20NTC - Getting Started with Data Science and AI in the Non-Profit Sector - via Keela Plugged In
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Ria Sankar is the Director of Program Management for Microsoft's AI for Good programs. This webinar covers her talk on "Getting Started with Data Science and AI in the Non-Profit Sector" that was supported to be offered during 2020 NTEN Conference in Baltimore (20NTC) and recorded via Keela's Plugged In platform due to COVID-19.
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20NTC - Getting Started with Data Science and AI in the Non-Profit Sector - via Keela Plugged In
- 1. AI for Good Research Lab Ria Sankar Director of Program Management Getting started with Data Science and AI linkedin.com/in/riasankar/
- 2. Microsoft Confidential 2 Microsoft Artificial Intelligence for Good AI for Humanitarian ActionAI for AccessibilityAI for Earth
- 3. Microsoft Confidential 3 Microsoft Artificial Intelligence for Humanitarian Action Human rightsNeeds of children Refugees & displaced peopleDisaster relief
- 4. Microsoft Confidential 4 Disaster Relief Open Street Map Street and Building Recognition Automated Needs Analysis Veterans aiding Hurricane Relief
- 5. Microsoft Confidential 5 Needs of children Genomics Database Malnutrition Psychological Poverty
- 6. Microsoft Confidential 6 Refugees & displaced people Deep learning for conflict analysis ChatbotsAutomated telephony & systems
- 7. Microsoft Confidential 7 Human rights Global Emancipation Network
- 8. Microsoft Confidential 8Microsoft Confidential 8AI for Good Research Lab 5 lessons learned along the way..
- 9. Microsoft Confidential 9 Lesson #1: Deep customer empathy brings transformation OXO Good Grips revolutionized an industry that has been unchanged for decades Microsoft Confidential 9
- 10. Microsoft Confidential 10 Lesson #2: The Law of Unintended Consequences Remedy: Instrument for leading indicators of failure Microsoft Confidential 10
- 11. Microsoft Confidential 11 Lesson #3: The Placebo effect The importance of hypothesis-driven testing when you are up against strong beliefs Microsoft Confidential 11AI for Good Research Lab Source: Wikimedia Commons Further Viewing: https://www.ted.com/talks/ben_goldacre_battling_bad_science?language=en#t-839994 Picture Source: https://upload.wikimedia.org/wikipedia/commons/5/55/VariousPills.jpg
- 12. Microsoft Confidential 12 Lesson #4: Spurious Correlations Be careful with visuals that imply causation. Supplement data with insights. Microsoft Confidential 12 Sources: http://bama.ua.edu https://www.tylervigen.com/spurious-correlations
- 13. Microsoft Confidential 13 Lesson #5: Beware of Bias Over 150 types of bias like self- selection bias shown here. Remedy: Question your wildest insights. If it’s too good to be true, it’s probably not. Microsoft Confidential 13
- 14. Microsoft Confidential 14Microsoft Confidential 14AI for Good Research Lab 5 easy steps to infuse data and AI in your programs..
- 15. Microsoft Confidential 15 Step 1 Diverse Hiring Focus on diversity of experience, skills & education
- 16. Microsoft Confidential 16 Diversity ensures ethical use of AI: FATE principles https://www.microsoft.com/en-us/ai/responsible-ai
- 17. Microsoft Confidential 17 Understand cause, effect, and response.
- 18. Microsoft Confidential 18 Step 2 Customer Understanding Understand your customer’s functional, social and emotional needs Functional “Find the best volunteers for disaster response” Social “Give volunteers a way to connect with like-minded friends” Emotional “Instill in veterans a new mission and sense of purpose”
- 19. Microsoft Confidential 20 Qual & Quant effective in understanding new or sparse use cases Further reading https://www.linkedin.com/pulse/user-research-turning-data-delightful-experiences-ria-sankar/
- 20. Microsoft Confidential 22 Step 3 Program Definition Define your problem in terms of key hypotheses and outcomes
- 21. Microsoft Confidential 23 Identify specific, measurable KPIs. Find your baseline. EffectiveSalesperson Score on “product knowledge” test Duration between open/closed logged calls NSAT – Partner satisfaction survey Result Metrics Beliefs Frequency General Specific
- 22. Microsoft Confidential 24 Step 4 Data Literacy Define your measurement system and data quality parameters.Business Problem Data Audience Insight
- 23. Microsoft Confidential 25 Lifecycle of a Belief Belief Quick Hypothesis Test No Change Machine Learning & Experiments No Change Significant Difference? Ship! Significant Difference? Offline metrics, Surveys, Sample data, Panel Studies Machine learning, Deep Learning, AB or Multi-variate tests Customer Interaction, Problem Definition, Data Collection Labelled Data
- 24. Microsoft Confidential 26 Define your measurement system and data quality parameters. Data Ingestion Data Collection Data Labelling Data Access Data Insights
- 25. Microsoft Confidential 27 MAP CHART Graphical Analysis 101 Graphs provide practical significance. Visuals help isolate data issues or define key outcomes
- 26. Microsoft Confidential 28 Hypothesis Testing 101 Source: https://sixsigmadsi.com/hypothesis-testing-cheat-sheet
- 27. Microsoft Confidential 29 https://medium.com/@davidbessis/building-ai-first-products-90d503ccd43a
- 33. Microsoft Confidential 35 Typical AI Models • Supervised vs. Unsupervised • Batch vs. Online • Classification vs. Regression • Generalized vs. Discriminative • Instance-based vs. Model-based Ensemble Methods Bagging and Boosting (rely on PhDs) https://azure.microsoft.com/en-us/blog/announcing-automated-ml-capability-in-azure-machine-learning/?WT.mc_id=azure-presentation-lazzeri Other Resources: https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/algorithm-cheat-sheet/machine-learning-algorithm-cheat-sheet-small_v_0_6-01.png
- 34. Microsoft Confidential 36 Classifying cheese into Brie, Mexican, Parmesan, Mozzarella What model will you choose?
- 35. Microsoft Confidential 37 What model will you choose? Analyzing weather patterns to uncover trends
- 36. Microsoft Confidential 38 What model will you choose? Analyzing weather patterns to FORECAST the next 10 days as Cloudy, Windy, Rainy or Sunny
- 37. Microsoft Confidential 39 What model will you choose? To find anomalies in your dataset? For spam, fraud filtration?
- 38. Microsoft Confidential 40 Typical AI for Good Solutions Chatbots Optical Character Recognition (OCR) Matching Algorithms Recommender Systems Direct Data Analysis Image Processing Acoustic Detection Deep Learning NLP Deep Learning Object Detection Fraud and Outlier Detection Genomics analysis
- 39. Microsoft Confidential 41 Step 5 Establish Urgency The 3 pillars of a powerful story Authenticity Insight Purpose
- 41. Microsoft Confidential 43 “” Data is the real currency of your company — Satya Nadella “
- 42. AI for Good Research Lab Thank you. Microsoft Confidential linkedin.com/in/riasankar/