Crescent Dunes looks and sounds a bit like an invention lifted from a science fiction novel. Deep in the Nevada desert more than 10,000 mirrors--each the size of a highway billboard--neatly encircle a giant 640-foot tower. It looks like it might be used to communicate with aliens in deep space. But the engineers and financiers behind the facility, located in the desert about halfway between Las Vegas and Reno, say the power plant's promise is anything but fiction. The solar power facility built and operated by the company SolarReserve can power 75,000 homes.

When artificial intelligence technology intersects with abundant oil and gas seismic data, the outcome could yield a more accurate depiction of what lies beneath the surface, enabling cash-strapped drillers to better target sweet spots and maximize returns. It's all based on algorithms that essentially teach computers how to solve complex problems--in this instance, how to quickly and accurately find subsurface faults that lead to lucrative hydrocarbon discoveries. Naveen Rao, the CEO of two-year-old startup Nervana Systems, compared the concept to the brain and its network of neurons. "Each neuron does a little bit of information processing. It combines that with the output of many other neurons, and the whole stack basically processes information that comes in through our sensors," Rao told Hart Energy.

A snake is the shortest animal between two points. At least, that seems to be the thinking behind this aquatic snakebot. Built as a collaboration between Norwegian maritime and defense technology giant Kongsberg, The Norwegian University of Science and Technology, and Norway's Statoil energy company, the Eelume robot is a tethered undersea snake with jets, made to go where humans simply can't. Here's what the untethered concept looked like: At its head, there's a light and a grasper, which would allow the robot to turn dials, pull levers, or otherwise manipulate objects under the sea like a disembodied diver's arm. That makes it especially useful for inspecting and repairing oil rigs, or other underwater structures (like lost Loch Ness Monster props).

Updated April 12, 2016 16:26:57 With oil and gas prices hovering at decade lows, companies are turning to artificial intelligence to cut costs and boost productivity. The technology, which gives companies the ability to predict future problems, is estimated to save the industry trillions of dollars and lead to a new wave of highly sophisticated jobs. At a time when the banking industry needs to become increasingly focused on creating better customer experiences, the importance of distributing personalized communications that provide real value has never been greater. Artificial intelligence (AI) can help make this possible - both automatically and at scale. The banking industry is undergoing a major transformation.

Don't be shocked if you see a mechanical snake swimming around undersea equipment in the near future... it (probably) isn't there to kill you. Eelume, Kongsberg Maritime and Statoil are building a robotic snake worker that will inspect (and occasionally fix) underwater gear. Robot snakes are nothing new, but this serpent is both production-ready and almost uncanny in how it moves. Throw in thrusters, however, and it's something else -- it can quickly twist around pipes as if they were only minor obstacles.

Forecasting models play a key role in money-making ventures in many different markets. Such models are often trained on data from various sources, some of which may be untrustworthy.An actor in a given market may be incentivised to drive predictions in a certain direction to their own benefit.Prior analyses of intelligent adversaries in a machine-learning context have focused on regression and classification.In this paper we address the non-iid setting of time series forecasting.We consider a forecaster, Bob, using a fixed, known model and a recursive forecasting method.An adversary, Alice, aims to pull Bob's forecasts toward her desired target series, and may exercise limited influence on the initial values fed into Bob's model.We consider the class of linear autoregressive models, and a flexible framework of encoding Alice's desires and constraints.We describe a method of calculating Alice's optimal attack that is computationally tractable, and empirically demonstrate its effectiveness compared to random and greedy baselines on synthetic and real-world time series data.We conclude by discussing defensive strategies in the face of Alice-like adversaries.

Gradient backpropagation (BP) requires symmetric feedforward and feedback connections — the same weights must be used for forward and backward passes. This "weight transport problem'' (Grossberg 1987) is thought to be one of the main reasons to doubt BP's biologically plausibility. Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.'s demonstration (Lillicrap et al. 2014) but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter — the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) (Ioffe and Szegedy 2015) and/or a "Batch Manhattan'' (BM) update rule.

This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defining new tasks/problems. In particular, it utilizes a principled Bayesian sequential decision problem framework for jointly and naturally optimizing the exploration-exploitation trade-off. In general, the resulting induced GPP policy cannot be derived exactly due to an uncountable set of candidate observations. A key contribution of our work here thus lies in exploiting the Lipschitz continuity of the reward functions to solve for a nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real time, we further propose an asymptotically optimal, branch-and-bound anytime variant of epsilon-GPP with performance guarantee. We empirically demonstrate the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian optimization and an energy harvesting task.

We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com.

With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.