Artificial Intelligence is most powerful when it draws from a multitude of devices, data sources and advanced analytics. Imagine the rapid creation of an anti-virus based on the simultaneous analysis of data from early cases of flu in Europe, and clinical trials in the US, and lab tests in Japan. This is data that will be drawn from multiple devices, operating systems and data bases across the globe. This scale of information processing will also demand a new era of computational power. Even a relatively simple AI computation will need to draw at will from tens of thousands of computers and servers across the globe.

Dating app Tinder relies on algorithms to decide which photos users see. As algorithms become entrenched into society, the debate about their effects rages on. In essence, algorithms are sequences of instructions used to solve problems and perform functions in computer programming. As mathematical expressions, algorithms existed long before modern computers. While they vary in application, all algorithms have three things in common: clearly-defined beginning and ending points, discrete sets of "steps," and design meant to address a specific type of problem.

The Cooper Hewitt Design Museum houses an impressive collection of designed objects that chronicle the history and significance of design in our evolving world. These objects range from unrealized works of architecture to handwoven textiles from Africa to graphic designed posters that reflect the culture and pulse of humanity of their time. The museum is housed in the former mansion of Andrew Carnegie. Upon its completion in 1901, the sixty-four room mansion was the first private residence in the United States to have a structural steel frame that allowed for more expansive spaces and a feeling of lightness. The Carnegie Mansion was also the first private residence to have a residential elevator, central heating, and a precursor to central AC.

I am really excited to announce that the general availability of the Azure N-Series will be December 1st, 2016. Azure N-Series virtual machines are powered by NVIDIA GPUs and provide customers and developers access to industry-leading accelerated computing and visualization experiences. I am also excited to announce global access to the sizes, with N-series available in South Central US, East US, West Europe and South East Asia, all available on December 1st. We've had thousands of customers participate in the N-Series preview since we launched it back in August. We've heard positive feedback on the enhanced performance and the work we have down with NVIDIA to make this a completely turnkey experience for you.

Community detection has become very popular in network analysis the last decades. Its range of applications include social sciences, biology and complex systems, such as the worldwide-web, protein-protein interactions, or social networks (see [5] for a thorough exposition of the topic). To tackle this problem, spectral approaches have been introduced in [12] or [18], inspired from the so-called spectral clustering problem (see [10]). However, the treatment of larger and larger graphs has been investigated and modularity-based algorithms has been proposed. This class of algorithms maximize a quality index called modularity, introduced in [13].

We are interested in learning customers' video preferences from their historic viewing patterns and geographical location. We consider a Bayesian latent factor modeling approach for this task. In order to tune the complexity of the model to best represent the data, we make use of Bayesian nonparameteric techniques. We describe an inference technique that can scale to large real-world data sets. Finally we show results obtained by applying the model to a large internal Netflix data set, that illustrates that the model was able to capture interesting relationships between viewing patterns and geographical location.

Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measured in human cancer cell lines. Real-world cancer cell lines contain noise, which degrades the performance of machine learning algorithms. This problem is rarely addressed in the existing approaches. In this paper, we present a noise-filtering approach that integrates techniques from numerical linear algebra and information retrieval targeted at filtering out noisy cancer cell lines. By filtering out noisy cancer cell lines, we can train machine learning algorithms on better quality cancer cell lines. We evaluate the performance of our approach and compare it with an existing approach using the Area Under the ROC Curve (AUC) on clinical trial data. The experimental results show that our proposed approach is stable and also yields the highest AUC at a statistically significant level.

The method presented extends a given regression neural network to make its performance improve. The modification affects the learning procedure only, hence the extension may be easily omitted during evaluation without any change in prediction. It means that the modified model may be evaluated as quickly as the original one but tends to perform better. This improvement is possible because the modification gives better expressive power, provides better behaved gradients and works as a regularization. The knowledge gained by the temporarily extended neural network is contained in the parameters shared with the original neural network. The only cost is an increase in learning time.

Money makes the world go round, or so they say. Payments, investments, insurance and billions of transactions are the beating heart of a fractal economy, which echoes the messy complexity of natural systems, such as the growth of living organisms and the bouncing of atoms. Financial systems are larger than the sum of their parts. The underlying rules that govern them might seem simple, but what surfaces is dynamic, chaotic and somehow self-organizing. And the blood that flows through this fractal heartbeat is data.

Artificial intelligence software can now help cops spot new or previously unknown child sexual abuse media and prosecute offenders. The toolkit, described in a paper published in Digital Investigation, automatically detects new child sexual abuse photos and videos in online peer-to-peer networks. The new approach combines automatic filename and media analysis techniques in an intelligent filtering module, which can identify new criminal media and distinguish it from other media being shared, such as adult pornography. Spotting newly produced media online can give law enforcement agencies the fresh evidence they need to find and prosecute offenders. "Identifying new child sexual abuse media is critical because it can indicate recent or ongoing child abuse," said lead study author Claudia Peersman from Lancaster University.