A bestselling author has reportedly predicted that the rise of Artificial Intelligence (AI) could have an outcome that is more anticlimactic than even seen in doomsday movies. In his upcoming novel Homo Deus: A Brief History of Tomorrow, author Yuval Noah Harari has described a bleak future for humankind, where instead of being completely wiped out by robotic beings, humans face a bleaker future of being rendered completely useless. A lecturer and historian at Jerusalem's Hebrew University, Harari believes AI will be successful in doing exactly as we fear. Machines will take over society and leave humans jobless and aimless. In fact, according to the author, the destructive powers of artificial intelligence have already started to take control and there are many areas where AI has performed better than people.

The Afghan Taliban confirmed on Wednesday that their leader Mullah Akhtar Mansour was killed in a U.S. drone strike last week and that they have appointed a successor -- a scholar known for extremist views who is unlikely to back a peace process with Kabul. The announcement came as a suicide bomber struck a minibus carrying court employees in the Afghan capital, killing at least 11 people, an official said. The Taliban promptly claimed responsibility for the attack. In a statement sent to the media, the Taliban said their new leader is Mullah Haibatullah Akhundzada, one of Mansour's two deputies. The insurgent group said he was chosen at a meeting of Taliban leaders, which is believed to have taken place in Pakistan, but offered no other details.

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The Afghan Taliban confirmed on Wednesday that their leader Mullah Akhtar Mansour was killed in a U.S. drone strike last week and that they have appointed a successor -- a scholar known for extremist views who is unlikely to back a peace process with Kabul. The announcement came as a suicide bomber struck a minibus carrying court employees in the Afghan capital, killing at least 10 people, an official said. The Taliban promptly claimed responsibility for the attack. In a statement sent to the media, the Taliban said their new leader is Mullah Haibatullah Akhundzada, one of Mansour's two deputies. The insurgent group said he was chosen at a meeting of Taliban leaders, which is believed to have taken place in Pakistan, but offered no other details.

The collaboration will accelerate the creation of Zebra's imaging analytics engine and create neural networks that will use Zebra's vast imaging dataset to assist radiologists with automated diagnostic algorithms. Kibbutz Shefayim Israel, May 24, 2016 - Zebra Medical Vision is announcing a new collaboration with Intermountain Healthcare, one of the top performing integrated care providers in the U.S. Intermountain plans to work with Zebra to accelerate the creation of meaningful imaging algorithms to improve patient care. Zebra is also announcing today an additional financing round of 12 million led by Intermountain Healthcare, with the participation of existing investors. Zebra Medical Vision was founded in 2014 with the vision of teaching computers to automatically read and diagnose medical imaging data. The company's analytics engine helps physicians and healthcare providers analyze millions of imaging records, in an effort to close the diagnostic gap created by a billion people worldwide joining the middle class in the coming decade, who will require diagnostic services.

"Diversified application areas are expected to drive the artificial intelligence market" The artificial intelligence market is estimated to grow from USD 419.7 million in 2014 to USD 5.05 billion by 2020, at a CAGR of 53.65% from 2015 to 2020. This growth can be attributed to the factors such as diversified application areas, improved productivity, and increased customer satisfaction. "Machine learning technology to gain maximum traction during the forecast period" The machine learning technology is expected to account for the largest share of the overall AI market duing the forecast period. In addition, due to the increase in demand for AI from the media & advertising and finance sectors, the artificial intelligence market is expected to gain traction in the next five years. The machine learning technology market for the retail, healthcare, law, and oil & gas sectors is also expected to witness growth during the forecast period.

Artificial Intelligence is already operating in a range of limited but interesting ways across the healthcare sector. The use of processing computers that can sift and sort data hundreds if not thousands of times quicker than humans is growing, with research suggesting that we spent around 2 billion in venture backed capital on it in 2015. But where is its use likely to impact healthcare in the next decade or so, with reports predicting spending on AI in healthcare will reach as much as 20 Billion in 10 years time? Before we look at applications in healthcare in particular, we should remember that AI is an umbrella term for three related technologies; machine learning, extended human cognition and robotics. AI is quite a broad field and in this regard the impact on healthcare as one large industry is likely to be significant, especially in being able to be major new platform/systems leveraging by SAAS systems and databases intelligently talking to each other.

The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computation (both stochastic gradients and parallelization can be used). Even so, conjugate gradient is not without its own issues: the conditioning of kernel matrices is often such that conjugate gradients will have poor convergence in practice. Preconditioning is a common approach to alleviating this issue. Here we propose preconditioned conjugate gradients for kernel machines, and develop a broad range of preconditioners particularly useful for kernel matrices. We describe a scalable approach to both solving kernel machines and learning their hyperparameters. We show this approach is exact in the limit of iterations and outperforms state-of-the-art approximations for a given computational budget.

In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification. The EPFES is derived from the frequently-employed state-space model, where the relevant information of the nonlinear system is captured by an unknown state vector. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this article, we propose two advancements of the previously-published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter for two completely different scenarios: First, we consider the so-called univariate nonstationary growth model with time-variant latent state variable, where the evolutionary selection of elitist particles is evaluated for non-recursively calculated particle weights. Second, the problem of nonlinear acoustic echo cancellation is addressed in a simulated scenario with speech as input signal: By using long-term fitness measures, we highlight the efficacy of the well-generalizing EPFES in estimating the nonlinear system even for large search spaces. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.

Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining a proper size of the to-be-learned dictionary is crucial for both precision and efficiency of the process, while most of the existing dictionary learning algorithms choose the size quite arbitrarily. In this paper, a novel regularization method called the Grouped Smoothly Clipped Absolute Deviation (GSCAD) is employed for learning the dictionary. The proposed method can simultaneously learn a sparse dictionary and select the appropriate dictionary size. Efficient algorithm is designed based on the alternative direction method of multipliers (ADMM) which decomposes the joint non-convex problem with the non-convex penalty into two convex optimization problems. Several examples are presented for image denoising and the experimental results are compared with other state-of-the-art approaches.