Chalenge Masekera BIG data, data science, data mining, machine learning and artificial intelligence are currently buzzwords in the world of technology. So what is the hype all about and what does this mean for business in Zimbabwe? By adopting data science businesses stand a chance to be able to understand and predict customer behaviour and system processes in simple and faster ways. Broadly, data science refers to the use and conversion of data into knowledge and actionable insights. All these industries that embraced ICTs some years ago and record their transactional data daily in computer systems are prime candidates for data science.

One of the biggest problems in solving poverty worldwide is the scarcity of reliable data in developing countries. Researchers have sought to address this by combining satellite imagery with artificial intelligence to identify impoverished areas from space, the BBC reports. A team from Stanford University trained a computer system and surveyed information in five African countries. Researchers Neal Jean, Marshall Burke, and their colleagues say this method could go a long way in tracking and targeting poverty in specific countries. Burke, an assistant professor of Earth system science at Stanford, says, "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."

Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise locations lead to waste of time and money during the operation. The objective of this study is to locate high-potential oil and gas field in 1: 250,000 sheet of Ahwaz including 20 oil fields to reduce both time and costs in exploration and production processes. In this regard, 17 maps were developed using GIS functions for factors including: minimum and maximum of total organic carbon (TOC), yield potential for hydrocarbons production (PP), Tmax peak, production index (PI), oxygen index (OI), hydrogen index (HI) as well as presence or proximity to high residual Bouguer gravity anomalies, proximity to anticline axis and faults, topography and curvature maps obtained from Asmari Formation subsurface contours. To model and to integrate maps, this study employed artificial neural network and adaptive neuro-fuzzy inference system (ANFIS) methods. The results obtained from model validation demonstrated that the 17x10x5 neural network with R=0.8948, RMS=0.0267, and kappa=0.9079 can be trained better than other models such as ANFIS and predicts the potential areas more accurately. However, this method failed to predict some oil fields and wrongly predict some areas as potential zones.

Pop culture's many takes on artificial intelligence

Stanford scientists have found a low-cost method to map poverty in areas previously devoid of data, by combining satellite images and making use of machine learning. These improved poverty maps could help organisations and policymakers distribute funds more efficiently and evaluate policies, researchers said. One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly in African countries. Aid groups and other international organisations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

Pop culture's many takes on artificial intelligence New technique using artificial intelligence to read satellite images could aid efforts to eradicate ...

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Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels. Do you remember lugging around those old suitcases? If not, good for you -- this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his idea to add wheels, because when he pitched his idea people thought he was crazy. Surely no one would want to pull their own suitcase?

There are few bigger challenges than trying to solve world poverty. While there are plenty of initiatives going on in this area, one of the most intriguing is being carried out by researchers at Stanford University. Using a combination of satellite data and machine learning, they've developed a "poverty map" of Africa that could help direct aid to some of the world's most deprived areas. "One part of the problem when it comes to dealing with poverty is that we don't have very good data," Neal Jean, a Ph.D student in Machine Learning at Stanford, told Digital Trends. "If we want to help people, but we don't know exactly where they are, that makes it very difficult to do. Traditionally, the way data is collected on poverty is by going out into the field and having people conduct surveys. Our objective in doing this project was to come up with a cost-effective and scalable way of filling in some of these data gaps."

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.