Page 147 - AI Computer 10

Applications of Computer Vision

Computer Vision has many applications in real life. Some of these are:
u Self-driving Cars: Self-driving cars are the most common example of computer vision as these cars have
enough potential to scan live objects i.e. nearby cars, traffic lights etc.
u Oil and Natural Gas Detection: The introduction of computer vision has enabled geologists to scan large
number of images in a matter of few hours, instead of days that used to be the case with manual scanning
of images.
u Hiring process: Computer Vision also helps personnel managers to hire most suitable candidates. Combining
computer vision, machine learning, and data science, AI systems are able to quantify soft skills and conduct
candidate assessments to help managers shortlist the candidates.
u Video surveillance: The concept of video tagging is used to tag videos with keywords based on the objects
that appear in each scene. Security companies feed video content in AI systems and the system is able to
recognise people or vehicles with the desired characteristics.

u Healthcare: The healthcare industry has adopted computer vision for internal organ imaging, surgical
simulations, and diagnosing diseases.
u Agriculture: The agricultural sector uses computer vision technologies in various forms such as smart
tractors, smart farming equipment, and drones to help monitor and maintain the fields efficiently and
easily. Computer vision also helps in pest control in crops.

u Face Recognition Systems: Face recognition systems use computer vision to scan through thousands of
images and recognise a person by matching facial characteristics. This helps in designing security systems,
facial biometric systems, and automatic entry systems.

Natural Language Processing (NLP)
Natural Language Processing is the most popular branch of AI. It deals with the interaction between computers
and humans using natural language. The main objective of NLP is to enable machines to read, interpret,
understand, and interact in natural language. For example, Google Translate uses NLP to enhance translation.
The two main components of Natural Language Processing are:
u Natural Language Understanding: Natural language understanding is the component of NLP that uses
computer software to understand human language or natural language. This component is commonly
integrated in speech recognition systems such as Alexa, Siri, and Google Assistant.
u Natural Language Generation: Natural language generation refers to the software process that produces
natural language output. A system like Gmail’s Smart Compose is an example of natural language generation
which recommends suggestions on what you should type next in an email.

Applications of NLP
NLP finds its application in several areas. Some of these are:

u Chatbots: Chatbots are a form of artificial intelligence agents that are programmed to interact with humans
in such a way that they sound like humans themselves. Chatbots are created using NLP and machine learning
and are able to understand the complexities of the English language and find the actual meaning of the
sentence.
u Autocomplete in Search Engines: Have you noticed that search engines tend to guess what you are typing
and automatically complete your sentences? All these suggestions are provided using autocomplete that
uses NLP. Search engines use their enormous data sets to analyse what a user is typing when they enter
particular words and suggest the most common possibilities.

13
13