Perception in AI refers to the ability of an intelligent system to interpret and understand sensory inputs from the environment. These inputs can come from a variety of sources, such as images, videos, sounds, and other types of sensor data. Perception is a crucial aspect of AI because it enables machines to make sense of the world around them and interact with it in meaningful ways. Here are some examples of perception in AI: Computer Vision: This is the ability of machines to interpret and understand visual inputs such as images and videos. Computer vision algorithms can be used for various applications, such as object detection, facial recognition, and image segmentation. Speech Recognition: This is the ability of machines to interpret and understand spoken language. Speech recognition algorithms can be used for various applications, such as virtual assistants, automated transcription, and language translation. Natural Language Processing (NLP): This is the ability of mach...
Simple IR Process: User Query -> Keyword Analysis -> Document Retrieval -> Document Ranking -> Search Result Here are a few examples of information retrieval applications: Search engines: Google, Bing, and Yahoo are all examples of search engines that use IR to retrieve relevant results from their indexes. When a user enters a query, the search engine retrieves the most relevant results from its index. Recommender systems: Netflix, Amazon, and Spotify all use IR to recommend content to their users based on their preferences. The system retrieves relevant content from its index based on the user's previous interactions with the system. Chatbots: Chatbots use IR to understand user input and retrieve relevant responses. The chatbot retrieves the most relevant response from its index of possible responses based on the user's input.