SeekBox AI

AI glossary

Public SeekBox information. Choose a topic for its full explanation and update date.

  • Agentic AI

    AI systems that can autonomously plan, use tools, and execute multi-step tasks with minimal human supervision, going beyond simple question-answering.

  • AI Alignment

    The research field focused on ensuring AI systems act in accordance with human intentions, values, and ethical principles, especially as systems become more capable.

  • API (Application Programming Interface)

    A set of protocols and tools that allow software applications to communicate with AI services programmatically, enabling integration of AI capabilities into products.

  • Artificial Intelligence (AI)

    Systems that perform tasks requiring human-like cognition, using techniques like machine learning, natural language processing, and computer vision.

  • Attention Mechanism

    A mechanism in neural networks that computes weighted relevance scores between elements of a sequence, allowing the model to focus on the most pertinent information for each output.

  • Benchmark

    Standardized evaluation datasets and metrics used to compare AI model performance across tasks like reasoning, coding, math, and language understanding.

  • Bias (in AI)

    Systematic errors in AI outputs that reflect prejudices present in training data, model design, or evaluation criteria, potentially leading to unfair outcomes.

  • Chain-of-Thought (CoT)

    A prompting strategy where the model is encouraged to produce intermediate reasoning steps before arriving at a final answer, improving performance on complex tasks.

  • Chatbot

    A conversational AI interface that processes user messages and generates contextually appropriate responses, often powered by LLMs with dialogue-specific fine-tuning.

  • Computer Vision

    A subfield of AI focused on enabling machines to interpret visual data from the world, including object detection, image classification, and scene understanding.

  • Context Window

    The maximum number of tokens an LLM can process in a single inference call. Modern models support windows from 8K to over 1M tokens.

  • Data Labeling

    The process of annotating data with ground-truth labels used for supervised learning, often requiring human annotators to classify, tag, or rate examples.

  • Deep Learning

    A subset of machine learning using multi-layered neural networks to learn hierarchical representations of data, enabling breakthroughs in vision, NLP, and generative tasks.

  • Diffusion Model

    A generative model that creates images (or other data) by learning to reverse a gradual noising process, producing high-quality outputs from random noise.

  • Edge AI

    Deploying AI models on local devices (phones, IoT, embedded systems) rather than cloud servers, enabling low-latency inference and data privacy.

  • Embedding

    A dense vector representation of text in a high-dimensional space where semantic similarity corresponds to geometric proximity. Used for search, clustering, and recommendation.

  • Emergent Behavior

    Capabilities that appear in large models but are absent in smaller ones, arising from scale rather than explicit training — such as in-context learning or chain-of-thought reasoning.

  • Few-Shot Learning

    A technique where a model learns to perform a task from a small number of examples provided in the prompt, without updating its parameters.

  • Fine-Tuning

    The process of further training a pre-trained model on a smaller, task-specific dataset to adapt it for particular use cases.

  • Foundation Model

    A large-scale model trained on broad data that can be adapted to a wide range of downstream tasks through fine-tuning, prompting, or other techniques.

  • GAN (Generative Adversarial Network)

    A generative model architecture consisting of a generator and discriminator that are trained adversarially — the generator creates samples while the discriminator distinguishes real from generated.

  • GPT (Generative Pre-trained Transformer)

    OpenAI's family of autoregressive language models, trained on large text corpora then fine-tuned with RLHF. GPT-4o is their flagship multimodal model.

  • GPU (Graphics Processing Unit)

    A parallel processing chip optimized for matrix operations, making it ideal for training and running neural networks. NVIDIA's H100 is the dominant AI GPU.

  • Grounding

    The practice of anchoring AI outputs in verifiable external data sources (documents, databases, search results) to reduce hallucination and improve factual accuracy.

  • Guardrails

    Safety mechanisms implemented in AI systems to prevent harmful, biased, or policy-violating outputs, including content filters, system prompts, and output classifiers.

  • Hallucination

    A phenomenon where LLMs generate plausible-sounding but factually incorrect or fabricated information, often with high confidence.

  • Inference

    The phase where a trained model processes new inputs to generate predictions or outputs, as opposed to the training phase where it learns from data.

  • Knowledge Distillation

    A training technique where a smaller "student" model learns to mimic the outputs of a larger "teacher" model, achieving competitive performance with fewer parameters.

  • Large Language Model (LLM)

    A neural network with billions of parameters trained on massive text corpora to understand and generate human language. Examples include GPT-4, Claude, and Gemini.

  • Latent Space

    A compressed, abstract representation space learned by a model where input data is encoded as vectors, capturing meaningful structure and relationships.

  • Machine Learning (ML)

    A subset of AI where algorithms improve their performance on tasks by learning from data rather than being explicitly programmed.

  • MCP (Model Context Protocol)

    An open protocol that standardizes how AI applications connect to external data sources and tools, enabling models to access real-time information and take actions beyond text generation.

  • Multimodal AI

    AI models capable of processing and generating multiple types of data — text, images, audio, and video — within a single system.

  • Natural Language Processing (NLP)

    A field of AI focused on the interaction between computers and human language, encompassing tasks like translation, summarization, sentiment analysis, and question answering.

  • Neural Network

    A computational model composed of layers of interconnected nodes (neurons) that process data through weighted connections, learning to approximate complex functions.

  • Open Source vs. Closed Source

    Open-source models release their weights and often their training code publicly (e.g., Llama, Mistral), while closed-source models (e.g., GPT-4, Claude) are only accessible via API.

  • Overfitting

    A condition where a model learns patterns specific to its training data that don't generalize to unseen data, resulting in high training accuracy but poor real-world performance.

  • Parameter

    The learnable weights and biases in a neural network that are adjusted during training. Model size is often expressed in parameter count (e.g., 7B, 70B, 405B).

  • Prompt

    The input text given to a language model that frames the task, provides context, and guides the model's output. Prompt engineering is the practice of crafting effective prompts.

  • Quantization

    A model compression technique that reduces the numerical precision of weights and activations (e.g., from 32-bit to 4-bit), decreasing memory usage and speeding up inference.

  • RAG (Retrieval-Augmented Generation)

    An architecture that combines a retrieval system with an LLM — the model first retrieves relevant documents, then generates answers grounded in that retrieved context.

  • Reinforcement Learning from Human Feedback (RLHF)

    A training technique where a reward model trained on human preference data is used to fine-tune an LLM via reinforcement learning, aligning it with human values.

  • Scaling Laws

    Empirical relationships showing that model performance improves predictably as a power law of compute, parameters, and data, guiding resource allocation for AI training.

  • Sentiment Analysis

    An NLP task where models classify text by emotional tone or opinion polarity, commonly used for analyzing customer reviews, social media, and brand perception.

  • Synthetic Data

    Artificially generated data used for training models when real-world data is scarce, expensive, or raises privacy concerns. Can be created by other AI models.

  • Temperature

    A parameter (typically 0–2) that controls the randomness of an LLM's output by scaling the logits before sampling. Lower values make output more deterministic.

  • Token

    The basic unit of text that LLMs process. Text is split into tokens (subwords) by a tokenizer, and the model predicts the next token in a sequence.

  • Training Data

    The dataset used to train a machine learning model. For LLMs, this typically includes web pages, books, code, and other text corpora totaling billions of tokens.

  • Transfer Learning

    A technique where knowledge learned from one task or domain is applied to a different but related task, reducing the data and compute needed for the new task.

  • Transformer

    A neural network architecture that uses self-attention mechanisms to process sequential data in parallel, forming the foundation of most modern LLMs.

  • Zero-Shot Learning

    A model's ability to perform tasks it wasn't explicitly trained on, using only a natural language description of the task without any demonstration examples.

Sources