A introduction to AI/machine learning focused less on theory and more on the practical applications to an SMB or working professional. Four specific use cases are covered. Talk given to the Taiwanese American Chamber of Commerce in August 2017.

1. 4 Ways to Enhance Your
Business With AI
Keita Broadwater
August 27, 2017
18th TACC-NC Business Workshop

2. Objectives
• Understand Artificial Intelligence (AI) and Machine Learning (ML)
• Learn Pre-requisites for Machine Learning
• Understand Practical Applications for your Business

3. About Me
Operations Leader with Data Science Skillset
15+ years in high tech
Currently:
• CFO of PV Tech, Inc
• Data Science Consultant
• BS, Physics – Florida A&M Univ.
• MS/PhD, Mechanical Engineering – Univ. of Md
• MBA – Cornell Univ.
Loves: Travel, Astronomy, Running, IoT, & Jazz
Email: keita.broadwater@gmail.com
Twitter: @keitabr

4. What is AI & Machine Learning ?
The science of making machines replicate human
intelligence.
Artificial Intelligence
A pillar of AI, where algorithms allow machines to learn
from data.
Machine Learning
One powerful subset of Machine Learning. An Extended
Neural Network.
A Neural Network is an algorithm made to mimic human
brain functions.
Deep Learning
Heavy SW Development Soft Software Development/PaaS Vendors
How is it
Done?
What AI is not:
• Not automation per se
• Not a Set of Rules
- Ground-up development
- Using coding languages (such as
scala, python) and databases
(SQL, no-SQL)
- Lighter SW development
- Takes advantage of existing
packages and platforms (IBM
Watson/Bluemix, AWS)
- Minimal or No coding
- User friendly products that
require various levels of
configuration and input data

5. Examples
1) AlphaGo – 1st computer program to defeat a Go
world champion, Lee Sedol
• Innovative, Surprising Moves & Strategies
• Deep Neural Network
2) Real-time Object Recognition– Using
Tensorflow: Google’s open-source ML library
• Innovative, Surprising Moves & Strategies
• Neural Network
• Trained with images of large variation in
scale, pose, and lighting

6. Personal Example
Lorem Ipsum is
simply dummy
text.
03
Home Security System
Father: 91%
Mother: 9%
Son: 20%
Roomba: 1%
INTRUDER: 2%
Problem: Late Night Intruder
Solution: System that uses footsteps to:
• identify people
• detect intruders
Machine Learning Tool: Support Vector Machine (SVM);
Classification
Data: Sensor Readings

7. 02
03
01
Pre-Requisites for Machine Learning Adoption
Data Collection
01
Security &
Privacy
02
Online
Presence
03
Data Collection
• Size of home grown data
• Use someone else’s data
• Garbage-in  Garbage-out
• “Clean” data
Online Presence
• Website
• Social Media
• Interactivity with Customers
Consideration for Security and Privacy
• If using person-identifiable data
• Anonymize
• Terms-of-Use Agreements
• Notifications

8. Meet Katy: Real Estate Agent
AI
01
• Average 15 Clients/month
• Growing Practice
• Part of Agency
• Sales and Revenue can be Uncertain
Online Presence
• Website
• Posts once a month on Facebook
• Posts Weekly on Weixin
Her Data
• List of Clients
• Basic Demographic, Financial
Details
• Access to MLS Data
• Housing Inventory, Sales History
• Katy’s Transactional History
Some Duties
• Time spent generating leads
• Writing Monthly Newsletter to Clients
• Time Spent Planning of Home Viewings

9. Content Generation: Overview
Value: Productivity; Save Time
Use cases include:
• Financial Updates
• Sales Reports
• Marketing Briefs
• Personalized Reports
Core Technology Involved:
NLG: Natural Language Generation
• Analyze Data
• Interpret Data
• Identify the Most Significant Parts
• Generate Written Reports in Human Language
Data Used To Train Models: Vendor Data
Turn Spreadsheet Data….. …Into Written Reports

10. Content Generation: NLG
Natural Language Generation is about:
- Producing machine written text that
• Has High Quality
• Is Understandable
• Is Easy to Read
Advantages
• Text is the preferred Medium
• Important Information Changes
• Source data is organized
• Variation in the output is required
• Automation is an advantage
Alternatives
• Fixed Templates
• Templates with Variables
• Graphics
Text
Planning
•Convert specific
pieces of data
into discrete
phrases
Sentence
Planning
•Combining the
phrases using
rules of grammar
Linguistic
Realization
•Ensuring that
complete text
makes sense to
the human eye
train_arrival = 20:00:00
“the arrival time is 8PM”

11. Content Generation: Katy
Recent Home Sales
Here is the inventory report for July 2017.
Visit: https://katyliu.com/homes.htm to take a quick look at recent home
sales.
Real Estate Trends
The real estate trends we've seen for the last four years are continuing.
Both San Mateo County and Santa Clara County show average sales prices
are staying higher than list prices.
Price trends are little changed. The peak in price per sq. ft. for the 3rd
quarter of 2017 is based on closed escrows for July sales (not all sales have
closed escrow), and is likely to change a little.
The California Association of Realtors in a July 24 release stated "In C.A.R.'s
newest market indicator of future price appreciation ... indicates that price
growth will continue to accelerate, potentially back into double digit
territory, as it reached its highest level since 2013." (homes are selling
faster than homes are offered for sale)
Advantages for Katy:
• Save time prepping monthly
reports
• Send Personalized Reports
Depending on Preferences of
Client

12. Content Generation: Implementation
Process:
1) Data preparation and upload
2) Editorial Training of the AI
• Create the basic outline of desired reports
• Connect data to specific parts of text
• Prepare Templates and Conditional Formatting
3) Setup automation of data upload and report generation
Company Pricing Website
AX Semantics Starting at $256/user/mth https://cloud.ax-semantics.com/
Arria NLG Unknown, Free Demo http://www.articulatorlite.com/
Yseop Unknown, Free Demo https://savvy.yseop.com/

13. Chatbots: Overview
Value: Handle customers facing tasks 24/7
• Pre-sales
• Customer Support
• Sales
Technology:
NLG: Natural Language Generation
NLP: Natural Language Processing
Data Used to Train Model:
Vendor Data
Customers….. …Interact with Machine Agent …That carries out a task
Place Order
Create Service Ticket
Set Appointment

14. What is a Chatbot?
A machine that can:
• Give coherent and meaningful answers
• Conduct a conversation with a human

15. Customer Chatbot: Katy
Advantages for Katy:
• Multiple customers can be
managed simultaneously
• Customer has access to basic
information on demand
• Customer preferences and
sentiment can be analyzed
Case 1:
1. Customer browses
property on website
2. Chatbot offers more
information
Case 2:
1. Customer is given
chatbot phone
number
2. Customer dialogues
with chatbot on
mobile device when
looking at homes

16. Chatbot: Implementation
Company Pricing Website
Promero Starting at $25/mth, Free Demo https://www.promero.com/sms-webchat-voice-email-bots/
Hutoma 0.0025/API call, Free Demo https://www.hutoma.ai/
Motion Starting at $15/mth, Free Demo https://www.motion.ai
Process
• Create Customer Conversation Flow
• Chosen from Template
• Link to Data
• Link to Platform (facebook messenger,
website, SMS etc)
• Link to other functions
• e.g., API to order from a website

17. Sales Forecasting: Overview
Value:
• Greater Operational Efficiency
• More Accurate Forecasts
• Less time spent in forecasting
Technology:
Time-Series Regression
Data Used to Train Models:
- User’s Historical Data
- Vendor’s Comparative Data
Turn Historical Data….. …Into Forecasts & Predictions

18. Sales Forecasting : Underlying Tech
Time Series Regression:
• Linear Regression of a time series
• Less Flexible Model
• Uses Entire dataset to fit model
• Auto-regressive models (ARIMA)
• More Flexible
• Uses dataset + takes into account
each previous value when making the
fit/forecast

19. Sales Forecasting : Katy
Input: Sales Record of Agent from MLS
Input: Details of Sales Transactions from MLS
Advantages for Katy:
• Speed-up & Enhance Katy’s
Forecasting Process
• Katy can adjust marketing plan
• Katy can adjust expenses
Output: 12 Month Sales Forecast

20. Sales Forecasting : Implementation
Process:
• Have CRM Setup -Or- Have Data in Spreadsheet
• Clean up the data, if necessary
• Upload spreadsheet data
Company Pricing Website
IBM Watson Analytics Free Tier if Data Size is Low https://www.ibm.com/watson-analytics
Salesforce Einstein Multiple Packages/Add Ons https://www.salesforce.com/products/sales-
cloud/features/sales-cloud-einstein/
Sales Temperature $25/location/month http://www.salestemperature.com/

21. Contract Review: Overview
Value: Streamline Contract Reviews; Enhance
Due Diligence Process
• Accurately Highlight Risks and Problem
Areas
• Reduce Legal Costs
AI Technologies:
NLP: Natural Language Processing
Regressive ML Models
Classification ML Models
Data Used to Train Models:
Vendor Data
Upload Contract into System… …Algorithm Analyzes and:
• Identifies Key Clauses
• Identifies Areas of Risk
• Highlights these for Review
• Provide Visualization of
information in Contract

22. Contract Review: Case Study
Master Services Agreements (MSAs): Contacts
between service providers and customers
Contract Negotiation:
• Duration: 3-9 months
• Inspection  Revision  Approvals
• Contracts Size:
• 25-75 Pages
• 10 – 20 Sections
• Key Bottleneck of Process
• Methodical Reading and Manual Parsing
• Prepping Comments for Review Meetings
• Risks
• Overlooking key terms and conditions
• What are the key tasks and duties
• Missing a key stakeholder’s input

23. Document Parsing: Implementation
Company Pricing Website
Beagle ~$100/mth, Free 7 day trial http://beagle.ai/
Kira Unknown; Demo Offered https://kirasystems.com/
Legal Robot Beta Product https://www.legalrobot.com/
Process
• Upload Contracts
• Assist in Training AI
• Identify Areas/Clauses of Concern
• Connect system to stakeholders
• Legal Team(s)
• Business Owners

24. Conclusions
AI & Machine Learning is Practical for Business at all Levels: Small, Medium and Large Enterprise
Benefits:
• Save Time
• Reach Customers
• Reduce Manual Errors
• More insights for Strategy and Planning
Requirement: Having a DataSet
• Size of Data: Enough Cases so that Algorithm Can Learn
• Often Vendors Use their own Data to build their models
• Clean Data:
• Organized in a tabular form
• Erroneous Data taken out
• Missing Data
• Make zero
• Average of surrounding data points

25. Thank You!
keita.broadwater@gmail.com

26. Content Generation: Example

27. Key Machine Learning Tech
(implementations of these can be found on IBM’s BlueMix platform: https://console.bluemix.net/catalog/?category=watson
https://console.bluemix.net/catalog/services/ibm-graph?env_id=ibm:yp:eu-gb
• Classification – Classification is a tool that predicts what group or segment
something belongs to (e.g., given a user data point, a tool can predict whether
the person is male or female; a stay-cationer or world traveler, etc.). This
classification can be used on data, text, images, etc.
• Image Recognition – Tools that can recognize an image by class or individually
• Natural Language Processing (NLP) – Being able to put regular human speech in
a format that is understandable to a machine or software
• Sentiment Analysis – Taking a piece of text and identifying emotional intent,
attitude or sentiment towards something
• Recommendation Systems – Systems that can recommend based on like-users’
preferences
• Clustering – Breaking an unorganized set of data into sub-groups or segments