night light


This intelligent night light is killing it on Kickstarter

Mashable

Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission. Smart home tech is having a moment, and we don't see it slowing down anytime soon. These days we have everything from pizza ordering devices and dog monitoring cameras to robots that can make pancakes. Enter: the smart night light. Zing is an intelligent, AI-powered night light that's currently killing it on Kickstarter -- and for good reason.


Satellite images used to predict poverty - BBC News

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Researchers have combined satellite imagery with AI to predict areas of poverty across the world. There's little reliable data on local incomes in developing countries, which hampers efforts to tackle the problem. A team from Stanford University were able to train a computer system to identify impoverished areas from satellite and survey data in five African countries. Neal Jean, Marshall Burke and colleagues say the technique could transform efforts to track and target poverty in developing countries. "The World Bank, which keeps the poverty data, has for a long time considered anyone who is poor to be someone who lives on below $1 a day," Dr Burke, assistant professor of Earth system science at Stanford, told the BBC's Science in Action programme.


Can Satellites Learn to 'See' Poverty?

The Atlantic

Night lights, therefore, appear to be an incredible resource. So much so that in countries with poor economic statistics, they can serve as a proxy for a regional wealth survey--except no one has to go house to house, running through a questionnaire. Yet research has also shown this not-a-survey will remain inexact: To a satellite at night, a few well-lit mansions and a dense but poorly lit shantytown can look nearly the same. A new paper from a team at Stanford, published last week in Science, applies a trendy technique to this tricky problem. In order to make night lights more discerning, engineers and computer scientists fed a convolutional neural net--a standard type of artificial intelligence program--a series of data sets.


Satellite Images Can Help Predict Poverty

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Scientists at Stanford University have found a new method in predicting poverty through the use of machine learning and satellite images. Using three data sources namely daytime images, night light images, and survey data, scientists built an algorithm to predict how wealthy or poor an area is. The first step involved deep learning techniques, where computers were taught to predict where night lights could be found simply by looking at daytime images. We trained a computer model to find things in imagery that are predictive of poverty.


Satellite Images Can Help Predict Poverty - Artificial Intelligence Online

#artificialintelligence

Scientists at Stanford University have found a new method in predicting poverty through the use of machine learning and satellite images. Using three data sources namely daytime images, night light images, and survey data, scientists built an algorithm to predict how wealthy or poor an area is. The first step involved deep learning techniques, where computers were taught to predict where night lights could be found simply by looking at daytime images. We trained a computer model to find things in imagery that are predictive of poverty.


Satellite images of Earth help us predict poverty better than everTrue Viral News

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Scientists at Stanford University fed a computer three data sources -- night light images, daytime images, and actual survey data -- to build an algorithm that predicts how rich or poor any given area is. Using night lights to predict poverty provides important information about the economic growth of different countries, says Simon Franklin, an economics researcher at the London School of Economics who was not involved with the study. By teaching computers which daytime features translate to night light, researchers can make better predictions about which places are poor. But there isn't much survey data, so using deep learning to connect daytime images and poverty information wouldn't create a very accurate algorithm.