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AI Glossary

An AI model designed to autonomously interact with its environment to perform tasks, often adapting to new information.

A method of task automation where agents work in a structured sequence to complete complex tasks independently.

An AI assistant designed to collaborate with humans, often in real-time, to aid in tasks or decision-making.

The process of ensuring an AI system's goals and actions align with human values and intentions.

An AI system capable of understanding and learning any intellectual task that a human can, with the ability to transfer knowledge across multiple domains without specialized training.

A hypothetical AI that surpasses human intelligence across all fields, including creativity, problem-solving, and emotional intelligence.

The process of measuring an AI model's performance against set standards or other models.

Systematic errors in AI that can lead to unfair or inaccurate outcomes, often rooted in biased data.

A reasoning technique where AI models break down complex problems into intermediate steps for improved answers.

An AI-powered conversational agent that can communicate with users in text or voice formats to answer questions or provide assistance.

A conversational AI model developed by OpenAI, based on the GPT architecture, for natural language interactions.

The process of categorizing data points into predefined classes, such as spam vs. non-spam emails.

An advanced AI chatbot created by Anthropic with an emphasis on ethical and safe interactions.

Responses generated by AI models based on the input prompt, typically used in text-based interactions.

The computational resources (e.g., processors, GPUs) required to train and run AI models.

Improving raw data by adding additional context, such as tags, metadata, or categorizations, to enhance usability.

AI designed specifically for understanding and generating human language in a conversational context.

The process of artificially creating new training data from existing data to enhance model performance.

The process of pulling specific data or insights from unstructured sources, like text or images.

The initial step in the data pipeline where data is collected from various sources and processed for use.

Collections of data used to train, validate, or test AI models.

A subset of machine learning using neural networks with multiple layers to learn complex patterns in data.

When an AI model produces the same output each time it receives the same input.

A process used in generative models to create or modify data, often seen in image generation techniques.

A representation of data, often words or sentences, in a continuous vector space to capture its meaning or relationships.

Tests or assessments to measure the effectiveness or accuracy of AI models.

AI systems designed with transparency to allow humans to understand how they reach their conclusions.

A technique where AI models learn tasks with minimal training examples.

The process of adapting a pre-trained model to a specific task with additional data.

A large-scale AI model pre-trained on vast data that can be adapted to various downstream tasks.

A family of AI models by Google focused on both conversational and multimodal tasks.

AI that can produce new content, such as text, images, or music, rather than simply analyzing existing data.

A transformer-based model that generates text by predicting the next word in a sequence.

Hardware optimized for parallel processing, commonly used to accelerate AI computations.

When an AI model generates information that is not based on real data or facts.

A setup where human input guides or corrects AI decisions to improve performance or accuracy.

The process of making predictions or generating responses based on a trained AI model.

A structured representation of interconnected facts that helps AI understand relationships between entities.

A powerful type of AI trained on massive text data to understand and generate human language.

The time delay between a user's input and the AI's response.

Meta's open-source large language model designed for various text generation and understanding tasks.

A field of AI where algorithms learn from data to make predictions or decisions without explicit programming.

Data that provides information about other data, often used to organize and retrieve data efficiently.

An open-source AI model focused on efficient, smaller-scale performance for various NLP tasks.

The settings and hyperparameters that define an AI model's structure and behavior.

AI models that can process and combine multiple types of input, such as text, images, and audio.

A technique where prompts are adjusted to allow a model to perform multiple tasks.

The field of AI focused on enabling computers to understand and process human language.

A series of interconnected nodes that mimic the human brain, used to detect patterns and make decisions in AI.

Building blocks within Workflows in Lleverage.

The values in a model that are adjusted during training to fit the data, such as weights in a neural network.

The process of analyzing text to extract structured information, like document parsing (CV).

The initial phase of training a model on large datasets to develop foundational knowledge before fine-tuning.

The input given to an AI model to generate a response, often structured to guide the model's output.

The practice of linking multiple prompts to guide the AI through a sequence of responses.

Crafting and optimizing prompts to achieve the best responses from AI models.

An interface to design, test, and refine prompts for better model interactions.

Adjusting prompts to refine or correct model responses without major modifications.

A model technique that retrieves data from external sources to improve response accuracy.

A type of machine learning where models learn by receiving rewards or penalties for their actions.

Training models by optimizing based on human feedback on responses.

A search that uses the meaning of words rather than exact matches to retrieve relevant information.

The process of identifying the emotional tone in text, often used in social media monitoring.

Finding data points similar to a query by comparing their vector embeddings.

A theoretical point where AI surpasses human intelligence, leading to rapid and possibly unpredictable advances.

Data that is organized in a clear, defined format, such as tables or databases.

AI-generated data presented in an organized format like lists, tables, or fields.

A parameter controlling the randomness of a model's output, where higher values lead to more varied responses.

An open-source framework by Google for building and deploying machine learning models.

A unit of text, such as a word or character, that a model processes to generate responses.

The maximum number of tokens a model can handle in a single input or output sequence.

A decoding method where only the top cumulative probability tokens are considered in response generation.

Data used to train an AI model, helping it learn patterns and make predictions.

A type of model architecture that excels in handling sequential data, particularly for NLP tasks.

Data not organized in a pre-defined way, like raw text, audio, or images.

A storage element in programming or machine learning that can hold data values for processing.

A specialized database optimized for storing and retrieving vector embeddings (e.g. Weaviate, Pinecone).

The process of converting text or other data into numerical vectors to enable similarity comparisons.

When a model performs a task it wasn't explicitly trained for by leveraging general knowledge.