The document provides an overview of artificial intelligence (AI), defining it as the science and engineering of creating intelligent machines that can think and act like humans. It discusses the challenges AI faces, such as data privacy and the need for better understanding of human intelligence, and highlights various applications including self-driving cars, robotics, expert systems, and consumer marketing. The document underscores AI's potential in solving complex problems across diverse fields while also recognizing that many challenges remain to be addressed.

1. Artificial Intelligence: Introduction,
Challenges and applications
Dr. Lavanya Sharma
Ms. Sudhriti Sen Gupta

2. Agenda
• Introduction
• Challenging issues
• Application Areas
• Tic Tac gaming

3. CS 484 – Artificial Intelligence 3
Introduction
• What is Artificial Intelligence?
– Systems that think like humans
– Systems that act like humans
– Systems that act or think rationally (logically, correctly)

4. Introduction
• John McCarthy coined the phrase Artificial Intelligence (AI) in 1956
• It is the science and engineering of making intelligent machines,
especially intelligent computer programs.
• It is related to the similar task of using computers to understand
human or other intelligence
• AI is a collection of hard problems which can be solved by humans
and other living things, but for which we don’t have good algorithms
for solving.
– e. g., understanding spoken natural language, medical diagnosis,
chess playing, proving math theories and many more.
• Intelligence is the computational part of the ability to achieve goals
in the world.

5. What is Intelligence?
The Turing Test
A machine can be described as a
thinking machine if it passes the
Turing Test. i.e. If a human agent
is engaged in two isolated
dialogues (connected by teletype
say); one with
a computer, and the other with
another human and the human
agent cannot reliably identify
which dialogue is with the
computer.
Fig.1 Turing Machine

6. ICAI'06 Eunika Mercier-Laurent 6
Challenges for AI
• Better understanding of our brain capacity
• Better use of computer capacity
• Data privacy and security: Most AI applications rely on huge
volumes of data to learn and make intelligent decisions

7. Fig.2. Challenging Issue in AI

8. Applications Areas

9. 9
Fig.3. Applications of AI

10. Artificial Intelligence in Movies
Fig.4. AI in Graphics

11. Artificial Intelligence in Real Life
A young science (≈ 50 years old)
– Impressive success stories
– “Intelligent” in specialized domains
– Many application areas
Face detection Formal verification
Industry Gaming Medical Domain
Detection and Tracking
Fig.5 Real time Applications

12. Smart cars
• Self-driving cars are becoming increasingly a reality with each passing
moment.
• For Eg. Google's self-driving car project (on going)
Fig 6.Self Driving Car: Google Project

13. Object Detection and Tracking
• Moving object or human detection in Indoor
or Outdoor Scenario’s
Fig.7. Moving object detection in Indoor or outdoor Video
sequence

14. Robots
• Excel at performing simple, repetitive tasks
• Free workers from tedious or hazardous jobs
• Have limited mobility
• Operation is controlled by a computer program that
includes commands
• Includes programming languages for controlling
• Variable Assembly Language (VAL)

15. Honda Humanoid Robot
Walk
Turn
StairsFig.8. Robotics

16. Fig.9. Military Robots

17. Expert Systems
• Mimics human expertise in a field to solve a problem in a
well-defined area
• Used for activities that human experts have already
handled successfully
• Tasks in medicine, geology, education, and oil
exploration
– For E.g. Dendril and Mycin

18. Surveillance
• AI can be trained using supervised exercises,
security algorithms to take input from security
cameras.
• Eventually, they can identify potential threats
and warn human security officers to
investigate further.

19. Preserving Wildlife
• Wildlife preservation is notoriously difficult, especially when
attempting to analyze population sizes or track animals.
• Scientists, simply, cannot possibly track every animal or tag
them all with GPS devices.
• A team in Chicago have successfully implemented a form of
AI, developed by Wildbrook.org to track animals
Fig.9. Wildlife Surveillance

24. Consumer Marketing
• Have you ever used any kind of
credit/ATM/store card while shopping?
– if so, you have very likely been “input” to an AI
algorithm
• All of this information is recorded digitally
• Companies like Nielsen gather this
information weekly and search for patterns
– general changes in consumer behavior
– tracking responses to new products

25. Identification Technologies
• ID cards
– e.g., ATM cards
– can be a nuisance and security risk:
• cards can be lost, stolen, passwords forgotten, etc
• Biometric Identification
– walk up to a locked door
• camera
• fingerprint device
• microphone
– computer uses your biometric signature for
identification
• face, eyes, fingerprints, voice pattern

26. Machine Translation
• Language problems in international business
– e.g., at a meeting of Japanese, Korean, Vietnamese and
Swedish investors, no common language
– or: you are shipping your software manuals to 127
countries
– solution; hire translators to translate
– would be much cheaper if a machine could do this!
– algorithms are developed which combine dictionaries,
grammar models, etc.

28. Turn of X

30. MiniMax Algorithm: Turn of X

33. Minimax Algorithm

37. References
• Lavanya Sharma, “Introduction: From Visual Surveillance to Internet of Things”,
From Visual Surveillance to Internet of Things”, Taylor & Francis, CRC Press, Vol.1,
pp.14
• Shubham, Shubhankar, Mohit, Lavanya Sharma, “Use of Motion Capture in 3D
Animation: Motion Capture Systems, Challenges, and Recent Trends”, in 1st IEEE
International Conference on Machine Learning, Big Data, Cloud and Parallel
Computing (Com-IT-Con), India, pp. 309-313, 14th -16th Feb
• Sharma, L. (Ed.), Garg, P. (Ed.). (2020). From Visual Surveillance to Internet of
Things. New York: Chapman and Hall/CRC,
https://doi.org/10.1201/9780429297922
• Rich and Knight, “Artificial Intelligence”, Tata McGraw Hill, 1992
• S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second
Edition, Pearson Edu.
• KM Fu, "Neural Networks in Computer Intelligence", McGraw Hill
• Russel and Norvig, "Artificial Intelligence: A modern approach", Pearson Education

38. Thank you