The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. For more information, check out... Our recently published Science paper: http://science.sciencemag.org/content... A project website featuring poverty maps of Nigeria, Tanzania, Uganda, Malawi, and Rwanda: http://sustain.stanford.edu/predictin...

Aptonomy Inc. has developed drone technology that could make prison breaks, robberies or malicious intrusions of any kind impossible for mere mortals. Dubbing it a kind of "flying security guard," the company has built its systems on top of a drone often used by movie-makers, the DJI S-1000, a camera-carrying octocopter. To that skeleton, Aptonomy adds a new flight controller, and second computer to power day- and night-vision cameras, bright lights, and loudspeakers, among other things. And more importantly than the hardware features, Aptonomy has developed artificial intelligence and navigational systems that allow its drones to fly low and fast, avoiding obstacles in structure-dense environments, and detecting human activity or faces in the area, autonomously. A user can open up a browser, get onto the Aptonomy interface, click on a point on a map to send out a drone to a particular location, then watch that flight in real time, or review a recording of it later.

How Artificial Intelligence Could Help Transform The Oil Industry (oilprice.com) There's another way to transform the oil industry. Give up, you old white bald guys. Electric is here, get used to it.

The cities of the future will be huge and super-dense -- but will they also be alive? Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence? People have speculated before about the idea that the Internet might become self-aware and turn into the first "real" A.I., but could it be more likely to happen to cities, in which humans actually live and work and navigate, generating an even more chaotic system? As cities become more networked and their mixture of urban infrastructure and surveillance infrastructure becomes more complex, eventually we'll have to build cities that can think for themselves. People have speculated about the potential for computer systems to help in urban planning forever, including papers about the use of "fuzzy logic" to automate the decision-making process and A.I. solutions for land use planning, and the an A.I. "spatial decision support system."

The government has decided to collaborate with more than 20 firms and research institutions to promote development of artificial intelligence technology to be applied in the medical, manufacturing and other sectors. The public-private initiative involving entities such as the government-backed Riken research institute, Toyota Motor Corp and NEC Corp is aimed at finding technological solutions to labor shortage and aging society issues that Japan is facing. In the medical field, AI technology is expected to be used in diagnosing a patient's symptoms and giving doctors advice on optimal treatment by analyzing the patient's electronic medical record and a huge amount of past similar cases. The government sees the AI sector as one of the pillars of its growth strategy and set up a research and development center for innovative intelligence technologies at Riken in April.

New ranking algorithms are continually being developed and refined, necessitating the development of efficient methods for evaluating these rankers. Online ranker evaluation focuses on the challenge of efficiently determining, from implicit user feedback, which ranker out of a finite set of rankers is the best. Online ranker evaluation can be modeled by dueling ban- dits, a mathematical model for online learning under limited feedback from pairwise comparisons. Comparisons of pairs of rankers is performed by interleaving their result sets and examining which documents users click on. The dueling bandits model addresses the key issue of which pair of rankers to compare at each iteration, thereby providing a solution to the exploration-exploitation trade-off. Recently, methods for simultaneously comparing more than two rankers have been developed. However, the question of which rankers to compare at each iteration was left open. We address this question by proposing a generalization of the dueling bandits model that uses simultaneous comparisons of an unrestricted number of rankers. We evaluate our algorithm on synthetic data and several standard large-scale online ranker evaluation datasets. Our experimental results show that the algorithm yields orders of magnitude improvement in performance compared to stateof- the-art dueling bandit algorithms.

While the oil and gas industry has had its share of ups and downs over the past decade, many financial institutions are banking on a very slow growth of oil prices in 2017. Though some believe that the efficiency gains that the oil industry can capture are quickly coming to an end, this sentiment is only capturing hard technology specifically related to oil and gas. To help bring the O&G industry to the 21st century, technology from other industries needs to be incorporated, using many hard-earned years of expertise and different lines of thinking. Oilprice previously mentioned incorporating food industry technology to increase safety standards when fracking, but incorporating technology from the IT industry is something that the O&G industry as a whole can benefit from. Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry.

Concrete is one of the most prevalent materials in our built environment, but it's hasn't seen much innovation. That's why it's so exciting that a team of Singapore researchers developed a new flexible concrete that's lighter and tougher than existing mixtures. In other design news, a developer in Germany is building the world's largest passive housing complex with a total of 162 units. Stanford researchers created a tiny black rectangle that uses sunlight to purify water in minutes instead of hours. In a stunning example of biomimicry this week, scientists studied squids to invent a high-tech fabric that repair itself and neutralizes toxins.

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.

In 2014, Google acquired the artificial intelligence startup DeepMind, but it wasn't cheap. With a price tag of 500M, there must have been something special that Google saw in DeepMind that was worth acquiring. While the company hasn't produced any actual products for commercial use, they have focused on machine learning. Machine learning is a type of artificial intelligence that aims to provide computers with the ability to learn new information without being directed to do so. Machine learning involves the development of computer programs that have the ability to teach themselves to grow and alter themselves when presented with new data.