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Jeff Harrison – AI and Machine Learning for Nonprofits

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Net2van's annual #NPtech trends meetup.
Where should nonprofits focus their attention in 2019?
https://www.meetup.com/net2van/events/256474659/

Join us for a fast-paced evening of Ignite-style 5 minute mini-presentations on the trends, tools, and techniques charities should explore to create more impact.

FEATURING

+ Rob Cottingham @robcottingham – Trust as the killer app in the Age of Fake

+ Brady Josephson @bradyjosephson –
fundraising on third party platforms: Facebook donations; Third party processing and Donor Advised Funds (Chimp, Google, Facebook, etc.)

+ Kyle Thom, iATS Payments @iatspayments – Video and chat tools to advance your engagement cycle

+ Shoni Field @shonifield – Blurring of the lines between phone and online; Getting serious about digital lead generation

+ Jeff Harrison – AI and machine learning trends for nonprofits

+ Jessica Macleod @jess_macleod, Jelly Marketing @jellymarketing – How to effectively use Instagram Stories (how to make them more engaging, gamification with polls, etc.)

+ Matthew Pattinson, Lean Leap @mtpattinson – Uncovering Big Ideas That Matter With The Lean Canvas.

+ Daryl Hatton, Fundrazr @FundRazr – The market shift from major donations to micro-donations and from macro-projects to micro-projects

+ Ash Kumar, VanHack @AswinKumar @govanhack – Explore micro-communities in 2019. Mass social media is plateau-ing and private communities like Slack and Facebook groups are growing quickly.

+ Ashleigh Turner @sexwithashleigh – INTERACTIVE: what services/online tools you have found to simplify things

SOCIAL LOVE
Hosted by @elivdg
Presented by @net2van
Venue partner HiVE Vancouver @hivevancouver

SPONSORS
@iatspayments @EventChain_io @VCN_Community @techsoupcanada @NTENorg

Published in: Government & Nonprofit
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Jeff Harrison – AI and Machine Learning for Nonprofits

  1. 1. Jeff Harrison Global Insights Manager Greenpeace International 2019 NonProfit Tech Trends AI & Machine Learning
  2. 2. The Who What Why When Where How of A.I.
  3. 3. The Who What Why When Where How of A.I.
  4. 4. M.B.A. Candidate at SFU - Specializing in Technology B.A. in Psychology from UBC Works remotely for Greenpeace International based in Amsterdam. Work history in Fundraising, Marketing, Data Strategy, Organizing and Direct Action Jeff Harrison Global Insights Manager | Greenpeace International
  5. 5. What is it?
  6. 6. What : Specific Abilities Not General Intelligence: Computer Vision Voice to Text Natural Language Processing Translation Prediction Models
  7. 7. What : 2 Big Topics Machine Learning ~ Application of algorithms to produce models that make predictions or classify data, without being explicitly programmed. Natural Language Processing ~ Computer “reading” of written language to “understand” meaning
  8. 8. Magic What : Machine Learning
  9. 9. Donor Quit Donor Quit Donor Stay Donor Quit Donor Stay Donor Stay Donor Stay Donor Stay Donor Stay Donor Quit Donor Stay Donor Quit Input Output Trained Model What : Machine Learning
  10. 10. What : Machine Learning 101110 10 101 10 10 10 01 10 10101 New Observation Prediction Trained Model
  11. 11. What : Natural Language Processing Natural Language Processing ~ Computer “reading” of written language to “understand” meaning
  12. 12. What : Natural Language Processing
  13. 13. What : Natural Language Processing
  14. 14. Why Now?
  15. 15. Why : Machine Learning is old…. 1959- Computer Neural Networks invented 1982- Neural Networks proved to complete “intelligent” tasks And then nothing….
  16. 16. Why : Data Explosion Global data is dramatically increasing with the rise in: ● Smart Phones ● Social Media ● Cloud Storage ...and that was 2012
  17. 17. Why : Increased Processing Power 1999 - the GPU was invented ● 200 times faster that CPU ● Machine Learning application was a discovery 2016 - Google Tensor Processing Units (TPU) ● Computers designed specifically for Machine Learning 2017 - Google/Amazon - Serverless Cloud Computing
  18. 18. Why : 2019 for Non-Profits Clear Value Proposition ● No longer high risk or speculative value Easy to use Developer Kits & APIs ● Check out Google ML - API library Growing Number of ‘Of the Shelf’ Products ● Everybody’s selling AI now. Watch out for snake oil….
  19. 19. Why : 2019 for Non-Profits Companies and organizations continue invest in sophisticated customer centric experiences enabled by AI. Donor expectation around quality, relevance and personalization will continue to grow. Delivering this experience through AI projects will be a critical capability
  20. 20. How?
  21. 21. Intel CEO Brian Krzanich “Data is the new Oil”
  22. 22. Donor Quit Donor Quit Donor Stay Donor Quit Donor Stay Donor Stay Donor Stay Donor Stay Donor Stay Donor Quit Donor Stay Donor Quit Input Output Trained Model How : Back to Basics
  23. 23. Donor Quit Donor Quit Donor Stay Donor Quit Donor Stay Donor Stay Donor Stay Donor Stay Donor Stay Donor Quit Donor Stay Donor Quit Input Output Trained Model How : Back to Basics DATA Use Cases
  24. 24. How : 1) Identify Use Cases with Strong Value Proposition (suggestions coming) 2) Work Backwards - Identify the data set you will need to train models a) Are you currently collecting the right data? 3) Invest in Systems you’ll need a) Do you have data silo’s? b) Data warehousing 4) Get the basics right, before you invest
  25. 25. How : Non-Profit Applications 1) Marketing Communication and Targeting a) No more limited “rules based” selection and systems. 2) Monitoring Social media comments, a) Understand brand affinity, issues/complaints at scale 3) Inbound Communication Management a) Today - Prioritization of incoming social/Email or other communication b) 2019 - AI Augmented Chat c) Eventually - Human-less AI Chat (Changing donation amount, finding information)
  26. 26. How : Case Study - Chat 2022 Prediction ● Donors will expect to interact with companies via Chat Bots ● Attitudes will be more critical to current methods (call in, email) ● “Siri donation $100 to Greenpeace” How do we prepare? ● Implement Human-Chat app now → Collect data ● Develop

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