Natural Language Understanding (NLU) focuses on the ability to interpret and comprehend human language, including understanding context, semantics, and identifying key entities within text.
Understanding the context and nuances of text input to provide relevant responses.
Grasping the meaning and intent behind words and phrases.
Identifying and categorizing key entities within the text, such as names, dates, or locations.
Natural Language Generation (NLG) describes the ability to generate human-like text from structured data or other inputs.
Continuing a given text prompt in a coherent and contextually appropriate manner to generate fluent and contextually relevant content.
Condensing longer texts into concise summaries while preserving essential information and maintaining coherence.
Rewriting text to express the same ideas using different words and structures while maintaining the original meaning.
Producing conversational responses that are contextually relevant and engaging within a dialogue context.
Automatically generating relevant and meaningful questions from a given text or context.
Rewriting text to match the style of a given reference text while preserving the original content.
Generating a piece of text given a description or a first sentence to complete.
Capabilities for retrieving relevant information from various sources and synthesizing it into coherent, contextually appropriate responses. This includes searching, extracting, combining, and presenting information in a meaningful way.
Capability to identify and extract factual information from text documents or knowledge bases, including entities, relationships, and key data points.
System capability to understand questions and provide accurate, relevant answers by analyzing available information sources.
Capability to aggregate and combine information from multiple sources, creating comprehensive and coherent responses while maintaining context and relevance.
Capability to analyze and determine the semantic similarity between sentences, supporting tasks like search, matching, and content comparison.
Capability to identify and retrieve relevant documents or text passages based on specific criteria or queries from a larger collection of texts.
Capability to perform efficient and accurate searches within large textual databases based on various criteria, including keywords, semantic meaning, or complex queries.
Capabilities for generating various forms of creative content, including narratives, poetry, and other creative writing forms.
Creating narratives, stories, or fictional content with creativity and coherence.
Composing poems, prose, or other forms of creative literature.
Capabilities for handling multiple languages, including translation and multilingual text processing.
Converting text from one language to another while maintaining meaning and context.
Recognizing and processing text in multiple languages.
Capabilities for adapting and personalizing content based on user context and preferences.
Tailoring responses based on user preferences, history, or context.
Modifying the tone or style of generated text to suit specific audiences or purposes.
Capabilities for performing logical analysis, inference, and problem-solving tasks.
Making logical inferences based on provided information.
Assisting with solving problems by generating potential solutions or strategies.
Verifying facts and claims given a reference text.
Capabilities for ensuring ethical, unbiased, and safe content generation and interaction.
Reducing or eliminating biased language and ensuring fair and unbiased output.
Avoiding the generation of harmful, inappropriate, or sensitive content.
Capabilities for classifying and categorizing text into predefined categories or labels.
Classifying a text as belong to one of several topics, which can be used to tag a text.
Classify the sentiment of a text, that is, a positive movie review.
Classifying the relation between two texts, like a contradiction, entailment, and others.
Capabilities for extracting and representing textual features as vectors for downstream tasks.
Representing parts of text with vectors to be used as input to other tasks.
Capabilities for classifying individual tokens or words within text.
Task to recognize names as entity, for example, people, locations, buildings, and so on.
Tagging each part of a sentence as nouns, adjectives, verbs, and so on.
Assigning labels or categories to images based on their visual content.
Assigning labels or categories to entire videos or segments based on their visual and audio content.
Assigning labels or categories to images based on their visual content.
Identifying and locating specific objects within an image or video, often by drawing bounding boxes around them.
Identifying and locating specific points of interest within an image or object.
Creating new images from learned patterns or data using machine learning models.
Predicting the distance or depth of objects within a scene from a single image or multiple images.
Identifying and isolating key characteristics or patterns from an image to aid in tasks like classification or recognition.
Producing segmented regions in an image to highlight specific areas or objects, typically represented as separate layers or overlays.
Transforming one image into another using a learned mapping, often for tasks like style transfer, colorization, or image enhancement.
The process of converting a 2D image into a 3D representation or model, often by inferring depth and spatial relationships.
Assigning labels or classes to audio content based on its characteristics.
Transforming audio through various manipulations including cutting, filtering, and mixing.
Classifying data based on attributes using classical machine learning approaches.
Predicting numerical values based on tabular attributes and features.
Capabilities for solving mathematical problems and proving theorems.
Executing pure mathematical operations, such as arithmetic calculations.
Solving mathematical exercises presented in natural language format.
Solving geometric problems and spatial reasoning tasks.
Proving mathematical theorems using computational methods.
Capabilities for code generation, documentation, and optimization.
Translating natural language instructions into executable code.
Generating natural language documentation for code segments.
Automatically filling in code templates with appropriate content.
Rewriting and optimizing existing code through refactoring techniques.
Retrieval of information is the process of fetching relevant data or documents from a large dataset or database based on a specific query or input.
Depth estimations the task of predicting the distance or depth of objects within a scene from a single image or multiple images.
Search is the process of exploring a dataset or index to find relevant information or results based on a given query.
Document retrieval is the process of retrieving relevant documents from a collection based on a specific query, typically through indexing and search techniques.
Document or database question answering is the process of retrieving and using information from a document or database to answer a specific question.
Generation of any is augmenting the creation of text, images, audio, or other media by incorporating retrieved information to improve or guide the generation process.
Capabilities for processing and generating images from various inputs and generating textual descriptions of visual content.
Generating textual descriptions or captions for images.
Generating images based on textual descriptions or instructions.
Generating video content based on textual descriptions or instructions.
Generating 3D objects or scenes based on textual descriptions.
Answering questions about images using natural language.
Capabilities for processing audio, including speech synthesis and recognition.
Converting text into natural-sounding speech audio.
Converting spoken language into written text.
Converting between any supported modalities (text, image, audio, video, or 3D).
Identifying indicators of malicious activity, suspicious patterns, or emerging threats across logs and data sources.
Reviewing code, configurations, or dependency manifests to surface potential security weaknesses and misconfigurations.
Scanning artifacts (code, logs, documents) to identify exposed credentials, tokens, or other sensitive secrets.
Evaluating data handling or user flows to surface potential privacy risks and recommend mitigations.
Detecting and correcting errors, inconsistencies, and missing values to improve dataset quality.
Deriving structural metadata (fields, types, relationships) from raw or semi-structured data.
Constructing informative transformed variables to improve downstream model performance.
Designing or explaining multi-step sequences that extract, transform, and load datasets.
Evaluating datasets for completeness, validity, consistency, and timeliness.
Breaking complex objectives into structured, atomic subtasks.
Allocating responsibilities to agents based on capabilities and task requirements.
Coordinating plans across multiple agents, resolving dependencies and optimizing sequencing.
Managing real-time collaboration and state synchronization among agents.
Facilitating negotiation, conflict handling, and consensus-building between agents.
Running standardized benchmarks or evaluation suites and summarizing results.
Creating targeted test inputs or scenarios to probe system behavior and edge cases.
Assessing outputs for accuracy, relevance, coherence, safety, and style adherence.
Identifying unusual patterns, drifts, or deviations in data or model outputs.
Tracking latency, throughput, resource utilization, and service reliability over time.
Defining or explaining steps to allocate and configure compute, storage, and networking resources.
Coordinating multi-stage application or model deployments, rollbacks, and version transitions.
Designing or modifying continuous integration and delivery workflows and pipelines.
Tracking, promoting, and documenting different iterations of models and their artifacts.
Configuring and interpreting telemetry signals, thresholds, and alerts for operational health.
Translating organizational or regulatory policies into structured, enforceable rules or checklists.
Evaluating processes or outputs against defined standards (e.g., GDPR, HIPAA) and identifying gaps.
Condensing system event or transaction logs into human-readable compliance or oversight summaries.
Categorizing potential operational or data-related risks by impact and likelihood for prioritization.
Interpreting and explaining API specifications, endpoints, parameters, and expected payloads.
Designing or describing automated sequences integrating multiple tools or services.
Selecting and ordering tool invocations to accomplish a specified goal efficiently.
Linking custom scripts or functions with external tools to extend capabilities.
Formulating high-level multi-phase strategies aligned with long-term objectives.
Maintaining coherent reasoning chains over extended sequences of steps or time.
Organizing intermediate reasoning steps into clear, justifiable sequences.
Proposing plausible explanations or solution pathways for incomplete or uncertain scenarios.