HARI SREENIVASAN: More than ever before, a big part of this election campaign has played itself out on social media. No doubt the candidates and their campaigns have tried to take advantage of these platforms. But there's been a much bigger role this year as well for unseen players. You might call it the rise of the bots. Miles O'Brien has the story, part of our weekly reporting about on the Leading Edge of science and technology.

Signal subspace methods (SSM) are efficient techniques to reduce dimensionality of data and to filter out noise [1]. The fundamental idea under SSM is to project the data on a basis made of two subspaces, one mostly containing the signal and the other the noise. The two subspaces are separated by a thresholding criterion associated with some measures of information. The two most popular methods of signal subspace decomposition are wavelet shrinkage [2] and Principal Component Analysis (PCA) [3]. Both techniques have proved to be quite efficient. However, wavelet decomposition depending on signal statistics is not equally adapted to different data, and requires some knowledge on prior distributions or parameters of signals to efficiently choose the thresholds for shrinkage. A significant advantage of the PCA is its adaptability to data. The separation criterion is based on energy which may be seen as a limitation in some cases as illustrated in the next section. In recent years, sparse coding has attracted significant interest in the field of signal denoising [4].

In this paper, we discuss structure learning of causal networks from multiple data sets obtained by external intervention experiments where we do not know what variables are manipulated. For example, the conditions in these experiments are changed by changing temperature or using drugs, but we do not know what target variables are manipulated by the external interventions. From such data sets, the structure learning becomes more difficult. For this case, we first discuss the identifiability of causal structures. Next we present a graph-merging method for learning causal networks for the case that the sample sizes are large for these interventions. Then for the case that the sample sizes of these interventions are relatively small, we propose a data-pooling method for learning causal networks in which we pool all data sets of these interventions together for the learning. Further we propose a re-sampling approach to evaluate the edges of the causal network learned by the data-pooling method. Finally we illustrate the proposed learning methods by simulations.

The Dong Hai Jiu 101 is back in action. The vessel, which is one of two searching for missing Malaysia Airlines Flight 370 in the Indian Ocean, ended its months-long break from the hunt this week. It also returned with some new equipment, according to an operational update released Wednesday by the Australian Transport Safety Bureau. The Dong Hai Jiu 101 finally left port on Oct. 20 after being delayed for months by bad weather. By Monday, it was back in the search area with a remotely operated vehicle on board.

U .S. military officers watched grainy video feeds at a small operations center in Baghdad on Tuesday as Predator drones tracked and killed three reputed Islamic State leaders -- one after another -- in the offensive on Mosul. The targeted air strikes were due in large part to intelligence extracted from cellphones, computer hard drives, memory cards and hand-written ledgers recovered from battlefields and towns taken from Islamic State fighters. Recently captured intelligence also has proved useful in providing clues to detecting potential terrorist plots, tracking foreign fighters and identifying Islamic State supporters around the globe, U.S. officials said. The largest data trove was recovered when U.S.-backed Syrian rebel forces recaptured Manbij, an Islamic State stronghold in northern Syria, in mid-August. Intelligence agencies recovered more than 120,000 documents, nearly 1,200 devices and more than 20 terabytes of digital information, officials said. Islamic State militants came early in the morning, riding atop trucks that lumbered into this northern Iraqi oil town.

The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off. In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'... It was, for decades, primitive technology. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it won't be long before machines, like their human creators, begin thinking for themselves, creatively.

It took less than 5 mins for me to activate and transfer money from my China Merchant's Bank account to a friend. Using facial recognition, the smartphone app directed me to tilt my head, blink my eyes and hold my face still to authenticate the transfer. Facial recognition is gaining traction at a startling pace in China. Real-world applications already range from verifying Uber drivers to identifying people seeking to withdraw cash from an ATM without a debt card. Chinese startups in this space have attracted serious funding: Face was most recently valued at $1B.

Is there a next phase for cloud computing? During the past few years, cloud computing has become a mainstream element of modern software solutions just as common as websites or databases. The cloud computing market is a race vastly dominated by four companies: Amazon, Microsoft, Google and IBM with a few other platforms with traction in specific regional markets such as AliCloud in China. In such a consolidated market, it's hard to imagine a technology being disruptive enough to alter the existing dynamics. Artificial intelligence (AI) is the type of technology with the potential to not only improve the existing cloud platform incumbents but also power a new generation of cloud computing technologies.

When Bob Dylan won the 2016 Nobel Prize in Literature, it shocked the humanitarian world. What's more, Dylan himself hasn't behaved like a traditional Nobel winner: he hasn't commented on the honor and has yet to give an acceptance speech. At least one member of the Nobel panel has called Dylan's silence "rude and arrogant," and the public has been reminded that if he doesn't give a lecture within six months, he won't receive the $900,000 prize money. Selecting Dylan as a Nobel laureate may be contentious, but it's mostly a sign of growth for intellectual society -- at least in Literature, no one is off-limits, not even mumbling masters of wordplay and songwriting. Growing pains are expected as the world of mainstream politics, activism and academia is suddenly forced to consider the potential of new industries, and vice versa.

They meet people, go to work, even fall in love, all without leaving the comfort of their own home. Now, for the first time, three people with severe spinal injuries have taken the first steps towards that vision by controlling a robot thousands of kilometres away, using thought alone. The idea is that people with spinal injuries will be able to use robot bodies to interact with the world. It is part of the European Union-backed VERE project, which aims to dissolve the boundary between the human body and a surrogate, giving people the illusion that their surrogate is in fact their own body. In 2012, an international team went some way to achieving this by taking fMRI scans of the brains of volunteers while they thought about moving their hands or legs. The scanner measured changes in blood flow to the brain area responsible for such thoughts.