Unit 1 : Introduction

Lecture 1:Artificial Intelligence and its applications

Dr. Vibhu Jately

School of Computer Science
UPES, Dehradun
India
 Table of Contents

1. Introduction
2. Definition of Artificial Intelligence
3.Types of AI
4. Applications of AI
Data Science
To different people data science means different things

Data science is using data to answer different questions

Computer Science
Broad definition as it
is a broad field Mathematics

Statistics
 Data science is a
multidisciplinary
approach to extract
knowledge from large
and ever-increasing
volumes of data
collected from various
sources.
Data science involves preparing data for analysis and processing
and presenting the results to reveal patterns and enable
stakeholders to draw informed conclusions
 Data Collection Data Preparation

 Data Acquisition
 Data Extraction  Data Cleaning
 Image and Video  Data Aggregating
signal reception

 Predictive Analysis
 Classification
Data Visualization  Clustering
 Data Mining

Data Processing
Data Science Life Cycle
BASIC DATA TYPE
• Qualitative data- It provides information about the quality of an object
or information which cannot be measured. It is also called as
categorical data.
For example: Good, Poor, Average.
• Quantitative data relates to information about the quantity of an
object-hence it can be measured. It is also termed as numeric data.
For example: Marks, Height, Weight.
 It has been estimated that 1.2 zettabytes worth of
information is currently available and this number is
growing exponentially.

Value Prefix
1024 Yotta
1021 Zetta
1018 Exa
1015 Peta
1012 Tera
109 Giga
106 Mega
 ARTIFICIAL
ARTIFICIAL INTELLIGENCE
INTELLIGENCE

 Intelligence: “Ability to learn, think and reason”

 Artificial Intelligence: Artificial intelligence (AI) is the
simulation of human intelligence by machines.
• The ability to solve problems
• The ability to act rationally
• The ability to act like humans
AI is the science and engineering of making intelligent
machines, especially intelligent computer programs.
 NEURAL NETWORKS AND
OPTIZATION TECHNIQUES
IN
SMART ANTENNAS
The term artificial intelligence was first
coined in 1956, at the Dartmouth
conference, and since then Artificial
Intelligence has expanded because of the
theories and principles developed by its
dedicated researchers.
EARLY HISTORY

Alan Turing John McCarthy

Turing Test
 Definition
Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems or machines
capable of performing tasks that typically require human intelligence. These tasks include:
• Reasoning
• Learning
• Problem solving
• Decision-making
• Perception (TESLA cars)
AI aims to mimic human cognitive functions through the development of algorithms and computational
models.

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 Types of AI
• Narrow AI (Weak AI):
• Definition: AI that is designed and trained for a specific task or a narrow range of tasks.
• Example: Virtual assistants like Siri and Alexa that can perform tasks like setting reminders or playing
music.
• General AI (Strong AI):
• Definition: AI with the ability to understand, learn, and apply knowledge across a broad range of tasks,
much like a human.
• Example: This level of AI remains theoretical and has not yet been achieved.
• Superintelligent AI:
• Definition: AI that surpasses human intelligence and capabilities in all aspects.
• Example: A hypothetical concept that raises ethical and safety concerns.

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 Applications
Artificial Intelligence (AI) is a rapidly advancing field that involves the creation of systems capable of performing tasks that typically
require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. Here
are some of the key applications of AI across various sectors:
1. Healthcare
• Diagnostics and Treatment: AI systems can analyze medical data to assist in diagnosing diseases more accurately and rapidly. For
example, IBM's Watson for Oncology helps in cancer diagnosis and treatment recommendations.
• Personalized Medicine: AI can tailor treatments to individual patients by analyzing genetic information and medical histories,
thus enhancing the effectiveness of therapies.
• Robotics: AI-powered robots assist in surgeries, providing precision and control beyond human capabilities. The da Vinci Surgical
System is a prominent example.
2. Finance
• Fraud Detection: AI algorithms analyze transaction patterns to identify and prevent fraudulent activities in real time.
• Trading: AI-driven trading systems can process vast amounts of data faster than humans, identifying patterns and making trades
at optimal times. Quantitative trading firms extensively use such technologies.
• Customer Service: Chatbots and virtual assistants handle customer inquiries and provide financial advice, improving customer
service efficiency.
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3. Transportation
• Autonomous Vehicles: Companies like Tesla and Waymo are developing self-driving cars that use AI to navigate roads, avoid
obstacles, and make real-time driving decisions.
• Traffic Management: AI systems optimize traffic flow in cities by analyzing data from various sources, reducing congestion and
improving transportation efficiency.
• Logistics and Supply Chain: AI helps in route planning, demand forecasting, and inventory management, enhancing the
efficiency of supply chains.

4. Retail
• Personalized Shopping Experiences: AI algorithms analyze customer data to provide personalized product recommendations,
improving the shopping experience.
• Inventory Management: AI systems predict demand trends and manage inventory levels to ensure that products are available
when needed without overstocking.
• Customer Service: AI-powered chatbots and virtual assistants handle customer inquiries, process orders, and provide support,
improving customer satisfaction.

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5. Manufacturing
Predictive Maintenance: AI monitors equipment to predict failures before they occur, reducing downtime and maintenance costs.
Quality Control: AI systems inspect products for defects, ensuring higher quality and consistency in manufacturing processes.
Robotic Process Automation: AI-driven robots perform repetitive tasks with high precision, increasing production efficiency and
safety.

6. Education
Personalized Learning: AI provides customized learning experiences based on individual student needs, preferences, and progress.
Administrative Tasks: AI automates administrative tasks such as grading and scheduling, allowing educators to focus more on
teaching.
Virtual Tutors: AI-powered virtual tutors offer additional support to students, helping them understand complex subjects and
providing instant feedback.

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7. Agriculture
Precision Farming: AI analyzes data from sensors and drones to optimize planting, watering, and harvesting, increasing crop yields
and resource efficiency.
Crop Monitoring: AI systems detect diseases, pests, and nutrient deficiencies in crops, enabling timely intervention and reducing
losses.
Supply Chain Optimization: AI improves the efficiency of the agricultural supply chain by predicting demand and optimizing
logistics.

8. Entertainment
Content Creation: AI creates music, art, and literature, pushing the boundaries of creativity. Examples include AI-generated music
by Amper Music and AI-written articles by tools like GPT-3.
Personalized Content: Streaming services like Netflix and Spotify use AI to recommend movies, shows, and music based on user
preferences and viewing/listening history.
Game Development: AI enhances video game development by creating intelligent NPCs (non-player characters) and procedural
content generation.

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9. Security
Surveillance: AI systems analyze video feeds to detect unusual activities and potential security threats in real time.
Cybersecurity: AI detects and responds to cyber threats by analyzing patterns in network traffic and identifying
anomalies.
Identity Verification: AI enhances security in access control systems through facial recognition and biometric
analysis.

10. Environment
Climate Modeling: AI analyzes climate data to improve weather forecasts and model climate change scenarios.
Wildlife Conservation: AI tracks animal movements and monitors habitats to aid in wildlife conservation efforts.
Energy Management: AI optimizes energy use in buildings and industries, contributing to more sustainable energy
consumption.

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11. Customer Service
Virtual Assistants: AI chatbots handle routine customer service tasks, providing instant responses and solutions.
Sentiment Analysis: AI analyzes customer feedback to understand sentiment and improve service quality.
Automated Support: AI-driven systems provide 24/7 customer support, resolving issues quickly and efficiently.

12. Legal
Legal Research: AI accelerates legal research by analyzing documents and identifying relevant case laws.
Contract Analysis: AI reviews contracts for compliance and highlights potential risks.
Predictive Analytics: AI predicts case outcomes based on historical data, assisting lawyers in decision-making.

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 Summary
The applications of AI are vast and continually expanding, transforming industries and improving efficiency, accuracy, and
outcomes across various fields. As AI technology evolves, it is expected to play an even more integral role in solving
complex global challenges and driving innovation.

The application domains of AI are diverse and continuously expanding, demonstrating the technology's versatility and
transformative potential. By integrating AI into various sectors, organizations can enhance efficiency, improve decision-
making, and drive innovation. As AI technology advances, its impact on these domains is expected to grow, offering even
more sophisticated and effective solutions.
 Reference Material
Textbooks

1. Artificial Intelligence by Rich and Knight, The McGraw Hill, 2017.

2. Machine Learning for Dummies, By John Paul Mueller and Luca Massaron, For Dummies,
2016.
3. "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig

4. "Introduction to the Theory of Computation" by Michael Sipser

5. "Artificial Intelligence: Foundations of Computational Agents" by David L. Poole and Alan K.

Mackworth

6. "Artificial Intelligence: A New Synthesis" by Nils J. Nilsson

7. "Machine Learning Yearning" by Andrew Ng

Thank You

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