Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.

Following a survey we did back in 2014, I posted on Finextra about how machine learning technologies are progressing from academia, robotics and medical engineering into financial services. At that time, there seemed to be some hesitancy with only 12% of 80 quant-savvy finance professionals saying they used machine learning in their workflows. Has Use of Machine Learning Changed? To provide some answers, we decided to survey attendees at our 2016 finance conference. Our sample was mainly made up of numerically- and model-led quant roles and risk management roles and therefore those most likely to use machine learning.

An anonymous reader quotes a report from Network World discussing Nvidia's project called DAVE2, where their engineering team built a self-driving car with one camera, one Drive-PX embedded computer and only 72 hours of training data: Neural networks and image recognition applications such as self-driving cars have exploded recently for two reasons. First, Graphical Processing Units (GPU) used to render graphics in mobile phones became powerful and inexpensive. GPUs densely packed onto board-level supercomputers are very good at solving massively parallel neural network problems and are inexpensive enough for every AI researcher and software developer to buy. Second, large, labeled image datasets have become available to train massively parallel neural networks implemented on GPUs to see and perceive the world of objects captured by cameras. The Nvidia team trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands.

Here's What We Can Expect from Deep Learning in 2016 and Beyond Is Humanising Chatbots the Next Inevitable Improvement?

V.A.* (Virtual Assistants) are computer programs designed to simulate a conversation with a human via textual or voice interactions. We will focus on text-based interactions only in this paper. Voice-based V.A. consist in the combination of an Automatic Speech Recognition or Speech-To-Text and Text-To-Speech layers on top of a text-based interactions layer. By now you must have heard of Apple SIRI, Amazon Alexa, Microsoft Cortana, IBM Watson, Viv.ai, X.ai Amy, Kasisto Kai etc. They are becoming ubiquitous with the rise of Mobiles, IoT (Internet of Things) and Messaging platforms.

It may sound like something from the Minority Report, but this app can predict where crimes will take place. The software, called CrimeRadar, has just launched its prototype in the Olympic host city of Rio de Janeiro. The app uses advanced machine learning to predict crime rates in the city's neighbourhoods at different times of the day and night. The Olympic Games have, arguably, exposed crime levels in Rio de Janeiro to a wider audience. A string of high profile robberies, allegedly including members of the US swim team; Australian athletes; and even the Brazilian government's head of security for events falling prey to criminals, have been reported.

Here's What We Can Expect from Deep Learning in 2016 and Beyond Is Humanising Chatbots the Next Inevitable Improvement?

A brief history of artificial intelligence The concept of artificial intelligence began as pure fiction, something to be imagined but never actually existing. Today, we know that that's no longer the case. Artificial Intelligence is real and there are already real-world applications where artificial intelligence is helping us solve some of the biggest problems facing humanity. Here's a look at how artificial intelligence has developed through the years. Greek myths The earliest known reference to something we could term artificial intelligence dates back to the ancient Greeks.

You can get a pretty good idea of a country's wealth by seeing how much it shines at night -- just compare the intense brightness of China and South Korea to the dark mass of North Korea that's sandwiched between them. But nighttime lights don't tell you which neighborhoods or villages within a large region are merely poor and which are home to people living in abject poverty. That's the level of detail policymakers need when they decide where to deploy their economic development programs. You could get that detail by sending legions of survey-takers into crowded slums and sparsely populated rural areas. But that would be hugely time-consuming and cost tens of millions of dollars or more.