HacktoberFestPune - DSC MESCOE x DSC PVGCOET

+ +Presented by
Developer Student Clubs
PVGCOET &
MESCOE

Today’s Timeline
● Introduction to Hacktoberfest and Open Source
● Contributions Begin!
● Expert Talk — Mr. Akshay Kulkarni
● Expert Talk — Mr. Ashish Tetali
● Fun and Games
● Closing Ceremony

What isHacktoberfest?
●
● Hacktoberfest is a month-long celebration of open source
software presented by Digital Ocean, Intel, and Dev
Maintainers are invited to guide would-be contributors toward
issues that will help move the project forward, and
contributors get the opportunity to give back to both projects
they like and others they've just discovered

Why contribute to Open Source?
● Give back to the community
● Build new skills and gain experience
● Looks great on your resume and your GitHub profile
● Meet awesome people and network
— Join our Discord discord.gg/NvnYM9 to interact with other
participants.
● Turn your side project into a full-blown open source product
● It’s fun!

What is so special about Hacktoberfest?
● You get a free t-shirt for submitting 4 PRs between October 1st and
October 31st
● T-shirts will be awarded on a first-come, first-serve basis to the first
70,000 participants who successfully complete the Hacktoberfest
challenge
● Receive Certificates from us for submitting 4 PRs between October
11th and October 13th 10 AMIST.
● Please note, low quality/spammy contributions will not count

How do I find a way to contribute?
● You can go to our GitHub accounts for repositories you can
contribute to —
DSC MESCOE bit.ly/dsc-mescoe-github
AND
DSC PVGCOET bit.ly/dsc-pvgcoet-github
Note — Look for Repositories labelled “hacktoberfest” or Issues
labelled “hacktoberfest-accepted”

How do I find a way to contribute?
● We have various repositories in store for you from basic to
intermediate levels of development.
● Topics include:
1. JavaScript and Node
2. HTML/CSS
3. React
4. Flutter
5. ML/AI
6. Python
7. Data Structures and Algorithms

Rules for Hacktoberfest
● PRs must be made on the GitHub platform and merged. Only then, they
will count
● Only PRs count. Issues/commits do not
● All pull requests created between Oct. 1 and Oct. 31 will count, regardless
of when you register for Hacktoberfest
● Pull requests created before Oct. 1 but merged after do not count

Sounds awesome. I’m in. What’s next?
● Step 1
A. Register at bit.ly/Register-HacktoberFestPune if you haven’t
already. VVIMP to receive Certificates
B. Go to hacktoberfest.digitalocean.com and click on “Start Hacking”.
Log In with your GitHubAccount.

DISMANTLING AI
Reconstructing your idea of the AI industry

12
ABOUT THE SPEAKER
Akshay Kulkarni
“Akshay Kulkarni is an Artificial Intelligence and Machine Learning Evangelist and also
he is a Google Developers Expert – Machine Learning, published author, of the books on
NLP and DL and regular speaker at major AI and Data Science conferences including
Strata, O’Reilly AI Conf, GIDS”
https://www.linkedin.com/in/akshay-kulkarni-1a562679/

13
AGENDA
 INTRODUCTION TO THE AI/ML WORLD
 UNLOCKING FEW USE-CASES
 INDUSTRIAL APPLICATIONS

15
HEARD OF THESE ?
Self Driving Car
Churn Analysis
Customer
Segmentation
Spam filter

“AI is a renaissance
to the society”
– Jeff Bezos

“AI is the new electricity”
– Andrew Ng

C O P Y R I G H T S A P I E N T R A Z O R F I S H | C O N F I D E N T I A L 24
Artificial Intelligence

PROBLEM
What is
happening?
Why?
How?
When?

UNLOCKING FEW USECASES
Understand, explore and exploit business and data before arriving at solution
approach and implementation

• Churn prediction is one of the most
popular Big Data use cases in
business. It consists of detecting
customers who are likely to cancel a
subscription to a service
CHURN PREDICTION

• Customer segmentation is the practice
of dividing a customer base into groups of
individuals that are similar in specific ways
so companies can do marketing to each
group effectively and appropriately
CUSTOMER SEGMENTATION

RECOMMENDATION ENGINES
Business Problem
 Personalized product recommendation based on user’s preference
Approach
 Using Machine Learning algorithms ( hybrid recommendation
systems ) on customer purchase data along with third party
information
 Using exhaustive recommendation strategies such as
 Recommended for you
 Frequently bought together
 Recommendation on recent viewed items
 Browsing history
 Related to items you have viewed
 Customer who have bought this also bought
 There is a new version
 Best selling in category
Results
• Nearly 35% of Amazon’s revenue is generated by its
recommendation engine

COMPLAINT E-MAIL CLASSIFICATION
Business Problem
 Roughly 2000 emails to contact center per day
 Requests for refunds, general feedback, website trouble
and more
 Not able to focus on critical problems
Results
• Able to respond to urgent mails 4 times faster
• Saved money on headcount of contact center

PERSONALIZATION
Business Problem
 Anticipate user’s preference and improve user
experience
 Higher degree of personalization and enable higher level
of anticipatory design
 Use Machine Learning to track user’s behavior and
predict next action based on purchase history,
preferences etc.
 Preselect size, color, fixed price etc. in anticipation of
purchase
• User’s delight and simplify their lives

34
SEMANTIC SEARCH ENGINE AND MULTI LANGUAGE SEARCH

SMART SPEAKER READS EMOTIONS TO PLAY MUSIC
Business Problem
 Moodbox is an emotionally intelligent speaker
system that uses artificial intelligence to learn to pick
the best music.
Results
 Moodbox uses its artificial intelligence system to
work out what music users most want to listen to.
 Users interact with the Moodbox by voice control,
and can keep a diary of their moods using the
speaker's smartphone app.

AI LAWYER SPEEDS UP LEGAL RESEARCH
Business Problem
 Answer legal questions
Results
 Ross Intelligence, which is built on IBM's super-
computer Watson, uses natural language processing to
answer legal questions
 Uses natural language processing to answer legal
questions in a fraction of the time that it would take a
legal assistant.
 Ross also monitors the law constantly to keep the user
updated about changes that might affect their case, so
they don't need to sift through the mass of legal news.

CHATBOT - BURGER KING
Business Problem
 Find nearby outlet and take orders through chat
Results
• Not disclosed to public yet

VISUAL RETAIL ANALYTICS & TARGETING
Business Problem
 More than 2000 stores in Great Britain itself
 Use video analytics to monitor cleanliness, compliance and verify
campaign rollouts
Approach
 Used Prism Skylab services to identify customer behavioral patterns
 Create display level heatmaps to identify product interaction
 Pathmaps to understand aggregate customer movement in key
areas of store
 People counting to measure conversion
Results
 Understand customer activity and foot traffic in real time
 Resulted in 19% increase in sales

40
• The aim is to successfully
classify the product
categories with the best
precision
• It is the biggest challenge to
tag a particular product or
SKU to its category
RETAIL PRODUCT CATEGORIZATION

REINFORCEMENT LEARNING : ANSWER FOR YOUR CUSTOMER JOURNEY PROBLEM
Historical states
Current states
Next possible states
Marketing
activity
1. Environment : Customers and
channels
2. Action : Marketing campaign through
any channel
3. Reward : Any metric that quantifies
conversion or towards conversion
4. State : Static and dynamic behavior

DOMAIN/INDUSTRY APPLICATIONS - USECASES

AI IN MARKETING
• Predicting Customer Behavior
• Churn Prediction
• Hyper Targeting
• Content Creation & Curation
• Predicting Lifetime Value (LTV)
• Buying propensity model
• Cross selling/Recommendation algorithms
• Website traffic forecasting - Forecasting
• Sentiment and trend analysis - NLP
• Market basket analysis - APIORI
• Chabot’s - NLP
• Marketing spend optimization / channel –
• Improve Customer Experience (CX) by
predicting user’s next actions and choices
• Using deep learning to identify behavioral
pattern based on camera footage inside and
outside store
• Optimize store operations and layout

AI IN RETAIL / ECOMMERCE
• Product Categorization
• Image recognition and understanding
(product catalog categorization)
• Fraud detection/ Anomaly detection
• Entity Resolution (do these three
accounts belong to the same person?)
• Pricing Optimization
• Demand forecasting (Supply and demand
analysis and forecast)
• Search Ranking
• Query expansion
• Image and text based search engine
• Categorizing emails - For example
complaint
• Credit risk
• Replenishment

AI IN MANUFACTURING
• Supply Chain Management
• Predictive Failure Analysis
• Process Optimization
• Predictive Maintenance
• Image recognition, this can be utilized to
identify (classify) damaged products
• Logistics

AI IN AUTOMOTIVE
• Effective incorporation of analysis –
Product design
• Procurement
• Workforce analytics
• Parts pricing opt
• Operation planning
• Failure patterns to establish relationship
between failures & causes of failure
• Enhancing overall in-vehicle user
experience through personalization
• Prediction of battery life for electric
vehicles

AI IN HEATHCARE
• Patient life cycle in hospital
• Understanding Medical Data
• Outbreak Prediction
• Staff optimization – patients
expected
• Predicting diabetes
• Identifying fraud – insurance
providers
• Medical image analysis

AI IN FINANCE
• Portfolio Management
• Algorithmic Trading
• Fraud Detection
• Loan/Insurance Underwriting
• Personal Recommendations
• Sentiment Analysis
• Security
• Customer Service
• Claims prediction
• Price sensitivity
• Agent & branch performance
• Credit scoring

AI IN TRAVEL
• Aircraft scheduling
• Air crew scheduling
• Dynamic pricing
• Customer complain resolution

AI IN HR
• Attrition Prediction
• Sentiment Analysis
• Hiring Forecasting
• Information Extraction
• Resume Screening
• Chatbot (Internal)
• Learning & Development
• Resource optimization
• Employee feedback analysis
• Employee performance analysis

AI IN OPERATIONS
• Inventory Management
• Sales Forecasting

55
Why NLP is hard?
• Semantics “Gabe invited me to his medical school ball”. What is “ball” in this context?
• Morphology “dog not like eat vegetable”
• the dog did not like to eat vegetables
• the dogs do not like to eat vegetables
• the dogs did not like to eat vegetables
• dogs do not like to eat vegetables
• Ambiguity of intent: “I just got back from New York”. What do they want?
• Situational ambiguity: “Elaina was found by the river head”. Could be by the head of the river (place) or the executive of the river (person)
• Unable to deduce meaning of unknown words from context like humans can
• Disambiguation – “jaguar” can refer to a car or to an animal

Innovations in
Open Source
CollegeStartups.in
https://www.linkedin.com/company/college-startups
ASHISH TETALI
Co-Founder of ARTUS & CollegeStartups.in
ash@CollegeStartups.in
Copyright © 2020 CollegeStartups.in, All rights reserved.

The Spark
Quiz Question: Type in the chat
How old is the concept of Open Source?
DID YOU KNOW?
The concept of Open Source is not new, it is over four (4)
decades old

A little bit about me…
Co-Founder at ARTUS
& CollegeStartups.in
Head of Strategy
& Transformation
Distributed Ledger Tech
(Blockchain)
14+ Global
Patents

About CollegeStartups.in
College Startups is an exclusive community and platform. Our members are passionate about
starting companies, fueling their careers, helping each other achieve their goals, and creating value
in the ecosystem
Our Vision
To build the next generation of leaders who can encourage, teach and support one another and
positively impact the world
Our Mission
Equip students with the resources and tools to help prepare them now and in the future

Search for – CollegeStartups.in
https://www.linkedin.com/company/college-startups
Connect with Us
FREE
STUDENT PASS
www.CollegeStartups.in/membership

Open Source .n, denoting software for
which the original source code is made freely
available and may be redistributed and
modified

Competitive advantage
through Collaboration
Why Open Source?

* Opportunity to innovate
* Be creative & experiment,
* Earn respect from peers
Why should you care?

Open Source as a
Business

* Offered the main product for free, charged users for enterprises if
they wanted additional service & training
* Was considered an inferior model to proprietary software with
lower margins between 50-60%
* Monetizing a fraction of software limits, exclusively in large
markets like OS and databases
Gen 1 – Service, Support, Training

* Core features are open sourced with a license like Apache 2.0, &
enterprise features are offered under a commercial license
* Monetize more effectively then Gen 1 with more value capture in
the enterprise segment despite smaller end markets (e.g. NOSQL)
* Suffered from cloud vendors creating SaaS offering from Core
Apache offering
Gen 2 – Open Core

* SaaS-like experience for developers, while vendors abstract away
complexities from system management
* Enjoy the best of both worlds: widespread adoption of OSS and
business value of proprietary software
* Stronger stance against cloud vendors despite short-term grow
margin hit
Gen 3 – Managed Cloud

* Bottoms-up model drives monetization for free users
* Accessible to companies that don’t have systems
management — Cloud enables faster time-to-value, and
managed products boost customer retention rates.
* Ride the wave of enterprises moving to the cloud
Cloud Monetization is “Natural”

Questions & Conversations

How organizations benefit from
Inner Source

Corporate to Collaborative

Open Community
Transparency
Scalability
Benefits of Inner Source

The next revolution
Expert Source

Open Source
Inner Source
+

Open
Source
Recap
Expert
Source
Inner
Source

HacktoberFestPune - DSC MESCOE x DSC PVGCOET

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