Slides contain about the AI and Intro to ML. Its description evolution applications and risks

1. ARTIFICIAL INTELLEGENCE
AND MACHINE LEARNING

2. CONTENTS
HISTORY
INTRODUCTION
BRIEF HISTORY
FEW THINGS TO KNOW
MACHINE LEARNING
NLP
DEEP LEARNIG
MACHINE LEARNING
CLASSIFICATION
SUPERVISED , UNSUPERVISED
REINFORCEMENT LEARNING
SUPERVISED LEARNING
REGRESSION
BAYES NETWORKS
NEURAL NETWORKS
FUTURE OF AI
FUTURE
RECENT MILESTONES
RISKS OF AI
MYTH vs FACTSWHAT IS NEURAL NETWORK
EXAMPLES

3. What is AI?
Why AI?

4. What is AI ?
• Ar tific ial + Intelligenc e
• It enables computers to perform
tasks (autonomously) that would
normally require human intelligence.
• Using AI, machines make decisions
on their own.

5. W H Y A I ?
A I c a n p r o v i d e :
A U T O M A T I O N
A u t o m a t e s r e p e t i t i v e l e a r n i n g a n d
d i s c o v e r y t h r o u g h d a t a .
P R O G R E S S I V E L E A R N I N G
A d a p t s t h r o u g h p r o g r e s s i v e l e a r n i n g a n d
l e t s d a t a d o t h e j o b .
A C C U R A C Y
W i t h t h e h e l p o f n e u r a l n e t w o r k s , i t c a n
p r e d i c t i n c r e d i b l e a c c u r a c y.

6. HISTORY

7. 01
02 When did AI begin?
03 When was AI first used?
W H O A N D
W H E N
Who invented the term artificial intelligence?

8. 01 Who invented the term artificial intelligence?
• John McCarthy created the term "artificial
intelligence" in 1955 and was a towering figure
in computer science at Stanford most of his
professional life.
• He coined the term artificial intelligence and
is regarded as the father of AI
• In his career, he developed the programming
language LISP in 1958 at MIT.

9. 02 When did AI begin?
In 1950 English Mathematician Alan Turing
published a paper entitled “Computing
Machinery and Intelligence” which opened the
doors to the field that would be called AI.

10. 03 When was AI first used?
A further step towards the development of
modern AI was the creation of ‘The Logic
Theorist’. Designed by Newell and Simon
in 1955 it may be considered the first AI
program.
Herbert Simon Allen Newell

11. Brief
HISTORY
HISTORY
1950
Allen Turing purposed the TURING TEST.
1951
The first AI based program was written.
1955
The first self learning program was created.
1959
The first MIT AI Lab is setup.

12. Brief
HISTORY
HISTORY
1961
The first robot is introduced in GM Assembly line.
1964
The first AI demo program was introduced which understand
Natural language.
1965
The first chat bot ELIZA was invented.
1974
The first autonomous vehicle is created in Stanford AI lab.

13. Brief
HISTORY
HISTORY
1979
INTERNIST is developed that could leverage clinical
knowledge to research its diagnosis.
1997
The IBM Deep Blue chess machine beats Garry Kasparov.
1999
Sony introduced Aibo(a robot dog).
1999
The MIT AI lab demonstrated the first emotional AI.

14. 1 2 3
A Few Things to Know
MACHINE
LEARNING
NATURAL
LANGUAGE
PROCESSING
DEEP
LEARNING

15. MACHINE
LEARNING
Machine learning is the new paradigm where
computer systems and machines use
algorithms to analyze massive (big) data sets
and learn from the data to solve problems on
their own rather than using traditional functional
programming.
Machine learning
1

16. 2
NATURAL
LANGUAGE
PROCESSING
NLP is the computer’s ability to recognize and
understand human speech as it is spoken. The
common NLP tasks are sentence segmentation,
part-of-speech tagging, parsing etc. The eventual
goal of AI-NLP is to communicate to a computer
directly using natural human language than using
computer languages. A human should be able to
talk to the computer like communicating to
another human. Siri, Alexa and Cortana are
starting stages of this movement.
Natural Language Processing
(NLP):

17. DEEP
LEARNING
Deep learning is an advanced facet of AI. This is
the mimicking of the brain by constructing artificial
neural networks, so that the computer is trained to
recognize patterns, images, sound, and text
Deep learning
3

18. 1 2 3
SUPERVISED
LEARNING
UNSUPERVISED
LEARNING
REINFORCEMNT
LEARNING
• Labelled Data
• Direct Feedback
• Predict
Outcome/Future
• No Labels
• No Feedbacks
• Find Hidden Structure in
the Data
• Decision Process
• Reward System
• Learn series of action
DIFFERENT TYPES OF MACHINE
LEARNING

19. LIST OF COMMON MACHINE LEARNING ALGORITHM
Linear Regression
Logistic Regression
Decision Tree
Naive Based

20. LINEAR REGRESSION
GRADIENT DESCENT
W H AT H A P P E N S I N T H E T R A I N I N G P R O C E S S
Form Of Linear
regression
Start with some values of the
coefficients/parameters, e.g. β0=0, β1=0
Keep changing B0 and B1 to reduce the
J(B0, B1) until we hopefully end up at a
minimum.
H E A D I N G
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Y=β0 + β1x
(β1, β0 are the constants)
• M o d e l C o e ff i c i e n t s / P a r a m e t e r s
• C o s t F u n c t i o n / L o s t F u n c t i o n
• E s t i m a t i n g t h e C o e ff i c i e n t s
u s i n g t h e G r a d i e n t D e s c e n t .

21. PROBLEMS WITH LINEAR
REGRESSION
1. It gives the value greater than 1 and less than 0 for the classification problem.
2. It sometimes produces the wrong prediction for the classification problem

22. PROPERTIES OF LOGISTIC
REGRESSION
• Logistic regression is the linear regression
analysis to conduct when the dependent
variable is dichotomous (binary).
• Predictive Analysis
• Logistic regression describes data
,explain the relationship between one
dependent binary variable and one or
more continuous-level (interval or ratio
scale) independent variables.
H E A D I N G
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LOGISTIC REGRESSION

23. PROBABILITY
What is Probability??
Conditional Probability
Bayes Theorem
Naïve Bayes Classifier

24. 1. P(c|x) is the posterior probability of class (c, target) given
predictor (x, attributes).
2. P(c) is the prior probability of class .P(x|c) is the likelihood
which is the probability of predictor given class.
3. P(x) is the prior probability of predictor.
CONDITIONAL PROBABILITY NAÏVE BAYES PROBABILITY

25. SEPARATE SPAM FROM THE
VALID MAIL
1. ”send us your password”->(spam)
2. “send us your review”->(ham)
3. ”review your password”->(ham
4. ”review us”->(spam)
5. ”send your password”->(spam)
6. ”send us your account”->(spam)
New E-mail: “review us now”
H E A D I N G A
S u p e r V I s e d
H E A D I N G B
A d d y o u r s u b - h e a d i n g o r
d e s c r i p t i o n h e r e . T h i s i s a d u m m y
t e x t .
H E SEPARATE SPAM FROM THE VALID MAILA D I N G C
spam ham words
2/4 1/2 password
1/4 2/2 review
3/4 1/2 send
3/4 1/2 us
3/4 1/2 your
1/4 0/2 account
P(spam)=4/6 P(ham)=2/6

26. spam ham words
2/4 1/2 password
1/4 2/2 review
3/4 1/2 send
3/4 1/2 us
3/4 1/2 your
1/4 0/2 account
”REVIEW US NOW”
P(review us |spam)=P(0,1,0,1,0,0|spam)=(1-2/4) (1/4) (1-3/4) (3/4) (1-3/4) (1-1/4)
P(review us |ham) =P(0,1,0,1,0,0|ham)= (1-1/2) (2/2) (1-1/2) (1/2) (1-1/2) (1-0)
P(ham | review us) =(P(review us |ham)* P(ham))/P(review us) =
(0.0625*2/6)/(0.0625*2/6+(0.0044*4/6))

27. B R A I N
N E U R A L
N E T W O R K I N G
1.Dendrites
2.Synapses
3.Large neurons together forms
the neural networks
.
A R T I F I C I A L
N E U R A L
N E T W O R K
L A Y E R S
1 . I N P U T L AY E R
2 . H I D D E N L AY E R
3 . O U T E R L AY E R
A B I L I T Y

29. W E I G H T S A N D B A I S E S
B A C K P R O P A G A T I O N
A C T U A L I N P U T
NEURAL NETWORKS

31. FUTURE OF AI
AUTOMATED TRANSPORT
Tesla’s cars, drones in Dubai, autonomous metro in Delhi.
ASSISTANTS AND IMPROVED ELDER CARE
Google Assistant, Siri , Cozmo, etc.
TAKING OVER DANGEROUS JOBS
Handling of radioactive wastes.
SOLVING CLIMATE CHANGE
Better predictions on weather.
CYBORG TECHNOLOGY
Exponential Learning

35. N A S A o n M a r s U r b a n
C h a l l e n g e B y
D A R PA
G o o g l e b u i l d s
s e l f - d r i v i n g c a r.
S i r i , G o o g l e N o w ,
C o r t a n a , A l e x a
2004 2004 2007 2011-2017
Spirit and Opportunity
rovers navigate
autonomously on
Mars.
Challenge for
autonomous cars to
obey traffic rules in
urban environment.
Google brings its first
autonomous car on
streets for street view
navigation.
Smart assistants using
natural language
processing comes to
phones.
Recent Milestones

36. In 2015, Google’s
DeepMind’s
AlphaGo defeats
European Go
professional Fan
Hui.
In 2017, AlphaGo
defeats Go
champion Ke Jie.

37. B o t d e f e a t s H u m a n
E l o n M u s k ’ s O p e n - A i m a c h i n e -
l e a r n e d b o t d e f e a t s p r o f e s s i o n a l
D o t a 2 p l a y e r D e n d i i n 1 v 1 .
A l p h a Z e r o M a s t e r s
C h e s s i n 4 h r s
A l p h a Z e r o d e f e a t s
S t o c k f i s h 8 ( b e s t c h e s s e n g i n e ) b y
2 8 - 0 i n 1 0 0 g a m e s ( 7 2 d r a w ) .
A I o u t s c o r e s
t o p p e r s a t S t a n f o r d
A l i b a b a ’ s l a n g u a g e p r o c e s s i n g A I
s c o r e s 8 2 . 4 4 i n r e a d i n g a n d
c o m p r e h e n s i o n t e s t a g a i n s t
8 2 . 3 0 4 o n a s e t o n 1 0 0 , 0 0 0
q u e s t i o n s .

38. S O P H I A ( r o b o t )
F i r s t R o b o t t o r e c e i v e c i t i z e n s h i p o f
a n y c o u n t r y
F A C T S A B O U T S O P H I A
N o t a c o n v e n t i o n a l R o b o t
M o d e l l e d a f t e r A u d r e y H e p b u r n , s h e w a s
c r e a t e d b y H a n s o n R o b o t i c s i n
c o l l a b o r a t i o n w i t h A l p h a b e t I n c .
A p p e a r e d o n s e v e r a l
s h o w s .
S o p h i a h a s g i v e n s o m e i n t e r v i e w s , s u n g
i n a c o n c e r t , m e t f a c e - t o - f a c e w i t h s o m e
i n d u s t r y l e a d e r s . W a s g r a n t e d c i t i z e n s h i p
o f S a u d i A r a b i a i n O c t o b e r, 2 0 1 7 .
D e s t r o y H u m a n s ?
S h e o n c e s a i d s h e “ w i l l d e s t r o y h u m a n s ”
w h e n h e r c r e a t o r a s k e d h e r, “ D o y o u w a n t
t o d e s t r o y h u m a n s ? . . . P l e a s e s a y ’ n o . ”

39. U N E M P L O Y E M E N T
P R O G R A M M E D T O D O
S O M E T H I N G
D E VA S TAT I N G
D E V E L O P I N G D E S T R U C T I V E
M E T H O D S T O A C H I E V E I T S
G O A L S
RISKS

40. Myths vs Facts

41. M Y T H
Superintelligence by 2100 is
inevitable/impossible.
M Y T H
Only Luddites worry about
AI.
F A C T
It may happen in decades,
centuries or never. Experts
disagree and we simply don’t
know.
F A C T
Many top AI researchers are
worried.

42. M Y T H I C A L W O R R Y
AI turning evil/conscious.
M Y T H
Robots are the main concern.
A C T U A L W O R R Y
AI competing with goals
misaligned with ours.
F A C T
Misaligned intelligence is the
main concern. It needs no
body, only an internet
connection.

43. M Y T H
AI can’t control human
beings.
M Y T H
Machines can’t have goals.
F A C T
Intelligence enables control.
We control tigers by being
smarter.
F A C T
A heat seeking missile has a
goal.

44. • W i k i p e d i a
• C o u r s e r a
• U d a c i t y
0 8 / 0 2 / 2 0 1 8
T H A N K Y O U
Abhishek Sharma
Heritage Institute of Technology
Kolkata(WB)
CSE
• f u t u r e o f l i f e . c o m
• C r e a t i v e V e n u s
• A I r e v o l u t i o n
You may not realize it, but Artificial Intelligence is all around us.“
Judy Woodruff