Whenever an earthquake strikes, news reports quickly fill in certain details, such as how strong the quake was and where it was centered. That information comes from a networks of seismometers scattered across the planet. Seismometers, though, can be expensive to install and maintain over long periods, and researchers cannot place them everywhere they might like, such as in the densely built and expensive streets of an earthquake-prone city like San Francisco. Some scientists, however, are exploring a different approach, using a sensor that is already widely deployed beneath the streets of towns and cities around the world. That sensor is the common fiber-optic cable, used to carry telephone and Internet traffic.

As part of Information Age's Artificial Intelligence Month, we are providing three CTO guides over the coming weeks on artificial intelligence: what it is, the industries most impacted and implementation best practices. The first guide discussed how business leaders and CTOs understand artificial intelligence; and how they define the technology in the context of business. Opinions ranged from AI being just an algorithm, to a spectrum of technologies that are already active in everyday life. In this guide, seven CTOs and AI experts provide their view on what is artificial intelligence; and how they define the technology in the context of business. This is the second guide, and will focus on the industries that AI will impact the most, with insights from CTOs and AI experts.

The rise of computer vision has given us robot chefs and cameras that detect gas flares in fuel production. It's also led to an increase in connected cameras that are trying to run at the edge of the network. "Running at the edge" means these cameras are not only communicating wirelessly with the cloud but also communicating with local gateways and working with built-in logic boards to complete a task. The task might be as simple as notifying a manufacturer when a production line produces a defective item or as complex as identifying a person to determine if the system should sound an alarm. But as we connect more cameras and ask them to perform more complicated tasks, their fundamental architecture is changing.

Countless dollars and entire scientific careers have been dedicated to predicting where and when the next big earthquake will strike. But unlike weather forecasting, which has significantly improved with the use of better satellites and more powerful mathematical models, earthquake prediction has been marred by repeated failure. Some of the world's most destructive earthquakes -- China in 2008, Haiti in 2010 and Japan in 2011, among them -- occurred in areas that seismic hazard maps had deemed relatively safe. The last large earthquake to strike Los Angeles, Northridge in 1994, occurred on a fault that did not appear on seismic maps. Now, with the help of artificial intelligence, a growing number of scientists say changes in the way they can analyze massive amounts of seismic data can help them better understand earthquakes, anticipate how they will behave, and provide quicker and more accurate early warnings. "I am actually hopeful for the first time in my career that we will make progress on this problem," said Paul Johnson, a fellow at the Los Alamos National Laboratory who is among those at the forefront of this research.

Researchers are planning to recreate the conditions of the lunar surface right here at home. A new facility in the works at ESA's Astronaut Centre in Cologne, Germany will soon serve as a three-part moon analogue environment on Earth, the agency announced this month. There, scientists will simulate lunar soil and a moon habitat, powered by systems that could one day be used to support a real base on the moon. Researchers are planning to recreate the conditions of the lunar surface right here at home. A new facility in the works at ESA's Astronaut Centre in Cologne, Germany will soon serve as a three-part moon analogue environment on Earth.

Global demographic and economic changes have a critical impact on the total energy consumption, which is why demographic and economic parameters have to be taken into account when making predictions about the energy consumption. This research is based on the application of a multiple linear regression model and a neural network model, in particular multilayer perceptron, for predicting the energy consumption. Data from five Balkan countries has been considered in the analysis for the period 1995-2014. Gross domestic product, total number of population, and CO2 emission were taken as predictor variables, while the energy consumption was used as the dependent variable. The analyses showed that CO2 emissions have the highest impact on the energy consumption, followed by the gross domestic product, while the population number has the lowest impact. The results from both analyses are then used for making predictions on the same data, after which the obtained values were compared with the real values. It was observed that the multilayer perceptron model predicts better the energy consumption than the regression model.

The oil industry has fallen hard for digital tech, with artificial intelligence and robotics a particular focus of attraction, KPMG's latest CEO Outlook: Oil & Gas has suggested. As much as 85 percent of respondents--52 global chief executives from the oil and gas industry--told KPMG that they have either already adopted AI in their operations or are in the process of testing it for adoption. What's more, a high percentage of the respondents believe that this increased adoption of digital technology solutions will actually create more jobs. Oil and gas companies' excitement about technology is not exactly new, but it is intensifying. After years of shunning suspiciously unfamiliar, possibly dangerous alternatives to the way business in the industry has been done for decades, oil and gas producers are now eager to enter the new digital age and reap as many benefits from it as possible.

We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table. Many complex domains are appropriately described in terms of sets of objects, properties of those objects, and relations among them. We are interested in the problem of taking actions to change the state of such complex systems, in order to achieve some objective. To do this, we require a transition model, which describes the system state that results from taking a particular action, given the previous system state.

Variable speed limits (VSL) control is a flexible way to improve traffic condition,increase safety and reduce emission. There is an emerging trend of using reinforcement learning technique for VSL control and recent studies have shown promising results. Currently, deep learning is enabling reinforcement learning to develope autonomous control agents for problems that were previously intractable. In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed. The proposed DRL models use a novel actor-critic architecture which can learn a large number of discrete speed limits in a continues action space. Different reward signals, e.g. total travel time, bottleneck speed, emergency braking, and vehicular emission are used to train the DVSL controller, and comparison between these reward signals are conducted. We test proposed DRL baased DVSL controllers on a simulated freeway recurrent bottleneck. Results show that the efficiency, safety and emissions can be improved by the proposed method. We also show some interesting findings through the visulization of the control policies generated from DRL models.

Noname manuscript No. (will be inserted by the editor) Abstract Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regressor. We then propose a half-quadratic learning algorithm in order to find the parameter of the regressor and the optimal weights associated with each loss function. Moreover, we show that our proposed loss function is robust in noisy environments. For a particular class of loss functions, we show that our proposed ensemble loss function is Bayes consistent and robust. Experimental evaluations on several data sets demonstrate that the our proposed ensemble loss function significantly improves the performance of a simple regressor in comparison with state-of-the-art methods. Keywords Loss function · Ensemble methods · Bayes Consistent Loss function · Robustness 1 Introduction Loss functions are fundamental components of machine learning systems and are used to train the parameters of the learner model.