ai primer - nethope...ai primer host: leila toplic, lead for emerging technologies, nethope aubra...
TRANSCRIPT
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: [email protected]
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; [email protected]
Aubra Anthony, Strategy & Research Lead, Center for
Digital Development, USAID; [email protected]
Cyrill Glockner, Principal Program Manager Business AI,
Microsoft; [email protected]