Page 10 - CT_AI_Class-5

 Machine Learning (ML): This is a subset of AI where machines learn from data.
Instead of being programmed with specific instructions, machines use patterns
in the data to make predictions or decisions. For example, a machine can learn

to recognise photos of cats and dogs by being shown many pictures of them. The
more data it gets, the better it gets at understanding and making decisions.

 Algorithm: An algorithm is a set of step-by-step instructions that a machine
or computer follows to complete a task. It’s like a recipe that tells the machine

exactly what to do at each stage. For example, an algorithm might tell a computer
how to sort a list of numbers from smallest to largest or how to recognise a face
in a photo. Algorithms help machines make decisions or solve problems quickly

and accurately.

 Neural Networks: These are a type of machine learning inspired by how the human
brain works. They are made up of layers of neurons that process information.
Each neuron takes in data, processes it and passes it on to the next layer. As the

network is trained with data, it learns to recognise patterns and make decisions.
Neural networks are used in tasks like recognising images, understanding speech

and even translating languages. The more data they get, the better they become at
their tasks.

If X is to the South of Y and Z is to the East of Y, then in what direction is
X with respect to Z?

a) North-East b) North-West

c) South-West d) South-East

HUMAN VS MACHINE INTELLIGENCE

Intelligence is the ability to learn, understand
and solve problems. It helps to think,
reason and make decisions. AI is a type of

intelligence that computers and machines
use to solve problems and make decisions,
just like humans do. While both humans

and machines can make decisions and
show creativity, they do so in very different
ways. Let's explore these differences in

more detail.

8 Artificial Intelligence (CT & AI)-V