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NLP is used for a variety of tasks such as email filters. For example, a lot of people receive a lot
emails which are useless. NLP checks the sender of the email and categorises the mails as spam
or junk. NLP is also used in the autocomplete and spell-check feature of word processors. NLP
also proves to be quite useful with voice text messaging and virtual assistants.
DATA
Data refers to raw facts and figures that are processed and analyse to find meaningful insights.
Data plays a pivotal role in the field of AI. Data collection is the process of gathering and sourcing
information from numerous origins, including sensors, databases, and online sources.
The quality of data is crucial for AI applications. For effective AI performance, data must be
accurate, relevant, complete, and free from errors. The accuracy of AI outcomes is heavily
dependent on the quality of the data provided. Thus,
data can be considered the lifeblood of AI: an AI system
relies on high-quality, well-structured data to learn
and make predictions. Providing incomplete, incorrect,
or low-quality data will lead to flawed, inaccurate, or
unreliable results.
Data types can include numerical values (such as
temperature, loan amount, etc.), categorical data (such
as gender, colour, etc.), or even unstructured text data
(like doctor’s notes, prescriptions, opinion surveys, etc.).
COMPUTER VISION (CV)
Computer Vision is a very popular field of AI that trains a computer to understand and interpret
the visual world. Human vision starts at the “eyes” but machine uses digital images from a camera
for vision. Deep learning models and machines accurately identify and classify objects that act
according to what they see, using digital images from camera.
According to Fei-Fei Li, Computer Vision is defined as “a subset of mainstream artificial intelligence
that deals with the science of making computers or machines visually enabled, i.e., they can
analyse and understand an image.”
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