Modern TTS can clone voices and express emotions.
Business (2 terms)
Series A/B/C Funding
Stages of venture capital financing. Series A is typically the first significant round after seed funding, with B and C representing subsequent larger rounds.
Unicorn
A privately held startup valued at over $1 billion. Many AI companies have achieved unicorn status due to massive investment in the sector.
Fundamentals (7 terms)
AGI (Artificial General Intelligence)
Hypothetical AI that can perform any intellectual task that a human can. Unlike narrow AI, AGI would have general reasoning and learning capabilities.
Artificial Intelligence (AI)
The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction. AI encompasses various techniques to enable machines to perform tasks that typically require human intelligence.
Benchmark
Standardized tests used to evaluate and compare AI model performance. Common benchmarks include MMLU, HumanEval, and ImageNet.
Machine Learning (ML)
A subset of AI where systems learn and improve from experience without being explicitly programmed. ML algorithms build models based on training data to make predictions or decisions.
Open Source AI
AI models and tools released with open licenses allowing free use, modification, and distribution. Examples include LLaMA, Stable Diffusion, and PyTorch.
Proprietary AI
AI models and systems that are privately owned and controlled, with restricted access. Examples include GPT-4 and Claude.
Sovereign AI
AI systems developed and controlled within a country's borders to ensure data sovereignty and reduce dependence on foreign technology. A growing trend in Europe and Asia.
Infrastructure (8 terms)
API (Application Programming Interface)
A way for software applications to communicate. AI APIs allow developers to integrate AI capabilities into their applications without building models from scratch.
Data Labeling
The process of annotating data with labels or tags to create training datasets for supervised learning. Critical for training accurate AI models.
Edge AI
Running AI models directly on edge devices (phones, IoT devices, vehicles) rather than in the cloud. Enables lower latency and privacy preservation.
GPU (Graphics Processing Unit)
Specialized processors originally designed for graphics but now essential for AI training and inference due to their parallel processing capabilities. NVIDIA dominates this market.
Inference
The process of using a trained AI model to make predictions on new data. Inference optimization is crucial for deploying models at scale with low latency.
ML Infrastructure
The hardware, software, and platforms that support machine learning development, training, and deployment. Includes GPU clusters, cloud services, and orchestration tools.
MLOps
Machine Learning Operations - practices for deploying and maintaining ML models in production reliably and efficiently. Combines ML, DevOps, and data engineering.
Vector Database
Databases optimized for storing and querying high-dimensional vectors (embeddings). Essential for similarity search, RAG systems, and recommendation engines.
Models (8 terms)
Context Window
The maximum amount of text an LLM can process in a single request, measured in tokens. Larger context windows enable processing longer documents.
Diffusion Model
A type of generative AI model that learns to create data by reversing a gradual noising process. Powers image generators like Stable Diffusion and DALL-E 3.
Foundation Model
A large AI model trained on broad data that can be adapted to many downstream tasks. Examples include GPT-4, Claude, and LLaMA which serve as foundations for various applications.
Generative AI
AI systems that can create new content including text, images, audio, video, and code. This includes tools like ChatGPT, DALL-E, Midjourney, and Stable Diffusion.
Large Language Model (LLM)
A type of AI model trained on vast amounts of text data to understand and generate human language. Examples include GPT-4, Claude, LLaMA, and Gemini. LLMs power chatbots, content generation, and code assistance.
Model Weights
The learned parameters of a neural network that determine its behavior. Weights are adjusted during training and stored for inference.
Multimodal AI
AI systems that can process and generate multiple types of data (text, images, audio, video) simultaneously. Examples include GPT-4V and Gemini.
Transformer
A neural network architecture that uses self-attention mechanisms to process sequential data. Transformers are the foundation of modern LLMs like GPT and BERT.
Safety & Ethics (5 terms)
AI Alignment
The challenge of ensuring AI systems behave according to human intentions and values. A critical research area as AI systems become more capable.
AI Safety
Research and practices focused on ensuring AI systems are beneficial, aligned with human values, and don't cause unintended harm. Key focus areas include alignment, interpretability, and robustness.
Constitutional AI
An approach to AI training where models are guided by a set of principles or 'constitution' to produce helpful, harmless, and honest outputs. Developed by Anthropic.
Explainability / Interpretability
The ability to understand and explain how an AI model makes its decisions. Important for trust, debugging, and regulatory compliance.
Hallucination
When AI models generate plausible-sounding but factually incorrect or nonsensical information. A significant challenge in deploying LLMs for factual applications.
Techniques (14 terms)
Computer Vision
AI that enables computers to interpret and understand visual information from images and videos. Used in facial recognition, autonomous vehicles, medical imaging, and quality control.
Deep Learning
A subset of machine learning using artificial neural networks with multiple layers. Deep learning excels at processing unstructured data like images, text, and audio.
Embedding
A numerical representation of data (text, images, etc.) in a high-dimensional vector space. Embeddings capture semantic meaning and enable similarity search.
Federated Learning
A machine learning approach where models are trained across decentralized devices without sharing raw data, preserving privacy while enabling collaborative learning.
Fine-tuning
The process of taking a pre-trained model and training it further on a specific dataset to specialize it for a particular task or domain.
Natural Language Processing (NLP)
A branch of AI focused on enabling computers to understand, interpret, and generate human language. Applications include sentiment analysis, translation, and text summarization.
Neural Network
A computing system inspired by biological neural networks. It consists of interconnected nodes (neurons) that process information and learn patterns from data.
Pre-training
The initial phase of training a model on a large, general dataset before fine-tuning on specific tasks. Foundation models undergo extensive pre-training.
Prompt Engineering
The practice of crafting effective prompts to guide AI models to produce desired outputs. Essential for getting the best results from LLMs and generative AI.
RAG (Retrieval-Augmented Generation)
A technique that enhances LLM responses by first retrieving relevant information from a knowledge base, then using it to generate more accurate and up-to-date answers.
Reinforcement Learning
A type of machine learning where agents learn to make decisions by taking actions and receiving rewards or penalties. Used in robotics, game playing, and autonomous systems.
RLHF (Reinforcement Learning from Human Feedback)
A training technique that uses human preferences to fine-tune AI models, helping align model outputs with human values and expectations.
Synthetic Data
Artificially generated data that mimics real data characteristics. Used for training when real data is scarce, sensitive, or expensive to obtain.
Tokenization
The process of breaking text into smaller units (tokens) for processing by language models. Tokens can be words, subwords, or characters.
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