Claridge, founder of xCraft, is best known for being the first contestant on Shark Tank to receive money from all the Sharks – even Kevin O'Leary! Walking the floor of Xponential 2017, the annual convention of the Association for Unmanned Vehicle Systems Integration (AUVSI), Claridge remarked to me how the drone industry has grown up since his TV appearance. Claridge has gone from pitching cellphone cases that turn into drones (aka phonedrone) to solving mission critical problems. The age of fully autonomous flight is near and the drone industry is finally recovering from the hangover of overhyped Kickstarter videos (see Lily drone's $34 million fraud). During the three days of Xponential 2017, several far-reaching announcements were made between stalwarts of the tech industry and aviation startups.

Smartphone batteries that charge in five minutes could be available next year, according to a firm developing the technology. The FlashBattery is designed to charge from empty to full capacity in around the time it takes to boil a kettle, or about one hundred times faster than the average smartphone. The Israeli firm behind the battery first unveiled its technology in 2014, before showcasing a more slim-lined version at the Consumer Electronics Show tech show in Las Vegas eight months later in early 2015. Doron Myersdorf, the CEO of StoreDot, told BBC News that mass production will begin by early 2018. The batteries have shorter lives than those currently available in smartphones, but the firm believes the speed at which they are juiced up means this isn't a problem.

Nigerian health officials won't have to rely on flawed, decade-old census data when they plan deliveries of the measles vaccine next year. Instead, they will have access to what may be the most detailed and up-to-date population map ever produced for a developing country. Created by the Bill & Melinda Gates Foundation in Seattle, Washington, and delivered to Nigerian officials on May 1, the map is based on a detailed analysis of buildings in satellite imagery and more than 2,000 on-the-ground neighbourhood surveys. It is one of several projects that are leveraging remote-sensing data and modern computer-learning algorithms to chart human settlements around the globe with unprecedented precision. Researchers hope that these data will enable better management of public health, infrastructure and natural resources--and improve planning for emergencies.

Japan's 2011 Fukushima nuclear disaster had an impact on every single person on Earth, according to scientists. The meltdown hit everyone on the planet with a dose of radiation, but fortunately, not enough to have a real impact. "We don't need to worry," said Nikolas Evangeliou, according to New Scientist. He is part of the team at the Norwegian Institute for Air Research that conducted the survey. Read: Fukushima's Nuclear Radiation So Destructive, Not Even Robots Can Survive The average person was dosed with 0.5 millisieverts of radiation from the accident. For those in the immediate vicinity of Fukushima, the amount was much higher, at about one to five millisieverts.

Like most other smart doorbells, Ring lets you see who's at your door and interact with your guests before letting them in. Unlike its competition, however, Ring doesn't require dedicated electrical wiring; instead, it can work off of rechargeable battery power. Since our test home doesn't have built-in doorbell wiring, we chose the wireless installation option. Unfortunately, despite the freedom it offered, we still didn't have a lot of placement options for the Ring. The former was too thin to serve as an adequate base, while the latter were too short to accommodate the Ring's entire length.

The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences, due to the costs inherent to drilling wells, and the potential return of oil deposits. In this paper, we describe preliminary work aimed at building a general framework for well log prediction. First, we perform a descriptive and exploratory analysis of the gaps in the neutron porosity logs of more than a thousand wells in the North Sea. Then, we generate artificial gaps in the neutron logs that reflect the statistics collected before. Finally, we compare Artificial Neural Networks, Random Forests, and three algorithms of Linear Regression in the prediction of missing gaps on a well-by-well basis.

When I give talks on data strategy and AI strategy, I talk about data being the new oil. And, data being the fuel (oil) that is fueling the AI machine. I also talk about how to value data, since it is perhaps the most valuable modern business asset, yet is not yet accurately accounted for. Check out the slide below that I present as part of my 40 minute talk. It compares the largest companies (by market cap) from 2006 vs. 20016.

In this paper, we consider a finite-horizon Markov decision process (MDP) for which the objective at each stage is to minimize a quantile-based risk measure (QBRM) of the sequence of future costs; we call the overall objective a dynamic quantile-based risk measure (DQBRM). In particular, we consider optimizing dynamic risk measures where the one-step risk measures are QBRMs, a class of risk measures that includes the popular value at risk (VaR) and the conditional value at risk (CVaR). Although there is considerable theoretical development of risk-averse MDPs in the literature, the computational challenges have not been explored as thoroughly. We propose data-driven and simulation-based approximate dynamic programming (ADP) algorithms to solve the risk-averse sequential decision problem. We address the issue of inefficient sampling for risk applications in simulated settings and present a procedure, based on importance sampling, to direct samples toward the "risky region" as the ADP algorithm progresses. Finally, we show numerical results of our algorithms in the context of an application involving risk-averse bidding for energy storage.

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