Welcome to the Applied Materials Earnings Conference Call. During the presentation, all participants will be in a listen-only mode. Afterwards you will be invited to participate in a question-and-answer session. As a reminder, this conference is being recorded. I'd now like to turn the conference over to Michael Sullivan, Vice President of Investor Relations. In a moment, we'll discuss the results for our third quarter which ended on July 31. Joining me are Gary Dickerson, our President and CEO; and Bob Halliday, our Chief Financial Officer. Before we begin, let me remind you that today's call contains forward-looking statements including Applied's current view of its industries, performance, products, share positions, profitability and business outlook. These statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements, and are not guarantees of future performance.

In the year 1820, a person could expect to live less than 35 years, 94% of the global population lived in extreme poverty, and less that 20% of the population was literate. Today, human life expectancy is over 70 years, less that 10% of the global population lives in extreme poverty, and over 80% of people are literate. These improvements are due mainly to advances in technology, beginning in the industrial age and continuing today in the information age. There are many exciting new technologies that will continue to transform the world and improve human welfare. Here are eleven of them.

Members of Rajat Monga's team at Google will be teaching tutorials on deep learning with TensorFlow at Strata Hadoop World in Beijing (August 4th) and NYC (September 27th). Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies. There's not going to be too many areas left that run without machine learning that you can program.

Artificial intelligence can find, map poverty, researchers ...

The cities of the future will be huge and super-dense -- but will they also be alive? Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence? People have speculated before about the idea that the Internet might become self-aware and turn into the first "real" A.I., but could it be more likely to happen to cities, in which humans actually live and work and navigate, generating an even more chaotic system? It's either my worst nightmare or the dawn of a wonderful new future, but scientists are… Read more Read more As cities become more networked and their mixture of urban infrastructure and surveillance infrastructure becomes more complex, eventually we'll have to build cities that can think for themselves. People have speculated about the potential for computer systems to help in urban planning forever, including papers about the use of "fuzzy logic" to automate the decision-making process and A.I. solutions for land use planning, and the an A.I. "spatial decision support system."

Artificial intelligence can find, map poverty, researchers ... Ai Weiwei exhibit extended until Sept. 11 at Warhol AI passenger carrying gold bars worth over Rs 2.5 crore held

Artificial intelligence can find, map poverty, researchers ... AI passenger carrying gold bars worth over Rs 2.5 crore held

This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares (RLS) algorithm. We estimate abnormal flows as outlier sparse flows via sparsity maximization in the underlying under-constrained linear-inverse problem. A major advantage is that our algorithm estimates normal flows by low-dimensional matrices with time-directional features as well as the spatial correlation of multiple links without using the past observed measurements and the past model parameters. Extensive numerical evaluations show that the proposed algorithm achieves faster convergence per iteration of model approximation, and better volume anomaly detection performance compared to state-of-the-art algorithms.

The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals. In contrast, a new trans-dimensional random field (TRF) LM has been recently introduced to show superior performances, where the whole sentence is modeled as a random field. In this paper, we examine how the TRF models can be interpolated with the NN models, and obtain 12.1\% and 17.9\% relative error rate reductions over 6-gram LMs for English and Chinese speech recognition respectively through log-linear combination.

"Seven hundred years after glasses were invented there are still 2.5 billion people in the world with poor vision and no access to vision correction," says Hong Kong philanthropist James Chen. Chairman of his family's Nigeria-based manufacturing company, Wahum Group, Chen is funding a contest called the Clearly Vision Prize that will award a total of 250,000 to projects that improve eyesight, especially in poor countries. Thirty-six semifinalists were announced this week (the five winners will be awarded September 15). Among the contenders: 3D printed eyeglass frames, drones that deliver medical supplies, and several smartphone-based technologies. Some of the smartphones help nonexperts test vision, and one uses artificial intelligence to "see" for blind people. The Clearly Vision semifinalists represent just a sampling of the smartphone projects fighting vision loss, a growing field that is bringing critical care to remote regions far from hospitals and doctors offices.