An attempt to demystify the hype around Artificial Intelligence with the help of real-world implementations.

1. Ramya Mopidevi

2. Artificial Intelligence
2
Artificial intelligence aims to create intelligent machines similar to human beings that perceive their environment and
take actions that maximize their chance of success at some goal.
Sociology
Philosophy
Computer
Science
Psychology
Neuron
Science
Biology
Mathematic
s
Areas Contributing to AI Key AI Enablers
Parallel and cheaper
computing
Innovative
Algorithms
Real-time
Data and Big
Data
 Knowledge
 Planning
 Reasoning
 Problem Solving
 Perception
 Learning
 Motion & Manipulation
AI Goals

3. Types of Artificial Intelligence
3
General Intelligence
Superintelligence
Perform any intellectual
task that a human being can
Scientific Creativity,
General wisdom and social
skills
Planning
Strategy for performing an
action
Expert Systems
Solve domain specific
complex problems
Machine
learning
Continuous learning for
better prediction
Speech & Voice
Recognition
Understand spoken
language to perform an
action
Natural Language
Processing
Dialogue between human
and intelligent machines
Vision Systems
Image processing &
computer vision using
learning algorithms
Robotics
Motion and manipulation in
physical world
WeakAINarrowAI
FullAIAI-Complete
Applied AI
Strong AI

5. Planning
5
AI that is concerned with the decision making and realization of strategies or action sequences performed by
intelligent agents, autonomous robots and unmanned vehicles when trying to achieve a goal
Artificial Intelligence
Planning
Actions on
Agents
to achieve
goals
Goals
Initial States
AI Planning Systems & Techniques
• The Stanford Research Institute
Problem Solver (STRIPS) using
Planning Domain Definition
Language (PDDL)
• GraphPlan
• Hierarchical Task Networks
• Partial Order Planning (PoP)
• Preference based planning
Algorithms for Planning
• Classical planning
• Reduction to other problems
• Temporal planning
• Probabilistic planning
Benefits of Planning
 Goal directed
 Reducing search
 Resolving goal conflicts
 Easy error recovery

7. Expert Systems
7
The expert systems are the applications developed to solve complex problems in a particular domain, at the level of
extra-ordinary human intelligence and expertise.
Benefits of Expert Systems
 Improved Decision Quality
 High Performance
 Availability
 Speed
 Less error rate
 Steady response
Domain Description
Financial Decision
Making
Insurance companies have used expert systems to assess the risk presented by the
customer and to determine a price for the insurance
Design and
Manufacturing
Assist in the design of physical devices and processes, ranging from high-level
conceptual design of abstract entities all the way to factory floor configuration of
manufacturing processes.
Process Monitoring
and Control
Analyze real-time data from physical devices with the goal of noticing anomalies,
predicting trends, and controlling for both optimality and failure correction
Inference Engine Strategies:
 Forward Chaining – “What
can happen next”
 Backward Chaining –
“Why this happened”
User
(May not be
an expert)
Domain
Expert
Knowledge
Engineer
Knowledge
Base
Facts (IF-THEN Rule)
Inference
Engine
Rules & Reasoning
User
Interface
Knowledge
Expert System

9. Machine Learning
9
Gives the computers the ability to learn without being explicitly programmed. It continuously observes a series of actions
performed over a period of time and uses this knowledge to build and enhance the predictive model for better decision making
Machine
Learning
Input
Output
Algorithm
Build Predictive
Model
Training
Data
Learn
Algorithm
Perform
Tasks
Feedback
Supervised Machine LearningMachine Learning
Supervised
Algorithms that can
apply what has been
learned in the past to
new data
Unsupervised
Clustering of
observation data to
come up with unknown
pattern
Deep
Learn through
hierarchy of simple and
complex concepts
Reinforcement
Observe environment
and make adjustments
if negative for better
decision making
Active
Learn by asking
questions to increase
confidence
Evolutionary
Learn to optimize using
introduced randomness
Benefits
 Learning from high
volume data
 Feature learning in a
short span of time
 Parameter
optimization

10. Machine Learning Industry Trends
10
Top 5 Machine Learning APIs
Domain Description
Data Security Machine learning algorithms can look for patterns in how data in the cloud is
accessed, and report anomalies that could predict security breaches.
Personal Security Machine learning can speed up the screening process at public places such as
airports, stadium, concerts etc. significantly to ensure safer events and
eliminate false alarms
Financial Trading Many prestigious trading firms use proprietary machine learning systems to
predict and execute trades at high speeds and high volume.
Healthcare Machine learning systems are vastly used in medical industry for predicting
cancer, diabetics risk factors even before they are diagnosed
Marketing
Personalization
Using Machine learning companies can personalize which emails a customer
receives, which direct mailings or coupons, which offers they see, which
products show up as “recommended” and so on, all designed to lead the
consumer more reliably towards a sale.
Automotive Machine learning allows smart car to learn about its owner and its
environment. It will adjust the internal settings — temperature, audio, seat
position, etc. — automatically based on the driver, report and even fix
problems itself
• Facebook ML algorithm to
prevent suicide
• Facebook personalized news
feed
• Google search engine
• Google Maps
• Google Assistant
• Waymo - Self Driving car
• Cortana Intelligence suite
• Kinect Gesture Recognition
• MS Word Editor to flag words
Real-World Machine Learning

11. Deep Learning
11
Deep learning uses Artificial Neural Network with many layers to learn optimal model parameters for a feature extraction.
It creates an algorithm that will automatically decide which features work best to accomplish a task. Deep learning can be
trained both in supervised or unsupervised manner.
• Hidden nodes form multiple layers of nonlinear
processing units
• These units transforms low-level input data into high-
level representations of data by finding patterns at
each preceding level forming complex patterns for
more accurate prediction
Input nodes similar to
neurons that receive input
signal – (images of
Cat/Dog/Man/Women)
Output nodes are
similar to neurons that
sends and output
signal (Result
Prediction)
Benefits
 Inputs can be texts,
images, sensors’ data and
even sound
 Pattern recognition

12. Deep Learning Industry Trends
12
Application Description
Colorization of
Images
Deep learning can be used to use the objects and their
context within the photograph to color the image, much like a
human operator might approach the problem.
Sounds To
Silent Movies
A deep learning model associates the video frames with a
database of pre-rerecorded sounds in order to select a sound
to play that best matches what is happening in the scene.
Automatic
Machine
Translation
Text translation can be performed without any preprocessing
of the sequence, allowing the algorithm to learn the
dependencies between words and their mapping to a new
language
Object
Classification in
Photographs
Deep learning enables classification of objects within a
photograph as one of a set of previously known objects
Real-World Machine Learning
• Google Brain Project
• DeepMind (Acquired)
Deep learning algorithms are applied to the other fields like
• Computer vision
• Automatic speech recognition
• Natural language processing
• Bioinformatics

14. Speech and Voice Recognition
14
Automatic speech and voice recognition is computer's ability to understand and translate a spoken language and into text or
perform some task associated with it. Deep Learning and Big Data are key enablers for Automatic Speech Recognition.
Modern End-to-End Automatic Speech
Recognition Systems
• Listen, Attend and Spell (LAS)
• Latent Sequence Decompositions (LSD)
• Watch, Listen, Attend and Spell" (WLAS)
Speech
Recognition
Speaker independent, hence training is not
involved
Voice
Recognition
Speaker dependent, hence software needs to
be trained with unique characteristics of
speaker’s voice. Typically used for biometric
authentication
Analog to
Digital
Acoustic
Model
Language
Model
Speech
Engine
Display
Feedback
Acoustic Model creates statistical representation of sounds that make up each word
Language Model captures the properties of language
Benefits of Speech Recognition
 Reduce costs
 Increase effectiveness
 Emotion analysisSpeech Recognition Process

15. Speech Recognition Industry Trends
15
Liv.Ai’s API – Gappi chat app available in multiple Indian languages that converts speech into text.
Application Description
Hands-free
Navigation
Ask destination distance and time to reach on GPS connected digital
maps
Automated
Identification
Create a ‘voiceprint’ based on specific text such as ‘Name’ and
‘Account Number’ which is stored against the individual’s record. so
when they next call, they can simply say their name and the person is
put straight through to a customer service representative
Removing IVR
menus
‘intelligent call steering’ (ICS) does not involve any ‘button pushing’. The
system simply asks the customer what they want (in their words, not
yours) and then transfers them to the most suitable resource to handle
their call.
• Google
• Microsoft
• IBM
• Baidu - Deep Speech 2,
• Apple
• Amazon
• Nuance
• SoundHound
• IflyTek
Top Speech Recognition Companies

17. Natural Language Processing
17
Natural language processing refers to AI method of communicating with intelligent system permitting a human-computer
dialogue in a conversational, day-to-day natural language such as English.
Natural
Language
Understanding
(NLU)
• Mapping the natural language input
into useful representations
• Analyzing different aspects of the
language
Natural
Language
Generation
(NLG)
• Text planning: retrieving relevant
content from knowledge base
• Sentence planning: Choosing required
words, forming phrases and setting
tone of the sentence
• Text Realization: Mapping sentence
plan into sentence structure
NLP is difficult because of ambiguities associated
with inconsistencies in human natural language
Benefits
 Fast return on value
 Real-time analysis
 Handles any format of
information
parsing Translating Generating Input
Phonology: Interprets
sound within and across
words
Morphology: Breakdown
words into morphemes
Lexical: Assign meanings
to individual words
Syntactic: Check if
sentence is grammatically
correct
Semantic: Performs
disambiguation of words
Discourse: Makes
connection between
sentences
Pragmatic: Look
contextual and situational
meanings
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18. Natural Language Processing Industry Trends
18
• Expect Labs
• SwiftKey
• NetBase
• FiscalNote
• Kelvu
Top Speech Recognition Companies Application Description
NLP in Text
Prediction
Text prediction technology designed to significantly boost the accuracy,
fluency and speed of text entry on mobile and computing devices by
learning from users’ writing style and predict favorite words, phrases
and emojis
NLP in Social
Media Analysis
Software to the data from the social web to apply social media
sentiment analysis using NLP technologies
NLP in Predicting
Government
Legislation
Product for analyzing political, legal, and regulatory information using
NLP and machine learning
NLP in
eCommerce
Analysis
System to help merchants improve the shopping experience on their
site, and increase conversions and revenue

20. Computer Vision
20
Technology through which a computer can see by extracting, analyzing, and comprehending useful information from a
single image or an array of images through algorithms
Optical Character Recognition
convert image into editable text
Estimating Position –
position of tumor in the body
Object Recognition –
Parking number plate recognition.
Facial recognition
expression based customer sentiment
Devices Required:
• Camera
• Processer
• Software
• Display for monitoring
Benefits
 No limitation like as human
perception
 Easy to work with the
devices (mount, remove,
replace and upgrade)
The Goal of CV is to emulate the striking perceptual
capability of human eyes and brains or even to surpass
and assist humans
The other vision systems are:
• Image Processing
• Machine Vision

21. Computer Vision Industry Trends
21
FB: New computer vision algorithms can “read” images and videos to the blind and display over 2 billion translated
stories every day
• Cognex
• Datalogic
• IVISYS
• Microscan
• National Instruments
• Optotune
• ProPhotnix
• Sensory
• USS Vision
• ViDi Systems
Application Description
Object Tracking in
video
Use the color of an object to track its trajectory as it moves in the video.
Plant classification Color histograms and machine learning to classify the plants
Image search Feature-based learning and recognition algorithm to re-rank the outputs from a
traditional keyword-based image search engine
Sharpening, blur,
and noise removal
Image processing for the enhancement of images through the use of sharpening
and noise removal operations
Facial animation A parameterized 3D model of shape and appearance (surface texture) can be
used directly to track a person’s facial motions and to animate a
different character with these same motions and expressions
Top Computer Vision Companies

23. Robotics
23
Robots are aimed at manipulating the objects by perceiving, picking, moving, modifying the physical properties of object,
destroying it, or to have an effect thereby freeing manpower from doing repetitive functions without getting bored, distracted,
or exhausted.
 Sensors (vision and tactile sensors)
 Actuators
 Electric, piezo and ultrasonic motors
 Pneumatic air muscles
 Muscle wires
Mechanical
construction
Electrical
Components
Computer
Program
Form, or shape designed to
accomplish a particular task
Power and control the
machinery
Determine what, when and
how a robot does something
Legged Wheeled Legged & Wheeled Track Skip/Slid SwimmingDronesStationary

24. Robotics Industry Trends
24
• Alphabet, Inc. (Google)
• Amazon
• iRobot
• Lockheed Martin
• Samsung
• Toyota
• Foxxconn Technology Group
• ABB Robotics
• EPSON Robotics
• FANUC Robotics
• KUKA Robotics
• Rethink Robotics
• Yamaha Robotic
• Yaskawa Robotics,
Top Robotic Companies
by Robotics Business Review
Industry Application
Industrial Robots are used for handling material, cutting, welding, color
coating, drilling, polishing, etc.
Military Autonomous robots can reach inaccessible and hazardous zones
during war.
Healthcare Robots are capable of carrying out hundreds of clinical tests
simultaneously, rehabilitating permanently disabled people, and
performing complex surgeries such as brain tumors.
Exploration the robot rock climbers used for space exploration, underwater
drones used for ocean exploration are to name a few.
Entertainment Disney’s engineers have created hundreds of robots for movie
making
Automotive Autonomous or self driving car are now becoming a reality

26. Industries that are most impacted due to AI
• Dynamic & data intensive markets
• Financial adviser dependent
• Customer service support
• Security/privacy/fraud sensitive
• Highly regulated
• Innovators from lab to real-world
• Venturing into diverse industries
• Nurture innovation and skills
• Investing in startups & incubators
• Personalize user experience
• Highly dependent on underwriters
& agents
• Clients preference for self-service
• Data driven claims & settlements
• Increase in personal & commercial
IoT devices
• Self-driving cars
• Shift from ‘owned’ to ‘rent a service’
• Usage based Auto-insurance
• Personalized driver assist features
• Increase in IoT & Telematics
• Preventive & early diagnosis
• Personal care for old & disabled
• Shortage of nurses & surgeons
• Large clinical data repository
• Too many medical publications
• Health & fitness tracker data
• Simple, routine & heavy lifting
work to reduce production time
• Work in danger zones
• Supply chain & inventory mgmt..
• Equipment & fleet monitoring
• On-demand delivery
• Online reviews influence on
shoppers
• Personalized shopping experience
• Cross product recommendations
• Marketing campaigns, offers &
discounts
Disruptive business are shifting their
focus from Information Technology to
AI-Powered Technology. IT/ITES
companies that already have huge
tech talent needs to focus on early
adoption of AI Technology by:
 Launching interactive AI courses to
train their domain SMEs
 Nurturing in-house innovation
though hackathons
 Investing in right AI tech start-ups
 Collaborating with Universities for
R&D in AI & emerging technologies
Technology Giants Insurance AutomotiveBFSi
Healthcare Retail & e-commerce IT/ITESManufacturing & Logistics
Intelligent search engine; Personalized
recommendations & newsfeeds; voice
powered assistant & chatbots; AI
platforms & products
Automated financial advisors &
brokers; Chatbots; AI user
authentication; Smart wallets; Credit
scoring models; Intuitive intelligence
for fraud prevention
Robo-underwriters & Robo-agents;
Automated underwriting & pricing
models; Sophisticated NLP & vision
systems algorithms for settlements
Robo-taxi; Connected cars; Voice
powered assistant; AI vision systems
& NLP models for accident prevention
& remote monitoring
Robot assisted surgeries & patient
care & monitoring; Designing
treatment plans; Online consultation;
Prediction models for drug creation
Robots for assembling & packaging;
drones for equipment monitoring &
ML for predictive maintenance;
Collaborative warehouse robots
Intelligent recommendations engine;
Chatbots, Voice & AR/VR based
shopping experience; customer
sentiment; AI models for store
operations; Drone warehouse
InfluencersDisruptionsInfluencersDisruptions
26
US Federal Report says Artificial Intelligence could automate 47% of jobs

27. A few Disruptive AI Innovations
27
Amazon - “People who bought this also bought…”, “recommended for you” on the other sites
Facebook – News feed, “Add a Friend”
Google - Google Translate, Search Engine
Gaming - The world’s best Checkers, Chess, Scrabble, Backgammon, and Othello players
Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit partnership to formulate best practices on artificial
intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence.

29. Which Artificial Intelligence is our Future?
29
Artificial Narrow Intelligence
Some real-world issues
Unemployment due to skill replacement
through automation
2010 Flash Crash – AI failure let to stock
market brief plummet, taking $1 trillion of
market value
To avoid legal battle Amazon Echo hands
over data to police in a murder case.
Apple & FBI battle over unlocking
terrorist’s FBI
ANI
AGI
Artificial General Intelligence
Human Level Machine Intelligence – a
machine that can perform any intellectual
task that a human being can
Ex: A machine that can create another
machine, build sky scrappers etc.
Threat to Privacy, Safety, Human Dignity
and potential devaluation to humanity by
artificial moral agents
ASI
Artificial Super Intelligence
The Law of Accelerating Returns
Technological Singularity -
Superintelligence will abruptly trigger
runaway technological growth, resulting
in unfathomable changes to human
civilization to extent that human
existence is under question.
We are Here!
Self-improvement of
Algorithms, content &
Hardware
Recursive self-
improvement causing
Intelligence explosion

30. 30
Thank
You