Task

Identifying and classifying entities in natural language into predefined categories such as names, locations, organizations, and numerical expressions. NER capable models are able to analyze the context and semantic nuances in the corpus to determine the appropriate category for each entity.

Requires a model to categorize data points into one of two possible categories and the goal is to accurately assign each data point to the correct category.

Refers to the process of determining the degree of similarity between two or more items. In the context of AI, this technique is commonly used to analyze and compare textual data, such as documents, articles, or social media posts. By identifying the similarities between different pieces of content, AI systems can extract valuable insights and patterns.

A type of task where an algorithm is trained to generate an output image from a given input image. This can involve tasks such as image colorization, style transfer, photo processing, or even image-to-image translation where the goal is to transform an image from one domain to another.

In this task diverse machine learning algorithms may be used to analyze a text and then creating a visual representation of the content. Through diffussion (mainly) diverse range and types of visual representations can be created and refined

This task is normally mono modal and typically involves using machine learning algorithms to analyze audio data and identify spoken words, then converting those words into text using natural language processing techniques.

In this multi-modal task natural language processing and machine learning are utilized to produce human-like speech from text.

A multi-modal Task that utilizes computer vision algorithms in combination with language generation models to recognize objects, characters, scenes, or activities within images and then generating relevant textual descriptions or identifications.

An AI system that operates exclusively through a single mode of input or output, such as voice or text, can interact with a range of services and apps to enrich the output's recency or relevancy as part of its processing.

An artificial intelligence model can understand and respond to one or various forms of input, such as text, voice, and visual cues. This type of assistant can integrate information from multiple sources and modalities to provide a more comprehensive and personalized experience for the user.