The document discusses artificial intelligence (AI) and machine learning, providing an overview of key concepts and a demonstration of an AI-powered job recommendation engine. It notes that global business value from AI is projected to reach $1.6 trillion by 2022. The document outlines different types of machine learning, factors to consider in developing an AI strategy, and suitable toolsets. It then demonstrates a job recommendation engine built using Spark MLib, Prediction IO, and a web application to deliver AI-powered recommendations to job seekers.

1. AI Powered Recommendation Engine
Host & Speaker: Praveen Kumar
Speaker: Ankur Gupta

2. As per Gartner, Global business value derived
from artificial intelligence (AI) is projected to total
$1.6 trillion in 2022
2017 2018 2019 2020
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20
30
40
50
60
70
2021 2022
Artificial Intelligence – To begin with….
Just to Start….
 The term AI was invented in 1950s. An acceleration lately because
of two main factors.
• Exponential growth in computational power
• Exponential growth in available data
Some real-life examples….
 Smartphones with spell-checks
 Siri, Google Assistant, Alexa
 New iPhone X is all about ML / face-recognition

3. How “real” is AI for my business?
Some of the business questions……
 Can I forecast better with AI?
 Can my warehouse operations be more efficient?
 How will it help my sales team close more deals than ever?
 Will AI powered Chat BOT reduce my customer services cost?
Some of the implementation questions….
 How do we start? Do I have AI skills?
 How my existing IT/application infrastructure will be used? What is right AI tool-set?
 How much automation and intelligence can be achieved, and what is the ROI?
 Do I have required data or data-set for AI to be effective?

4. How “real” is AI for my business?
Some of the business questions……
 Can I forecast better with AI?
 Can my warehouse operations be more efficient?
 How will it help my sales team close more deals than ever?
 Will AI powered Chat BOT reduce my customer services cost?
Some of the implementation questions….
 How do we start? Do I have AI skills?
 How my existing IT/application infrastructure will be used? What is right AI tool-set?
 How much automation and intelligence can be achieved, and what is the ROI?
 Do I have required data or data-set for AI to be effective?
Of course!!! But to answer “BY
WHEN”, more digging is required
Organizational Change
Management is the other 50%

5. How to start AI journey? – The 5 Pillars
General Managers
General managers to be educated covering the usage and benefits of AI. “trust” needs to be there
that machine will solve a problem. They become the translators or promotors.
Employees
Employees to be motivated for adapting innovative technologies like AI as they will be the drivers
Skills & Partners
Partners to facilitate the road-map planning, first few POCs/Pilots and work with GMs and
Employees for implementation, Growing AI skill base
Data
Need sufficient data not “huge amount of data” and ML will learn as the data set improves
Environment & Algorithms
Available by Google, Amazon, Microsoft, Salesforce and many other companies via cloud
Take the Lab approach
Work with Partners
Get business involved
Prepare to Fail Fast
Realize the VALUE and
Accelerate

6. Artificial Intelligence
AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer
systems. These machines are programmed to "think" like a human and mimic the way a person acts.
Machine Learning
Machine learning is a category within the larger field of artificial intelligence that is
concerned with conferring upon machines the ability to “learn.”
Deep Learning
Deep learning is a subset of machine learning, uses
neural networks
About AI, ML and DL

7. Machine Learning
Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning
Continuous
Target Variable
Categorical
Target Variable
Target variable
not available
Regression
Stock price
prediction
Classification
Medical
Imaging
Clustering Association Classification Clustering Control
Customer
Segmentation
Market
Analysis
Text
Classification
Lane Finding
on GPS data Driverless Cars
Target variable not available Categorical Target Variable
Types of ML

8. Supervised Learning
Natural-language
processing
Unsupervised
Learning
AI Tool-set
The Drivers…..
 Area of business operations
 Existing & Future technology Landscape
 Current skill-set / skill-level of the team
 Operational strategy – Open Source or COTS
Classification Regression Clustering Association
• Apache OpenNLP
• Gate and Apache UIMA
• Natural Language Toolkit
• Stanford's Core NLP Suite
• ScalaNLP
• MALLET
• Spark MLib
• Scikit-learn
• Accord Framework
• Tensor Flow
• Pytorch
• PredectionIO
• Keras
• Caffe
• JSAT
• Python
• Java
• R
• C++
• C
• Go
• JavaScript
• Scala
• Julia

9. Artificial intelligence based jobs recommendations for job
seekers, based on their competencies and other parameters.
The demo will cover the followings….
 Spark MLib framework
 Prediction IO as AI middleware platform
 Web application to receive recommendations from AI engine
AI Powered Recommendation Engine (Web and Mobile App)

10. AI Powered Recommendation Engine Demo (Cont.)

11. AI Powered Recommendation Engine Demo (Cont.)
Create Models Apply weights
Training Models
Apply algorithms and calculate result

12. What all did we cover…
How “real” is AI for Businesses?
Types of machine learning
How to establish AI strategy?
How to choose the right AI toolset?
Demonstration of AI-powered recommendation engine

13. Q & A

14. Please send in your queries at marketing@jktech.com
Thank You!