My Slide Deck on AI and ML.

1. ARTIFICIAL INTELLIGENCE
JOURNEY TO A WORLD WHERE COMPUTERS DREAM AND DESIRE

2. HOW DO WE NORMALLY CODE?
• Addition of two numbers
• Date (The four pages program in
OOPs lab)
print("Enter two Numbersn")
a = int(raw_input('A='))
b = int(raw_input('B='))
c=a+b
print ('C= %s' %c)
#include<iostream.h>
#include<stdlib.h>
int a[13]={0,31,28,31,30,31,30,31,31,30,31,30,31};
int h[13]={0,31,29,31,30,31,30,31,31,30,31,30,31};
int flag1=0;
class date
{
int flag, day, month, year;
public:
date(int d, int m, int y)
{ day=d;
month=m;
year=y;
if(year%400==0||(year%4==0&&year%100!=0))
flag=1;
else
flag=0;
}
}

3. HOW DO WE NORMALLY CODE? (Contd..)
• We understand the given problem.(Sometimes at least)
• We code the program with a pre-existing algorithm or CCP :P
• We test it with some test cases that you can think about.
• And then shout! “Ma’am I got the output ma’am!”
My question is.. Is your program really
intelligent?
NO! It is an explicitly written program which has no intelligence involved.

4. WHEN IS YOUR PROGRAM INTELLIGENT?
• A program is said to be intelligent when the program can learn
by itself to improve its decision making skill.
• “A computer program is said to learn from experience E with
respect to some task T and some performance measure P, if its
performance on T, as measured by P, improves with experience
E.”
Tom Mitchell
And that’s what Artificial Intelligence is all about.

5. What Is AI?
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 intelligence.

6. MACHINE LEARNING

7. THE MACHINE LEARNING APPROACH
• Instead of writing a program by hand for each specific task, we
collect lots of examples that specify the correct output for a
given input.
• A machine learning algorithm then takes these examples and
produces a program that does the job.
• Massive amounts of computation are now cheaper than paying
someone to write a task-specific program.

8. THREE TYPES OF LEARNING
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

9. Supervised Learning
• The computer is presented with example inputs and its
target(output), given by a "teacher", and the goal is to learn a
general rule that maps inputs to outputs.
• Categories:
• Regression
• Classification

10. Supervised Learning: Example
• Suppose you have been given a basket filled with some fresh fruits
and your task is to arrange the same type fruits at one place.
• The fruits are apple, banana, cherry, grape.
• So you already know from your knowledge that, the shape of each
and every fruit. So it is an easy to arrange the same type of fruits at
one place.
• Here your previous work is called as training data in data mining.
• So you already learn the things from your training data, This is
because of you have a response variable which says you that if some
fruit have so and so features it is grape, like that for each and every
fruit.
• This type of learning is called as Supervised Learning.
• This type solving problem come under Classification.
• So you already learnt the things so you can do you job confidently.

11. Supervised Learning: Real World Example
• Prediction of stock prices
• Predicting weather
• All examples with historical data

12. Unsupervised Learning
• The computer is presented with example inputs and it has “no
teacher” and no target, the goal is to learn a general rule that
infers output from the input.
• Category
• Clustering

13. Unsupervised Learning: Example
• Suppose you have been given a basket filled with some fresh
fruits and your task is to arrange the same type fruits at one
place.
• The fruits are apple, banana, cherry, grape.
• This time you don't know any thing about that fruits, you are
seeing them for the first time. So how will you arrange the
same type of fruits?
• What you will do is select any physical character like color, size,
shape and try to arrange them according to the physical
appearance.
• This type of learning is called as Unsupervised Learning.

14. Examples Of Unsupervised Learning
• Clustering similar News articles under one relevant
headline.(https://news.google.co.in)

15. Other Examples
• Social network analysis
• Image Processing
• Speech Recognition

16. Reinforcement Learning
• The computer is presented with a dynamic environment and the
goal is to perform a task with the continuously changing
environment without a teacher explicitly telling it whether it has
come close to its goal or not.

17. Reinforcement Learning: Real World
Example
• Autonomous cars(self-driving car)
• Google Self-driving cars
• Tesla Model S – Autopilot
mode

18. Reinforcement Learning: Real World
Example
• Gaming bots
• Deep blue : Chess playing computer developed by IBM. It the
masterpiece of a truly intelligent player. It won against Garry
Kasparov(Chess grandmaster) in a match becoming the first computer
system to defeat a reigning world champion in a match under standard
chess tournament time controls.

19. APPLICATIONS OF ARTIFICIAL
INTELLIGENCE
AND MACHINE LEARNING

20. Adaptive Websites
• Product recommendations

21. Computer Vision
• Understanding images
• Face recognition
Oh Mark!! :O

22. Natural Language Processing(NLP)
• Making computer systems understand natural human language.
• Extensively uses Formal Language(Automata) Theory
• Ex: Google Now by Google, Siri by Apple Inc, Cortana by
Microsoft.
Poor chap, got rejected by a
computer. :D

23. Well, can you believe that it is a machine that is talking to its
user?

24. Speech Recognition
• Making computer systems understand the human voice and
converting it into text.
• Ex: Google Voice

25. Search Engines
• Using Intelligent search algorithms to bring the best and relevant
results to the users.
• Ex: Google Search Engine, Bing etc.
• Includes semantic search

26. And a lot more use cases
• Internet fraud detection
• Spam detection in E-mails
• Robotics
• Online Advertisements
• Computational Linguistics(Sentimental Analysis)
• Software Engineering too :P

27. IMPLEMENTATION
HOW IS IT IMPLEMENTED?

28. VARIOUS METHODS & TOOLS
• Linear Regression
• Logistic Regression
• Decision trees
• k-NN (nearest neighbors algorithm)
• Naive Bayes classifier
• Neural Networks
• .. And a lot more ..

29. NEURAL NETWORKS

30. NEURAL NETWORKS
• Algorithms that try to mimic the brain.
• Was very widely used in 80s and early 90s; popularity
diminished in late 90s.
• Recent resurgence: State-of-the-art technique for many
applications.
• The “one learning algorithm” hypothesis
• The field dealing with neural networks is called as Deep
Learning

31. REPRESENTATION
Three major parts:
• Synapses(Nerve Ending)
• Axon
• Dendrite

32. REPRESENTATION
Three layers:
• One Input layer
• N Hidden layers(processing part of the neural
network)
• One Output layer
Three major components:
• Nodes
• Layers
• Edge Weights

33. COMPLEXITY
This neural network can be
used to process a 28x28
pixel image.

34. Types
• Feedforward neural network (Unidirectional)
• Recurrent neural networks (Bidirectional)
Interesting Notes
• No one really knows how a Neural Network works/learns. No
One!
• They can compute anything(at least theoretically)
• But Neural Networks are slow learners
• They can DREAM!

35. DREAMS

38. Leonardo DiCaprio

41. 

42. DREAMS: What Do They Signify?
• With simple words you give to an AI program a couple of
images and let it know what those images contain (what objects
- dogs, cats, mountains, bicycles, ... ) and give it a random
image and ask it what objects it can find in this image.
• Then the program starts transforming the image till it can find
something similar to what it already knows and thus you see
strange artifacts morphing in the dreamed image (like eyes or
human faces morphing in image of a pyramid).

43. ADVANCEMENTS AND BREAKTHROUGHS
IN AI AND ML

44. ADVANCEMENTS
1. Cheap parallel computation.
2. Vast amount of unstructured and unmined data (It’s also
cheap).
3. Advanced Learning algorithms.
4. Many Bots developed which defeated humans in a particular
task.
5. Cloud computing has given us the power of utilizing high end
servers.
6. Tesla successfully launched its Autopilot mode in cars.

45. CURRENT SCENARIO
1. AI is changing the face of Indian IT services
• Automating repeated tasks
2. Driverless or autonomous cars by Google, Tesla, Uber.
3. Search by Images
4. Personalized content
5. Self aware Robots
6. Evolution of Emotional Intelligence
7. IoT + AI = Next Big Thing!

46. Why Learn AI now?
• Machine Learning and AI are being used extensively in the
current scenario.
• Even a matrimonial/dating site uses Machine Learning. :/
• Every human likes his computer to be personalized for him.
• You can build products that can change the way humans live.

47. QUESTIONS?

48. FURTHER READING
• Online courses
• Coursera, Udacity and other MOOC.
• Stanford University
• Text Books
• T. Mitchell (1997). Machine Learning, McGraw-Hill Publishers.
• Peter, Norvig. Artificial Intelligence: A Modern Approach, Pearson.
• Journals, e.g.
• Machine Learning, Kluwer Academic Publishers.
• Journal of Machine Learning Research, MIT Press.
• Conferences, e.g.
• International Conference on Machine Learning (ICML)
• Neural Information Processing Systems (NIPS)
• AI Blogs and Communities

49. “
”
ANY A.I. SMART ENOUGH TO PASS A
TURING TEST IS SMART ENOUGH TO
KNOW TO FAIL IT.
IAN MCDONALD
Thank you!