Artificial Intelligence and Machine Learning
1. Introduction
Artificial Intelligence (AI) and Machine Learning (ML) are now part of everyday life, even if
most of us do not notice them. Some kind of AI is working in the background whenever a bank
flags a strange card payment, YouTube recommends a video, or your phone suggests the next word
in a message.
In layman's terms, Artificial Intelligence is the capacity of a machine or computer to carry out
activities that typically call for human intelligence. These tasks include understanding language,
recognizing faces, making decisions, solving problems and learning from experience. AI includes
machine learning. An ML system is given a large amount of data and finds the patterns on its own,
as opposed to having a programmer manually write each rule. The more good data it sees, the
better it usually becomes.
The reason this topic matters is that AI is changing how people work, study, travel, get treated in
hospitals and even how countries compete with each other. Understanding it is no longer only for
, computer scientists. Because we will all live and work alongside these systems, it is essential that
each student understand the fundamentals.
2. AI's History and Development
The idea of intelligent machines is older than computers. Ancient myths talked about mechanical
servants, and in the 1800s Ada Lovelace wrote that machines might one day do more than just
calculate. However, AI's true history dates back to the twentieth century.
• 1950: Alan Turing wrote a paper that asked, "Can machines think?" He proposed the
Turing Test, where a machine is considered intelligent if a human cannot tell whether they
are talking to a person or a computer.
• 1956: John McCarthy and others first used the term "Artificial Intelligence" at a workshop
at Dartmouth College. This is usually seen as the birth of the field.
• 1960s and 1970s: Early programs (like ELIZA) could play checkers, prove simple
theorems, and have very basic conversations. Researchers were very hopeful, but
computers were slow and had little memory, so progress stalled. This period led to the first
"AI winter", when funding dropped.
• 1980s: Expert systems became popular. These were programs filled with rules written by
human experts, used in medicine and business. They were expensive to maintain and broke
easily, so interest faded again.
• 1997: IBM's Deep Blue defeated the world chess champion Garry Kasparov.
• 2000s: More data from the internet, faster processors and better algorithms made machine
learning practical.
• 2012: A deep neural network won the ImageNet image recognition competition by a large
margin. This started the modern deep learning boom.
1. Introduction
Artificial Intelligence (AI) and Machine Learning (ML) are now part of everyday life, even if
most of us do not notice them. Some kind of AI is working in the background whenever a bank
flags a strange card payment, YouTube recommends a video, or your phone suggests the next word
in a message.
In layman's terms, Artificial Intelligence is the capacity of a machine or computer to carry out
activities that typically call for human intelligence. These tasks include understanding language,
recognizing faces, making decisions, solving problems and learning from experience. AI includes
machine learning. An ML system is given a large amount of data and finds the patterns on its own,
as opposed to having a programmer manually write each rule. The more good data it sees, the
better it usually becomes.
The reason this topic matters is that AI is changing how people work, study, travel, get treated in
hospitals and even how countries compete with each other. Understanding it is no longer only for
, computer scientists. Because we will all live and work alongside these systems, it is essential that
each student understand the fundamentals.
2. AI's History and Development
The idea of intelligent machines is older than computers. Ancient myths talked about mechanical
servants, and in the 1800s Ada Lovelace wrote that machines might one day do more than just
calculate. However, AI's true history dates back to the twentieth century.
• 1950: Alan Turing wrote a paper that asked, "Can machines think?" He proposed the
Turing Test, where a machine is considered intelligent if a human cannot tell whether they
are talking to a person or a computer.
• 1956: John McCarthy and others first used the term "Artificial Intelligence" at a workshop
at Dartmouth College. This is usually seen as the birth of the field.
• 1960s and 1970s: Early programs (like ELIZA) could play checkers, prove simple
theorems, and have very basic conversations. Researchers were very hopeful, but
computers were slow and had little memory, so progress stalled. This period led to the first
"AI winter", when funding dropped.
• 1980s: Expert systems became popular. These were programs filled with rules written by
human experts, used in medicine and business. They were expensive to maintain and broke
easily, so interest faded again.
• 1997: IBM's Deep Blue defeated the world chess champion Garry Kasparov.
• 2000s: More data from the internet, faster processors and better algorithms made machine
learning practical.
• 2012: A deep neural network won the ImageNet image recognition competition by a large
margin. This started the modern deep learning boom.
