18-03-2022
Are you ready
for the new AI
world?
Laxminarayanan G
PGP25250
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18-03-2022
ARTIFICIAL
INTELLIGENCE
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What differentiates
a machine from a
human?
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What’s Intelligence?
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Intelligence - noun
the ability to acquire and apply knowledge and skills.
"an eminent man of great intelligence"
Intelligence
synonyms:
intellectual/mental capacity, intellect, mind, brain, brains,
brainpower, powers of reasoning, judgement, reason,
reasoning, understanding, comprehension, acumen, wit, sense,
insight, perceptiveness, perception, perspicacious, perspicacity,
penetration, discernment, sharpness, quickness of mind, quickwittedness, smartness, canniness, astuteness, intuition, acuity,
alertness, cleverness, brilliance, aptness, ability, giftedness,
talent; informal braininess
"a man of great intelligence"
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What constitutes Intelligence?
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What’s Artificial
Intelligence?
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CAN YOU GUESS
HOW OLD IS AI?
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Areas explored in Artificial Intelligence
Source: Neota Logic
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AI is everywhere.
It’s in
AI is
everywhere
➢
our phones,
➢
cars,
➢
shopping experiences,
➢
romantic matchmaking,
➢
hospitals,
➢
banks, and
➢
all over the media.
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Guess who she
is?
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WHAT
MAKES AI
SPECIAL
This Photo by Unknown Author is licensed under CC BY-SA-NC
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Let's discuss an
example
Throwing the paper ball into the dust bin
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ML Way
• Now, for the same example a
Machine Learning program
would begin with a generic
formula but after every
attempt/experience refactor it’s
formula.
• As the formula is continuously
improved
using
more
experiences (data points) the
outcome too improved.
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Machine Learning in Layman Terms
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CAN YOU THINK OF A
PROBLEM WHICH
CANNOT BE SOLVED
BY THE TRADITIONAL
APPROACH?
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CREATING
PARADIGM
SHIFT
Artificial Intelligence
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•
Prediction
•
Process of filling in missing information.
Takes information you have, often called “data,” and
uses it to generate information you don’t have.
•
Can be about the present.
•
Prediction Introduction
•
We predict whether
• a current credit card transaction is legitimate or
fraudulent,
• a tumor in a medical image is malignant or benign,
• the person looking into the iPhone camera is the
owner or not.
•
New wave of AI does not actually bring us intelligence
but instead a critical component of intelligence—
prediction.
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Prediction
For example, we are transforming transportation
into a prediction problem.
•
Autonomous vehicles have existed in
controlled environments for over two
decades. They were limited, however, to
places with detailed floor plans such as
factories and warehouses.
•
The floor plans meant engineers could
design their robots to maneuver with basic
“if-then” logical intelligence:
•
However, no one could use those vehicles
on a regular city street. Why?
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Autonomous Cars
Instead of telling the machine what to do in
each circumstance, engineers recognized
they could instead focus on a single
prediction problem:
“What would a human do?”
Now, companies are investing billions of
dollars in training machines to drive
autonomously
in
uncontrolled
environments, even on city streets and
highways.
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How does the
human drive?
•
•
•
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The human drives for millions of
miles
Receiving
data
about
the
environment through their eyes
and ears
Processing that data with their
human brain, and
Acting in response to the incoming
data: drive straight or turn, brake
or accelerate.
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Imagine an AI sitting
in the car with a
human driver.
• Engineers give the AI its own eyes and
ears by outfitting the car with sensors
(e.g., cameras, radar, lasers).
• So, the AI observes the incoming data as
the human drives and simultaneously
observes the human’s actions.
When environmental data comes in,
does the human turn right, brake, or
accelerate?
• The more the AI observes the human, the
better it becomes at predicting the
specific action the driver will take, given
the incoming environmental data. The AI
learns to drive by predicting what a
human driver would do given specific road
conditions.
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IF MODERN AI IS JUST
PREDICTION, THEN WHY
IS THERE SO MUCH
FUSS?
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Prediction is a
foundational input
•
The reason is because prediction is such a
foundational input. You might not realize it, but
predictions are everywhere.
•
Our businesses and our personal lives are
riddled with predictions.
•
Often our predictions are hidden as inputs into
decision making.
•
Better prediction means better information,
which means better decision making.
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How paradigm shift
has happened
•
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Many problems have transformed from
•
algorithmic problems (“what are the
features of a cat?”)
to
•
prediction problems (“does this image with
a missing label have the same features as
the cats I have seen before?”).
Machine learning uses probabilistic models to
solve problems.
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HR as a set of Predictions
• Hiring: Who will do well at our organization?
• Promotion: Who will do the next level job better?
• Retention: Who is likely to leave the organization?
• Compensation: What policies can make them stay?
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My Recommendation
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Analytics –
Data Driven
Organization
Laxminarayanan G
PGP25250
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18-03-2022
Let me start
with this
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The power of Data, Analytics & Visualization
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How analytics is
viewed in industry
Approach of corporates in building their analytics organization
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Approach of corporates in building
their analytics organization
Data Engineering/
Management
Data Visualization
Data Science
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How
organizations
mature in
Analytics space
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Evolution of Analytics in organizations
Evolution of Analytics
Analytics
as R&D silo
Analytics
Aware
19YY – 20YY
⚫
⚫
⚫
⚫
R&D focus in
most industries
20YY-20YY
⚫
Insurance –
credit scoring
Investment
banking –
risk/price
modeling, options
valuation
Oil & Gas –
reserve analysis
and discovery
Actuarial
Models
Analytics
Applied
⚫
⚫
Awareness across
many industries
as investments
flow into
technology
enablers
Insurance – risk
selection and
risk-based pricing
become focus,
especially in auto
insurance
Healthcare –
results tracking,
remote diagnostic
support
Smart Phones
Social Media
Analytics as
a Disruptor
20YY-20YY
⚫
⚫
⚫
Analytics
applications
gaining traction
across industries
and functions
Targeted
functions –
customer
relationships,
supply chain,
finance, risk,
workforce
management
Some companies
establishing
analytics as key
differentiator
Data Scientists
Cloud Computing
Insight
Economy
20YY-20YY+
⚫
⚫
⚫
⚫
“Big Data”
CrowdSourcing
Rapid emergence
of new
competitors and
new offerings
Established
analytics leaders
(e.g., Google)
leveraging their
capabilities to
disrupt industries
Collaborations
between data
owners and
service providers
Use of telematics
data in insurance
with potential
applications in life
insurance
20YY+
⚫
⚫
⚫
⚫
Culture of datadriven decision
making
Integrated data
ecosystems, zero
latency
information flows
and secure data
exchange as the
new normal
Integration of
operational and
behavioral data
Machine-learning
detection of
patterns and
trends
“Internet of Things”
Machine Learning / AI
“Digital
Enterprise”
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Issues to
Outcomes
Confidential
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Envisioned state of a company ☺
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Boardroom of the Future
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