Artificial Intelligence for Goods: Cases and Tools
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This document discusses how artificial intelligence and machine learning can be applied to solve societal challenges. It provides examples of using AI for tasks like clustering news articles, predicting blood donations, detecting wildfires and plant diseases from images, and assisting low-vision individuals. The document also outlines different types of machine learning problems and algorithms like classification, regression, and neural networks. It presents several AI projects and organizations working on social issues.
Artificial Intelligence for Goods: Cases and Tools
- 2. About 1. CEO DevRain, devrain.com. 2. CTO ДонорUA, donor.ua. 3. PhD in Computer Science. 4. Microsoft Regional Director 5. Microsoft AI Most Valuable Professional 6. Open data, Smart City expert. 7. Ex-EGAP Challenge coordinator. 8. The Best Professional in Software Architecture (Ukrainian IT Award).
- 3. Global issues 1. Employment and skills 2. Climate change 3. Food, Energy and Water 4. Health, Sleep, Nutrition 5. Media Biases and Access 6. Corruption 7. People with disabilities 8. Gender Equality 9. Ending Poverty 10. Ageing 11. AIDS 12. Human Rights 13. Saving animals 14. International Law and Justice 15. Migration 16. Oceans and the Law of the Sea 17. Peace and Security 18. Population 19. Refugees 20. Africa 21. Fakes 22. Privacy 23. Quality education 24. Democracy 25. Terrorism 26. Populism Source: United Nations
- 4. How can AI and machine learning be applied to solve some of society's biggest challenges?
- 5. Process 1. Creating dataset (open data, manual, IoT) 2. Understanding data (feature engineering) 3. Model 1. Creation 2. Evaluation 3. Validation 4. Improvement
- 6. Types of problems 1. Clustering 1. Given news articles, cluster into different types of news. 2. Classification 1. Yes/No - binary classification 2. “Is this picture a cat or a dog or a tiger?” - multi-class classification 3. Association analysis 1. If {A, B} then {C} 4. Regression 1. What is the price of house in a specific city?
- 7. CareerVillage.org Nonprofit that crowdsources career advice for underserved youth. Your objective: develop a method to recommend relevant questions to the professionals who are most likely to answer them. https://www.kaggle.com/c/data-science-for-good-careervillage
- 8. DonorUA: predicting if person will donate a blood Features: 1. Recency – months since last donation 2. Frequency – total number of donation 3. Monetary – total blood donated in c.c. 4. Time – months since first donation Full article: http://devrain.com/posts/solving-classification- problems-with-azure-machine-learning-for-blood- donations-prediction
- 9. Natural Language Processing 1. Sentiment analysis 2. Categorization 3. Named Entity Recognition 4. Summarization 5. Key phrases extracting 6. Part-of-Speech 7. Content Analysis
- 10. DonorUA 1. Social media monitoring 2. Donor – recipient match 3. Predicting supply/demand 4. Donor’s portrait http://blog.1991.center/donorua Join our Telegram channel: https://t.me/donorua
- 11. Language Understanding (LUIS.ai) A machine learning-based service to build natural language into apps, bots, and IoT devices. Quickly create enterprise-ready, custom models that continuously improve.
- 13. Word embedding Vector Representations of Words
- 15. Hot dog or not hot dog?
- 16. Fighting fire with machine learning: using TensorFlow to predict wildfires “We decided to develop a device that could identify and predict areas in a forest that are susceptible to wildfires, providing an early warning to fire departments. Using TensorFlow, we can analyze images of biomass and estimating their moisture content and size to determine the amount of dead fuel.” https://www.blog.google/technology/ai/fighting-fire-machine- learning-two-students-use-tensorflow-predict-wildfires/
- 17. AI helps farmers identify diseased plants An example of a diseased cassava leaf. Cassava is a crop that provides for over half a billion people daily. https://www.blog.google/technology/a i/ai-takes-root-helping-farmers- identity-diseased-plants/
- 18. Вирубка лісів Згідно зі статистикою організації Глобального моніторингу лісів, за останні 15 років Україна втратила майже 500 тис. га лісу. З 2015 року, коли у країні був введений мораторій на експорт лісу-сировини, як не парадоксально звучить, показники вирубки лісів масово збільшилися. За оцінками деяки фахівців протягом 2016 року в Україні було вирубано 16,4 млн кубометрів деревини, з них 8-9 млн кубометрів легально. http://texty.org.ua/d/deforestation-longread/ http://texty.org.ua/pg/blog/nartext/read/76201/J ak_big_data_i_drony_mozhut_vratuvaty
- 19. Seeing AI A free app that narrates the world around you. Designed for the low vision community, this research project harnesses the power of AI to describe people, text and objects. https://www.microsoft.com/en- us/seeing-ai
- 20. Wild Me Wild Me used computer vision and deep learning algorithms to create a platform called Wildbook, which scans millions of crowdsourced wildlife images at scale. Wildbook can identify the species as well as the individual animal, and the public can follow the movements of their favorite animals. The aggregated data is used by scientists to help inform conservation decisions. Microsoft is supporting their efforts by hosting Wildbook on Azure and making Wild Me’s open source algorithms available as APIs. https://www.microsoft.com/en-us/ai/ai-for-earth- projects?activetab=pivot1:primaryr3
- 21. Object recognition Analyze images to detect objects, tags, texts etc.
- 22. Emotion recognition Analyze faces to detect a range of feelings.
- 23. Face verification Check the likelihood that two faces belong to the same person.
- 25. Video Indexer Unlock video insights
- 26. Linked Data
- 27. Semantic Web
- 28. Office 365 Graph
- 30. Uber Movement
- 31. Microsoft Research Open Data https://msropendata.com/
- 32. Can AI become an issue?
- 33. Fakes
- 34. GPT-2 GPT-2 generates synthetic text samples in response to the model being primed with an arbitrary input. The model is chameleon-like — it adapts to the style and content of the conditioning text. This allows the user to generate realistic and coherent continuations about a topic of their choosing. https://blog.openai.com/better-language- models/#sample8
- 36. Programs and Organizations • AI for Sustainable Global Development https://ai4good.org/ • Google AI for Social Good https://ai.google/social-good • AI for good with Microsoft Artificial Intelligence https://www.microsoft.com/en-us/ai/ai-for-good
- 37. Join us! • Telegram https://t.me/devraincommunity • Facebook Page https://www.facebook.com/DevRainSolutions/ • Meetup https://www.meetup.com/devrain/