Anomaly Detection
Imagine that you are making a piece of software that will track the purchases made with credit cards and look for any strange patterns of use that could be signs of fraud. Such a type of anomaly is known as a “point anomaly.” When one observation stands out from the rest of the data points being looked at, this is called a “contextual anomaly.” For example, say you have hundreds of pictures of famous buildings, and your machine learning model filters out a picture of mountains that was added to the set by mistake. This occurred because the mountains were in the wrong dataset. When it comes to these kinds of anomalies, something that is considered an anomaly in one context might not be considered an anomaly in another context.
Computer Vision
The branch of artificial intelligence known as “computer vision” focuses on the processing of images. Let’s investigate a few of the many opportunities that come with using computer vision.
The app “Seeing AI” is a fantastic illustration of the power that computer vision can provide. The Seeing AI app was developed specifically for people who are blind or have low vision. It makes use of the power of artificial intelligence to open up the visual world and describe nearby people, text, and objects.
Most computer vision solutions come from machine learning models, which can be used to analyze visual data from cameras, videos, or still images.
Natural Language Processing
“Natural language processing,” or NLP, is a branch of artificial intelligence that works on making software that can understand both written and spoken languages.
Behind the scenes, natural language processing looks at how sentences are put together and what each word means. To put it another way, it deciphers human speech so that it can carry out various tasks without any human intervention. Virtual assistants like Google Assistant, Siri, and Amazon’s Alexa are among the most well-known applications of natural language processing. Words and phrases like “Hey Alexa, increase the volume, please.” can be translated into numbers that machines can understand with the help of natural language processing.
NLP makes it possible to
- Identify emotions in text and classify opinions as positive, negative, or neutral. Companies can learn about customer sentiment toward their brands and products by sifting through customer comments posted on social media, product reviews, and online surveys. You could, for instance, monitor tweets about your company in real time to look for signs of dissatisfied customers.
- Extract specific data from text, using text extraction. This tool is useful for identifying and extracting relevant keywords, features (such as item codes, colors, and characteristics), and named entities from large datasets (like names of people, locations, company names, emails, etc.).
- Design chatbots to interact with humans through text or speech.
For instance, Human Interact’s virtual reality (VR) game Starship Commander is set in a science fiction universe and allows players to pilot their own spaceship. Because the game uses natural language processing, the players can control the story and interact with the game’s characters and starship systems.