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

1. Jeff Harrison
Global Insights Manager
Greenpeace International
2019
NonProfit Tech Trends
AI & Machine Learning

2. The
Who
What
Why
When
Where
How of A.I.

3. The
Who
What
Why
When
Where
How of A.I.

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. What is it?

6. What :
Specific Abilities Not General Intelligence:
Computer Vision
Voice to Text
Natural Language Processing
Translation
Prediction Models

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. Magic
What : Machine Learning

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. What : Machine Learning
101110 10 101 10
10 10 01 10 10101
New Observation
Prediction
Trained
Model

11. What : Natural Language Processing
Natural Language Processing ~
Computer “reading” of written language to “understand” meaning

12. What : Natural Language Processing

13. What : Natural Language Processing

14. Why Now?

15. Why : Machine Learning is old….
1959- Computer Neural Networks invented
1982- Neural Networks proved to complete “intelligent” tasks
And then nothing….

16. Why : Data Explosion
Global data is dramatically
increasing with the rise in:
● Smart Phones
● Social Media
● Cloud Storage
...and that was 2012

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. 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. 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. How?

21. Intel CEO Brian Krzanich
“Data is the new Oil”

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. 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. 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. 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. 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