Page 14 - CT_AI_Class-5

TRAINING AND LEARNING

For machines to become smart, they need to learn. This process is called training
and learning. Training and Learning in AI are the processes that help machines get

smarter and more accurate over time. They are the key parts of how AI systems
improve over time. Here's how they work:

 Training: This is the process where an AI

system is taught using large amounts of
data. During training, the system is exposed
to examples that help it understand patterns

and make decisions.

For example: Training an AI to recognise
handwriting involves showing it many
examples of different handwritten letters. Over time, the AI learns how to

recognise letters even if they are written in different styles. The more data it
receives, the better it gets at understanding and recognising patterns.

 Learning: After the training phase, AI
systems can begin to learn from new data

they encounter. This is known as learning.
The more data the system is exposed
to, the better it becomes at completing
tasks and making decisions. In machine

learning, the system continues to improve
by adjusting itself based on what it learns
from the data.

For example: Once trained to recognise cats in photos, the AI continues to
improve as it encounters new images. If it sees a new type of cat, it learns to
recognise that too, based on the features it has learned from previous data.

In simple terms, training is like teaching an AI using lots of examples and learning
is how it gets better over time by applying what it has been taught to new
situations. Through these processes, AI systems become more accurate, reliable

and capable of performing complex tasks.

Ask
AGENT
OrangeAI
Which types of data are commonly used to train AI systems?

Study

12 Artificial Intelligence (CT & AI)-V