AI Primer

Host: Leila Toplic, Lead for Emerging Technologies,

NetHope

Aubra Anthony, Strategy & Research Lead, Center

for Digital Development, USAID

Cyrill Glockner, Principal Program Manager

Business AI, Microsoft

August 27Webinar: AI Primer

• What is AI, ML, Big Data?

• What are the types of ML?

• What are key AI

capabilities?

• What is the process for

developing and using

AI/ML?

Sept 19Webinar: Practical

Implementations in

Conservation Context• Protection Assistant for

Wildlife Security –

Carnegie Mellon

University

• Mapping Ocean Wealth –

The Nature Conservancy

Oct 21• Session: Ethical AI

(90min, NetHope

members only,

partners by

invitation only)

• Session: Demos of

AI/ML tools &

services

Oct 23• Session: Practical

Implementations

(The Carter Center,

Plan International)

• Session: Learn how

to evaluate AI for

your programs

(hands-on session).

Pre-Summit NetHope Summit 2019 Post-Summit

Package up content

from Summit

sessions and make it

available via

webinars and

Solution Center

Ongoing:

AI Working Group (NetHope members only; register: http://bit.ly/ET_WorkingGroup)

May, 2019

May 3, 2019AI for Good

Workshop at ICT4D

Conference

Kampala, Uganda

May 31, 2019AI for Good

Workshop at the AI for Good Global Summit at the UN in

Geneva

AI for Good: Recent and upcoming opportunities to learn & share

JanuaryAI Working Group

established as part of

the Emerging

Technologies

initiative.

January 2019

Designed to help nonprofit sector know what questions to

ask when evaluating and incorporating AI into their work.

Framework includes questions for each stage:

• Defining the opportunity

• Evaluating data and bias

• Resourcing the solution

• Implementing the solution

• Maintaining and extending the solution

Framework:

http://bit.ly/AI4Gframework

AI/ML tools, services, training:

http://bit.ly/AI4Gtools

Resources:AI for Good Framework and a Repository of Tools & Resources

What is AI, ML, Big Data?

Today: AI Primer

What are the key AI capabilities?

What are the types of ML?

What is the process for developing and using AI/ML?

What is AI, ML, Big Data?

Today: AI Primer

What are the key AI capabilities?

What are the types of ML?

What is the process for developing and using AI/ML?

Terminology

Machine Learning (ML) is a set of methods for getting computers to recognize patterns in data and use these patterns to make future predictions. For shorthand, you could think of ML as “data-driven predictions.”

Artificial Intelligence (AI) uses computers for automated decision-making that is meant to mimic human-like intelligence. Automated decisions might be directly implemented (e.g., in robotics) or suggested to a human decision-maker (e.g., product recommendations in online shopping); the most important thing for our purpose is that some decision process is being automated. For shorthand, you can think of AI as “smart automation.”

Big Data: A set of technologies developed to handle data sources that are “big” in

terms of volume, velocity, or variety. While the term “Big Data” emphasizes data

management more than learning and predictions, many former Big Data companies

have rebranded themselves as AI companies, and there is broad overlap in tools and

techniques.

What is AI, ML, Big Data?

Today: AI Primer

What are the key AI capabilities?

What are the types of ML?

What is the process for developing and using AI/ML?

Types of Machine Learning

9

Supervised learning: Given a set of labeled training data, learn to predict labels for unlabeled data.

Estimate the probability of loan repayment based on financial data from past borrowers.

Unsupervised learning: Find patterns or structure in a dataset

Determine whether potential borrowers comprise several distinct groups, for which different loan products could be designed.

Reinforcement learning: Reward-based training system, maximizing its chances of achieving a well-defined goal

Currently most useful for robotics and autonomous vehicles (and Go)

Deep Learning

Deep learning is part of a broader family of machine learning methods based on artificial neural networks.

Artificial neural networks (ANN) are computing systems that are inspired by, but not identical to, biological neural networks.

They can be trained to match inputs to specific outputs by adjusting parameters within the neural net

Can be used for supervised, unsupervised, or reinforcement ML.

(wikipedia)

What is AI, ML, Big Data?

Today: AI Primer

What are the key AI capabilities?

What are the types of ML?

What is the process for developing and using AI/ML?

Key AI capabilities

Natural language processing (NLP)analyzes or synthesizes “natural” human languages such as English, Spanish, or Arabic.

Computer vision processes images or video in order to identify objects or interpret scenes or events.

MIT, Harvard: Identifying Infection in surgical scars

Key AI capabilities (...continued)

Speech or audio recognition analyzes audio files to recognize specific sounds or speech patterns. Speech recognition often relies on NLP to transcribe speech into written text.

Advanced Analytics carries out sophisticated analysis of multiple data sources, structures.

Content Generation creates new text, images, video from understanding of key patterns in training text, images, video.

Rainforest Connection: Detecting Illegal Deforestation via monitoring chainsaw sounds

Practical Examples: Chatbots

Users request information from a system, often using written or spoken queries.

Key capabilities: Natural language processing, speech recognition, conversational interfaces (chatbots)

Application areas: Health, Agriculture, Financial inclusion

Farm.ink, Fall

Armyworm Tech Prize

winnerPlan Internaitonal’s TESSA

chatbot

Personal targeting/screening

Uses information about people to decide to whom resources should be allocated. Typically used by a small group of decision-makers.

Key technologies: Natural language processing, advanced analytics

Application areas: Employment, credit scoring, law enforcement, health

Harambee - Matching at-risk youth with

entry-level jobs.http://harambee.co.za/

FarmDrive - Alternative credit scoring for

smallholder farmershttps://farmdrive.co.ke/

Humanitarian response

Uses satellite imagery and other geographic information (e.g. geotagged user data) to decide whereresources should be prioritized for allocation. Typically used by a small group of decision-makers.

Key technologies: Computer vision, geospatial analysis, social media analytics

Application areas: Humanitarian assistance, disaster response, law enforcement, policy planning

Artificial Intelligence for Digital Response

(AIDR)

Vision + Audio diagnostics

1

7

Analyzes images from cell phone cameras to diagnose disease in humans or plants.

Key technologies: Computer vision, speech or audio recognition, NLP

Application areas: Agriculture, Health

Also some recent research on diagnosing mechanical malfunction (e.g., in cars or factories) or logging of rainforests using cell phone audio.

Diagnosing plant diseases

with Plantix.

Cataract screening in Kenya with the Peek apphttps://www.medicalnewstoday.com/articles/264905.php

https://www.customvision.ai

Demo: Image Classifier

Build your own computer vision model using an off-the-shelf tool.

What to keep in mind

• Garbage in / Garbage out• Machine Learning is not magic, it will only deliver results as good as the data you are using for

your models.

• Large data sets are required for high accuracy • Don’t underestimate the number of samples needed for training a model. Labeled data is not

readily available in many development contexts.

• Feature extraction happens automatically• Keep in mind that in supervised learning, model will make decisions about the features of an

image in order to identify what it is.

• Explainability remains an unresolved issue • Some progress has been made to allow developers to understand and debug their models.

• Suitability of Machine Learning for your problem• Machine Learning can do some things really well if there is a correct answer. If it requires

“common sense” or background knowledge, Machine Learning might not be the best solution.

Other real-world applications

Health

• Image-based diagnostics (Parasight)

• Tailored behavior change messaging (Surgo Foundation)

• Identifying mosquitoes from photos or audio

• Disease surveillance (USAID Zika Grand Challenges)

Finance

• Alternative credit scoring and personalized financial services (Branch, Tala, USAID/DIV-LenddoEFL)

Agriculture

• Prediction of crop yields from satellite imagery and climate data (USAID/BFS + CIAT)

• Agricultural input loans (USAID/DIV -Apollo, FarmDrive)

Humanitarian response

• Earthquake early warning system (USAID/DIV -Grillo)

• Situational awareness from social media (AIDR)

Conservation

• Protection Assistant for Wildlife Security (PAWS)helps park rangers design effective patrol routes to prevent poaching of wildlife. (CMU)

What is AI, ML, Big Data?

Today: AI Primer

What are the key AI capabilities?

What are the types of ML?

What is the process for developing and using AI/ML?

Developing and Using AI+ML: It’s a process

Example: Using computer vision to identify malaria in blood smear image

24

Red blood cell

White blood cell

Red blood cell infected

with malaria parasite

A closer look at data review

Labeling Image Data Cleaning Data

Removing images that are:

● out of focus

● poor lighting

● distracting background conditions

● are of cats

Image Label

no parasite

parasite

no parasite

no parasite

A closer look at model-building: Features

Features are the data elements that describe things in your dataset. In computer vision applications, these can be organized into a hierarchy:

Objects

Shapes

Textures

Edges and corners

Colors

Pixel values

For other types of data, features might be easy-to-interpret descriptors (gender, income, birth date, etc.)

High

level

Low

level

A closer look at model building: Evaluation

False positives: Detecting a malaria parasite in cells that do not actually contain one.

False negatives: Failing to detect a malaria parasite in cells that do actually contain one.

Which definition of “accuracy” is appropriate depends on context:

What are the consequences of being wrong?

Are you looking for something rare or something common?

Image Predicted Label

Evaluation

Parasite True Positive

No parasite True Negative

Parasite False Positive

No parasite False Negative

A closer look at integrating into practice...

Estimating cost of errors

• What is the cost of missing potential malaria infections? (false negatives)

• What is the cost of unnecessary confirmatory testing/treating people who don’t have malaria? (false positives)

• How accurate does model need to be to actually improve treatment?

Establishing proximity to final decisions

• Should this be used as a screening test or final diagnosis?

• Should it be used to assist a community health worker in referral for a confirmatory test, or the basis on which they administer treatment?

Child receiving artemisinin

combination therapy (ACT).

Image credit: Bonnie Gillespie

Ongoing:

AI Working Group (NetHope members only)

Webinars, NetHope Summit, ResourcesRESOURCES:

Framework:

http://bit.ly/AI4Gframework

AI/ML tools, services, training:

http://bit.ly/AI4Gtools

Emerging Tech:

https://solutionscenter.nethope.org/et

Let us know what else you would like to learn about AI or share with the NetHope community.

Email: leila.toplic@nethope.org

Sept 19Webinar: Practical

Implementations in

Conservation Context• Protection Assistant for

Wildlife Security –

Carnegie Mellon

University

• Mapping Ocean Wealth –

The Nature Conservancy

Oct 23• Session: Practical

Implementations

(The Carter Center,

Plan International)

• Session: Learn how

to evaluate AI for

your programs

(hands-on session).

Oct 21• Session: Ethical AI

(90min, NetHope

members only,

partners by

invitation only)

• Session: Demos of

AI/ML tools &

services

Pre-Summit NetHope Summit Post-Summit

Package up content

from Summit sessions

and make it available

via webinars and

Solution Center

Q&AHost: Leila Toplic, Lead for Emerging Technologies,

NetHope; leila.toplic@nethope.org

Aubra Anthony, Strategy & Research Lead, Center for

Digital Development, USAID; aanthony@usaid.gov

Cyrill Glockner, Principal Program Manager Business AI,

Microsoft; cyrillg@microsoft.com