This is computer-generated wordplay and an example of how the boundaries of artificial intelligence are shifting. If a computer can crack jokes, what other human activities could they start to replicate? What jobs could it take? Artificial intelligence has become an increasingly big issue for education - not least because many tech companies and publishers are circling around the huge commercial opportunities. But could students really get their answers from a robot rather than a teacher?

WASHINGTON – A coalition drone strike in Syria killed three Islamic State group leaders involved in plotting foreign attacks, including two men who helped facilitate last year's attacks in Paris, the Pentagon said Tuesday. "The three were working together to plot and facilitate attacks against Western targets at the time of the strike," Pentagon press secretary Peter Cook said in a statement. They were killed in a Dec. 4 airstrike in Raqqa, an IS group stronghold in Syria. Two of those killed -- Salah-Eddine Gourmat and Sammy Djedou -- were involved in facilitating the Nov. 13, 2015, Paris attacks, in which 130 people died, Cook said. Gourmat was a French national and Djedou, Belgian.

In our recent reviews of historical Watson and the modern Watson of today we concluded that IBM's Watson Group may have the first or at least the current strongest comprehensive AI platform. This is the first time that we know of that all three elements of AI have been brought together in a single user friendly platform: image processing, text and speech processing, and knowledge retrieval. This is not so much a platform for data scientist to use to expand the capabilities of AI as it is a platform for business users (with the aid of data scientists) to exploit the capabilities of modern AI by building new products and services. To wrap up this review of Watson, we wanted to provide some thought-starters on what new services or even new businesses you might build on Watson. Oh, and regarding new businesses, did we mention that developers who join the Watson Ecosystem are eligible to become a Watson "partner" with a shot at the $100 Million funding IBM is making available to startups plus support and access from IBM business and technology advisors.

"…humans--through our ingenuity, our commitment to fact and reason, and ultimately our faith in each other--can science the heck out of just about any problem," wrote President Barack Obama in the November 2016 issue of WIRED magazine, which he guest edited. That dynamic is readily apparent in supply chain management, where many of those humans President Obama mentions are eagerly exploring how new technology can revolutionize the way we solve some of our thorniest business problems. Articles on how disruptive technology will reshape supply chain management as we know it usually focus on a few key topics. Drones, big data analytics, 3D printing, IoT, robotics, and artificial intelligence frequently appear at the top of the list. Artificial intelligence in particular landed on President Obama's radar, and he expressed a positive attitude toward the potential it holds.

Curiosity in crisis: NASA reveals rover's drill has crippled its robotic arm and stranded it on the red planet Bug is preventing the rover from moving its robotic arm and driving Problem involves a motor in the rover's drill that is used to bore into rocks Curiosity has been taking pictures and tracking the weather Problem involves a motor in the rover's drill that is used to bore into rocks This Dec. 2, 2016 image taken by NASA's Curiosity rover shows rocky ground on the lower flank of Mount Sharp, a mountain on Mars. Curiosity landed on the red planet in 2012 and uncovered geologic evidence of an ancient environment that could have supported primitive life early in the red planet's history. The highest wave in history: UN confirms six-storey-high... Facebook launches'Parents Portal' to help adults keep their... Christmas comes to the space station! Japanese'white stork'... Using a hands-free kit while driving is just as distracting... The highest wave in history: UN confirms six-storey-high... Facebook launches'Parents Portal' to help adults keep their... Christmas comes to the space station!

A dynamic Boltzmann machine (DyBM) has been proposed as a model of a spiking neural network, and its learning rule of maximizing the log-likelihood of given time-series has been shown to exhibit key properties of spike-timing dependent plasticity (STDP), which had been postulated and experimentally confirmed in the field of neuroscience as a learning rule that refines the Hebbian rule. Here, we relax some of the constraints in the DyBM in a way that it becomes more suitable for computation and learning. We show that learning the DyBM can be considered as logistic regression for binary-valued time-series. We also show how the DyBM can learn real-valued data in the form of a Gaussian DyBM and discuss its relation to the vector autoregressive (VAR) model. The Gaussian DyBM extends the VAR by using additional explanatory variables, which correspond to the eligibility traces of the DyBM and capture long term dependency of the time-series. Numerical experiments show that the Gaussian DyBM significantly improves the predictive accuracy over VAR.

Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms. But the influence of its exponent remains poorly understood. In particular, it is unclear how the exponent of the reproducing kernel Hilbert space~(RKHS) regularization term affects the accuracy and the efficiency of kernel-based learning algorithms. Here we consider regularized least squares regression (RLSR) with an RKHS regularization raised to the power of m, where m is a variable real exponent. We design an efficient algorithm for solving the associated minimization problem, we provide a theoretical analysis of its stability, and we compare its advantage with respect to computational complexity, speed of convergence and prediction accuracy to the classical kernel ridge regression algorithm where the regularization exponent m is fixed at 2. Our results show that the m-power RLSR problem can be solved efficiently, and support the suggestion that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.

General introduction Network and link analysis is a highly studied field, subject of much recent work in various areas of science: applied mathematics, computer science, social science, physics, chemistry, pattern recognition, applied statistics, data mining & machine learning, to name a few [4, 20, 30, 56, 61, 73, 96, 101]. Within this context, one key issue is the proper quantification of the structural relatedness between nodes of a network by taking both direct and indirect connections into account. This problem is faced in all disciplines involving networks in various types of problems such as link prediction, community detection, node classification, and network visualization to name a few popular ones. Preprint submitted to Elsevier January 2, 2018 The main contribution of this paper is in presenting in detail the bag-ofpaths (BoP) framework and defining relatedness as well as distance measures between nodes from this framework. The BoP builds on and extends previous work dedicated to the exploratory analysis of network data [54, 53, 67, 104]. The introduced distances are constructed to capture the global structure of the graph by using paths on the graph as a building block. In addition to relatedness/distance measures, various other quantities of interest can be derived within the probabilistic BoP framework in a principled way, such as betweenness measures quantifying to which extent a node is in between two sets of nodes [60], extensions of the modularity criterion for, e.g., community detection [26], measures capturing the criticality of the nodes or robustness of the network, graph cuts based on BoP probabilities, and so on.

Wars are not just won by those in the field, but by those who support them and provide them the proper intelligence. It's a known fact, that between each war, is the "war between wars". This refers to in military terms the time where each side is collecting intel, whether human, signal intelligence, targets and a whole plethora of information that will allow a strategical advantage to each side. This information is also used to deploy diplomacy to try and avoid the wars to begin with, if they can and should be avoided. In the 2014 Blockbuster "The Imitation Game" we learn about the true story of how Alan Turing helped the Allies, and specifically Britain, by building a machine that would be able to crack the Enigma Machine the Germans were using to encrypt their signal intelligence.

For the past 30 days in the frigid port city of Duluth, Minnesota, a colony of houseflies has been kept alive by a piece of software. The computer takes care of their needs, giving the insects water and nutrients in the form of powdered milk and sugar. The flies, of course, are unaware that their ultimate fate depends on whether or not a machine correctly identifies blobs of pixels flitting across a camera--if it fails, they die. It's one hell of a metaphor in a time where futurists are considering a world where daily needs are met by computers that track and analyze us. "We should be smart about how we plan for artificial intelligence, because one way or another it's coming," said David Bowen, the 41-year-old artist and professor behind the installation, which he calls FlyAI.