Since the early 2000s, the cost of sequencing a human genome -- determining the precise order of nucleotides within DNA molecules that defines who we are -- has dropped sharply. A genome that cost $100m to sequence in 2001 can today be sequenced for roughly $1,000. This plummeting cost, along with the shortened timescales for sequencing DNA, has led to a revolution in biotechnology: gene hacking, or the ability to turn genes on and off, and to manipulate biology to our advantage. The most radical branch of this new technology is "gene editing" -- a process by which our DNA code can be cut and pasted using molecular "scissors" for a variety of applications, including curing diseases such as cancers and HIV. Until recently, swapping the code was an arduous process.

Predicting earthquakes is the holy grail of seismology. After all, quakes are deadly precisely because they're erratic--striking without warning, triggering fires and tsunamis, and sometimes killing hundreds of thousands of people. If scientists could warn the public weeks or months in advance that a large temblor is coming, evacuation and other preparations could save countless lives. So far, no one has found a reliable way to forecast earthquakes, even though many scientists have tried. Some experts consider it a hopeless endeavor.

We present a novel approach for planning the development of hydrocarbon fields, taking into account the sequential nature of well drilling decisions and the possibility to react to future information. In a dynamic fashion, we want to optimally decide where to drill each well conditional on every possible piece of information that could be obtained from previous wells. We formulate this sequential drilling optimization problem as a POMDP, and propose an algorithm to search for an optimal drilling policy. We show that our new approach leads to better results compared to the current standard in the oil and gas (O&G) industry.

Last year, on October 21, your digital video recorder -- or at least a DVR like yours -- knocked Twitter off the internet. Someone used your DVR, along with millions of insecure webcams, routers, and other connected devices, to launch an attack that started a chain reaction, resulting in Twitter, Reddit, Netflix, and many sites going off the internet. You probably didn't realize that your DVR had that kind of power. This has as much to do with the computer market as it does with the technologies. We prefer our software full of features and inexpensive, at the expense of security and reliability. That your computer can affect the security of Twitter is a market failure. The industry is filled with market failures that, until now, have been largely ignorable. As computers continue to permeate our homes, cars, businesses, these market failures will no longer be tolerable. Our only solution will be regulation, and that regulation will be foisted on us by a government desperate to "do something" in the face of disaster. In this article I want to outline the problems, both technical and political, and point to some regulatory solutions. Regulation might be a dirty word in today's political climate, but security is the exception to our small-government bias. And as the threats posed by computers become greater and more catastrophic, regulation will be inevitable. So now's the time to start thinking about it. We also need to reverse the trend to connect everything to the internet. And if we risk harm and even death, we need to think twice about what we connect and what we deliberately leave uncomputerized. If we get this wrong, the computer industry will look like the pharmaceutical industry, or the aircraft industry.

Live updates: Fight is on to lower Lake Oroville's water level before new storms hit Officials are rushing to repair the spillways at the Oroville Dam in Northern California and lower the water level in Lake Oroville before rain arrives later this week. Officials are concerned that damage to an emergency spillway could dump large amounts of water into the Feather River, which runs through downtown Oroville. Engineers are racing to lower the water level at Lake Oroville. These graphics explain what is happening at the Oroville Dam. Here is Butte County's emergency information website.

A universal basic income will become necessary because of all the jobs that will be lost to automation in the coming years, says the founder and CEO of Tesla Motors. "There will be fewer and fewer jobs that a robot cannot do better," billionaire entrepreneur Elon Musk told a crowd on Monday at the World Government Summit in Dubai. "These are not things I wish will happen; these are things I think probably will happen." Elon Musk, Chairman of SolarCity and CEO of Tesla Motors, speaks at SolarCity's Inside Energy Summit in Manhattan, New York October 2, 2015. The man behind the Tesla electric car, the SpaceX private space flight program and solar power company SolarCity then brought up the question of what to do about a future with fewer jobs.

Siemens has been researching neural networks for about 30 years and has made significant progress in applying this technology to artificial intelligence. For example, the company's Software Environment for Neural Networks (SENN) is being continuously refined and adapted to new and evolving applications, including the optimization of gas turbines and wind turbines. "We hold something like 50 patents for learning processes," notes Sterzing. Siemens Power Generation Services and CT have developed a system that continuously optimizes the operation and control of combustion in gas turbines. Based on AI from CT, the system, which is known as a Gas Turbine Autonomous Control Optimizer (GT-ACO), is currently being installed at a top customer in Asia.

We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case MTL is known to be undecidable. Nonrecursive datalogMTL turns out to be PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets of up to 11GB.

Ensemble Clustering (EC) has gained a great deal of attention throughout the fields of data mining and machine learning, since it emerged as an effective and robust clustering framework. Typically, EC methods try to fuse multiple basic partitions (BPs) into a consensus one, of which each BP is obtained by performing traditional clustering method on the same dataset. One promising direction for ensemble clustering is to derive pairwise similarity from BPs, and then transform it as a graph partition problem. However, these graph based methods may suffer from an information loss when computing the similarity between data points, because they only utilize the categorical data provided by multiple BPs, yet neglect rich information from raw features. This problem can badly undermine the underlying cluster structure in the original feature space, and thus degrade the clustering performance. In light of this, we propose a novel Simultaneous Clustering and Ensemble (SCE) framework to alleviate such detrimental effect, which employs the similarity matrix from raw features to enhance the co-association matrix summarized by multiple BPs. Two neat closed-form solutions given by eigenvalue decomposition are provided for SCE. Experiments conducted on 16 real-world datasets demonstrate the effectiveness of the proposed SCE over the traditional clustering and state-of-the-art ensemble clustering methods. Moreover, several impact factors that may affect our method are also explored extensively.

The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a given time series. The DyBM, however, is defined only for binary valued data, without any nonlinear hidden units. Here, in our first contribution, we extend the DyBM to deal with real valued data. We present a formulation called Gaussian DyBM, that can be seen as an extension of a vector autoregressive (VAR) model. This uses, in addition to standard (explanatory) variables, components that captures long term dependencies in the time series. In our second contribution, we extend the Gaussian DyBM model with a recurrent neural network (RNN) that controls the bias input to the DyBM units. We derive a stochastic gradient update rule such that, the output weights from the RNN can also be trained online along with other DyBM parameters. Furthermore, this acts as nonlinear hidden layer extending the capacity of DyBM and allows it to model nonlinear components in a given time-series. Numerical experiments with synthetic datasets show that the RNN-Gaussian DyBM improves predictive accuracy upon standard VAR by up to 35%. On real multi-dimensional time-series prediction, consisting of high nonlinearity and non-stationarity, we demonstrate that this nonlinear DyBM model achieves significant improvement upon state of the art baseline methods like VAR and long short-term memory (LSTM) networks at a reduced computational cost.