ARTIFICIAL
INTELLIGENCE
INTRODUCTION OF AI
• Artificial intelligence (AI) is the simulation of human intelligence in machines that
are programmed to think and act like humans. Learning, reasoning, problem-
solving, perception, and language comprehension are all examples of cognitive
abilities.
•
• Artificial Intelligence is a method of making a computer, a computer-controlled
robot, or a software think intelligently like the human mind. AI is accomplished by
studying the patterns of the human brain and by analysing the cognitive process.
The outcome of these studies develops intelligent software and systems.
HISTORY OF AI
• The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumours of artificial beings endowed with intelligence or
consciousness by master craftsmen. The seeds of modern AI were planted by philosophers who attempted to describe the process of human
thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the
1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a hand ful of scientists
to begin seriously discussing the possibility of building an electronic brain.
•
• The field of AI research was founded at a workshop held on the campus of Dartmouth College, USA during the summer of 1956.[1] Those
who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being
would exist in no more than a generation, and they were given millions of dollars to make this vision come true.[2]
•
• Eventually, it became obvious that researchers had grossly underestimated the difficulty of the project.[3] In 1974, in respo nse to the criticism
from James Light hill and ongoing pressure from congress, the U.S. And British Governments stopped funding undirected research into
artificial intelligence, and the difficult years that followed would later be known as an “AI winter”. Seven years later, a visionary initiative by the
Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 1980s the investors became
disillusioned and withdrew funding again
TYPES OF DATA IN AI
• THERE ARE TWO TYPES OF DATA
• 1 TRAINING DATA
• 2. TESTING DATA
SOURCES OF DATA IN AI
• INTERNET SOCIAL MEDIA ACCOUNTS
• INTERNET SEARCH ENGINES
• GOVERNMENT SYSTEMS
• BUISNESS SYSTEM
• IOT. DEVICES
DOMAIN OF AI
• 1 DATA TRAINING AND TESTING DATA
• NATURAL LANGUAGE PROCESSING (NLP)
• COMPUTER VISION
COMPUTER VISION AND IT’S
APPLICATION
• Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural
networks to teach computers and systems to derive meaningful information from digital
images, videos and other visual inputs
• Facial recognition.
• Self-driving cars.
• Robotic automation.
• Medical anomaly detection.
• Sports performance analysis.
• Manufacturing fault detection
NLP AND ITS APPLICATION
• Natural language processing (NLP) is a machine learning technology that gives computers
the ability to interpret, manipulate, and comprehend human language. Sentiment Analysis.
• NLP APPLICATIONS ARE
• Chatbots & Virtual Assistants.
• Text Extraction.
• Machine Translation.
• Text Summarization.
• Market Intelligence.
MACHINE LEARNING
• Machine learning (ML) is defined as a discipline of artificial intelligence (AI)
that provides machines the ability to automatically learn from data and past
experiences to identify patterns and make predictions with minimal human
intervention.
Deep learning
• Deep learning is a method in artificial intelligence (AI) that teaches
computers to process data in a way that is inspired by the human brain. Deep
learning models can recognize complex patterns in pictures, text, sounds, and
other data to produce accurate insights and predictions.
Applications of machine learning
• Image Recognition.
• Speech Recognition.
• Recommender Systems.
• Fraud Detection.
• Self Driving Cars.
Applications of Deep learning
• Fraud detection.
• Customer relationship management systems.
• Computer vision.
• Vocal AI.
• Natural language processing.
• Data refining.
• Autonomous vehicles.
• Supercomputers
Future scope of AI
• Emerging trends: AI in healthcare, finance, education, and beyond
• Ethical considerations and challenges: Bias, transparency, accountability
• The role of AI in future innovations: Quantum computing, AI governance,
collaborative AI systems
BIBLIOGRAPHY
• Books and Articles:
•
• Russell, S., & Nervi, P. (2016). Artificial Intelligence: A Modern Approach (3 rd ed.). Pearson.
• Good fellow, I., Bingo, Y., & Coolville, A. (2016). Deep Learning. MIT Press.
• Mitchell, T. (1997). Machine Learning. McGraw-Hill Education.
• Websites:
• Wikipedia
• Wikihow
•
CERTIFICATE
• This presentation has been created by [Your Name], a dedicated researcher and student, with the aim of
exploring the various aspects and advancements in the field of Artificial Intelligence.
•
• The sources and references used in this presentation are duly acknowledged in the bibliography section. This
presentation has been prepared with the utmost sincerity and diligence.
•
• Presented by:
• Ayush Kumar rai
•
• Date: 20 may 2024
ACKNOWLEDGEMENT
• I Would like to express my sincere gratitude to everyone who
supported me in the preparation of this PowerPoint
presentation on Artificial Intelligence.
•
• First and foremost, I extend my deepest thanks to my
instructor, MR SANJAY SIR, for their invaluable guidance,
suggestions, and constant encouragement throughout this
project.
•
• I am also grateful to my family and friends for their
unwavering support and understanding during the times I was
engrossed in research and preparation.
