A brief introduction to AI and its branches and types. Introduction to Machine learning and Deep Learning

1. AI-Artificial Intelligence
INTRODUCTION
Artificial intelligence is a branch of computer science concerned with developments of methods
that allows computers to learn without programming.To create a digital brain in simple words.
It includes two categories
1.ANI=ARTIFICIAL NARROW INTELLIGENCE(e.g. Good presenter)
2.AGI=ARTIFICIAL GENERAL INTELLIGENCE(Do anything a normal human can do),Q-star-
(Even more than that)will be discussed later.
AGI, or Artificial General Intelligence, represents the pinnacle of artificial intelligence
development, aiming to create machines with human-like cognitive abilities across a wide range
of tasks and domains. Unlike narrow AI systems, which are designed for specific purposes, AGI
possesses the capacity for generalized learning and reasoning, enabling it to adapt and perform
various tasks without explicit programming. Achieving AGI would mean creating machines that
can understand, learn, and solve problems in a manner akin to human intelligence,
encompassing skills such as creativity, abstract thinking, and emotional understanding. While
AGI remains a theoretical concept, its realization holds immense potential for revolutionizing
industries, addressing complex global challenges, and fundamentally altering the relationship
between humans and machines. However, achieving AGI also raises significant ethical,
societal, and existential questions, necessitating careful consideration and responsible
development practices as researchers continue to pursue this ambitious goal.
ANI=ARTIFICIAL NARROW INTELLIGENCE
ANI stands for Artificial Narrow Intelligence, and it refers to AI systems that are designed and
trained for specific tasks or narrow domains. Unlike general AI, which would have the ability to
understand and learn any task a human can, ANI is limited to a predefined set of tasks or
applications. Examples of ANI include virtual assistants like Siri or Alexa, recommendation
systems on streaming platforms, and spam filters in email services. These systems excel at
their specific tasks but lack the broader understanding and adaptability of human intelligence.
ANI plays a crucial role in many aspects of our daily lives, from helping us find information

2. quickly to automating routine tasks, demonstrating the practical applications of artificial
intelligence in various domains.
From here when we will discuss AI it should be understood that we are discussing only ANI ANI
is divided into further TWO SUB GROUPS
ML=Machine Learning
MACHINE LEARNING Is a branch of AI that focuses on methods that can learn from examples
and experiences.
Machine learning is further divided into three parts
1. Supervised Learning
(Model is Leaning with labeled data)
2. Unsupervised Learning
(Discover patterns with unlabeled data)
3. Reinforcement Learning
(Learn to act on feedback or rewards)
DL=Deep Learning
DEEP LEARNING is a category of Machine Learning that focuses on Neural Networks.
Deep learning is like teaching a computer to think and learn just like a human brain
does. It's a special type of technology that uses a network of connected "neurons" to
understand and make sense of information. Imagine you're showing pictures of cats and
dogs to a computer. With deep learning, the computer can learn to tell the difference
between cats and dogs by itself, without being specifically programmed for each task.
It's like teaching a child to recognize animals by showing them pictures over and over
again until they learn on their own. Deep learning helps computers understand and
solve complex problems, like recognizing faces in photos or understanding spoken
language, making it a really powerful tool for all sorts of exciting things in our world
today.

3. Conclusion
Machine learning is a branch of artificial intelligence (AI) that focuses on developing
algorithms and statistical models to enable computers to learn from and make
predictions or decisions based on data. It encompasses a wide range of techniques,
from simple linear regression to complex neural networks. Deep learning, on the other
hand, is a subset of machine learning that involves the use of neural networks with
multiple layers (hence the term "deep") to process and learn from large volumes of data.
Deep learning has revolutionized many fields, including image recognition, natural
language processing, and autonomous driving, by achieving state-of-the-art
performance in tasks that were once considered challenging for traditional machine
learning approaches. Both machine learning and deep learning offer exciting
opportunities for beginners to explore the world of AI and develop solutions to real-world
problems.