Eps 1469: How ai can help tackle poverty
— The too lazy to register an account podcast
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| Content creation: | GPT-3.5, |
Host
Hugh Kuhn
Podcast Content
The World Economic Forum and the Government of India are collaborating to identify promising use cases for AI in agriculture that could pave the way to strengthen food systems around the world. To help realize these opportunities, the World Economic Forum recently partnered with the Government of India and the state of Telangana to identify valuable use cases for AI in agriculture, develop innovative AI solutions, and ensure their widespread adoption. The researchers also highlight the need for more coordination and investment in AI, and say real applications are still lacking. In Latin America, scientists, researchers, and entrepreneurs are using AI for a range of health applications: predicting dengue outbreaks, mental disorders, or diagnosing Alzheimer's disease.
If these problems can be solved, AI could play an important role in lifting billions of people out of poverty. We will need to use every tool at our disposal, and as artificial intelligence becomes more powerful every day, we must encourage more innovators and entrepreneurs to focus on new ways to use this technology to solve our biggest societal problems. AI can help us rewrite the global development strategy and enhance the best of humanity. Using AI to fight poverty could help end once and for all what many consider to be the greatest problem facing humanity.
AI can now be used to solve some of the most important social and economic problems of our time. It can also help organisations and aid workers in the field assess the effectiveness of their poverty eradication efforts. To efficiently map poverty, AI can combine high-resolution satellite imagery with powerful machine learning algorithms and predict how rich or poor a given area around the world will be ) for example, farmland).
Researchers at Stanford University combined daytime satellite imagery with powerful machine learning algorithms to predict poverty in Nigeria, Uganda, Tanzania, Rwanda and Malawi Uganda Tanzania Rwanda. A powerful tool developed by Stanford researchers combines free and open-source satellite imagery with artificial intelligence to estimate poverty levels in African villages and how African villages have developed over time. Researchers at Stanford University have created a powerful new tool that can help assess poverty levels in African villages and how their development has changed over time.
For the past four years, the Facebook Data for Good team and UC Berkeley have been working on micro-estimating wealth and poverty in low- and middle-income countries using non-traditional data. To predict where the poor live, the research team is training machine learning algorithms using Demographic and Health Survey data from 56 countries, which provide basic household data on a range of health and economic indicators. In many developed countries, household survey and census data can be used to identify slums. In Asia, researchers are using artificial intelligence and machine learning to combine satellite imagery with household survey data to better understand poverty.
States don’t collect much data, and scaling up traditional household surveys is expensive, and AI can help change that. The QCRI was compiled because many countries, especially developing countries, lack up-to-date data, making it difficult to plan or assess effective poverty reduction measures. In many parts of the world, poverty datasets are outdated or exist only at high levels.
Our original poverty mapping effort, published in 2016, covered five countries using one year of data. The researchers then fed the algorithm data from a household survey and asked it to predict the distribution of poverty in Uganda.
Combining the two sets of images helps the algorithm predict poverty in Rwanda. By combining economic data with geospatial data as input to an AI/ML system, Marshall Burke and his team at Stanford University were able to predict poverty areas with 81-99% accuracy. Marshall Burke, an assistant professor of Earth system science at Stanford University, used satellite imagery of impoverished regions in Africa.
This study examines how artificial intelligence can help better understand and overcome over-indebtedness in high-risk poverty settings. Our goal is to help prevent over-indebtedness and reduce poverty by providing artificial intelligence tools that can better characterize the realities of over-indebted families and based on this characteristic to better assess future risk of over-indebtedness. Our results also contribute to new ways of identifying and characterizing poverty risk at an early stage, allowing for tailored interventions for different profiles of over-indebtedness. Surprisingly, how various risk factors combine to create specific situations of over-indebtedness is a very important issue for the goal of poverty eradication that has received less attention from researchers.
Let's say poverty is caused by lack of education and life skills, as well as lack of food and clean water, which can be a regional systemic phenomenon, natural disasters or wars, or both. Improving food systems is critical to achieving many of the SDGs, but this is just one of many ways AI can help create the more just and sustainable world the SDGs envision.
Once we study this function and evaluate its accuracy, we can apply it to predict poverty in places where there is no underlying hard data. The CBR then uses the available poverty data for the localities in which it exists to learn a function that computes a poverty estimate for the locality based on the feature set. The tool then uses deep learning technology—computer algorithms that are constantly learning to spot patterns—to create a model that analyzes and generates a wealth index, an economic component commonly used by surveyors to measure the wealth of households in developing countries, for free.