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4 Ways AI Can Help Your Small Business

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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.

Published in: Data & Analytics
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4 Ways AI Can Help Your Small Business

  1. 1. 4 Ways to Enhance Your Business With AI Keita Broadwater August 27, 2017 18th TACC-NC Business Workshop
  2. 2. Objectives • Understand Artificial Intelligence (AI) and Machine Learning (ML) • Learn Pre-requisites for Machine Learning • Understand Practical Applications for your Business
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 14. What is a Chatbot? A machine that can: • Give coherent and meaningful answers • Conduct a conversation with a human
  15. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 25. Thank You! keita.broadwater@gmail.com
  26. 26. Content Generation: Example
  27. 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

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