AI THINKING
The ability of a digital computer or computer-controlled robot to perform tasks commonly
associated with intelligent beings. Data is any form of recorded information. Examples include
a fingerprint, a photo of a face, and an age. Data is the lifeblood of AI. AI can help solve
problems you are passionate about. Before start, define the project goals. Successful AI
applications begin with narrow, well-thought-out problem statements. All projects have
constrainst. A constraint is a retriction that limits an idea. Limited or poor-quality data can
lead to biased AI. A feature is measurable piece of data that can be used by an AI to make a
decision. AI can absorb biases from the real world, and companies and governments must
take responsibility for those biases. A user persona is a fictional person who would want to
use an AI application, usually used as a tool to imagine the needs of real users. Machine
learning is a type of AI that recognizes patterns in data and draws conclusions based on those
patterns. Machine learning components is data, algorithm, model, and prediction. There are
many different types of data, and appropriate data collection methods lead to better AI
applications. Biometric data is data that comes from measuring a person’s physical or
behavioral characteristics. Numerical data is data that is made up of numbers. Visual data is
data that is made up of things like images or videos. Audio data is data that consists of sounds.
Textual data is written or printed words, sentences, and other strings of characters. The first
step in building a new app is creating a Data Collection Plan (DCP). Many companies collect
huge amounts of data about people with minimal consent in order to power their Ais. When
AI application require personal user data, the responsibility of data privacy is shared between
users, companies, and government.
