Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-time. Prompted by this observation, we study a convex optimization scheme for block-sparse recovery from linear measurements. To obtain linear sketches, we use expander matrices, i.e., sparse matrices containing only few non-zeros per column. Hitherto, to the best of our knowledge, such algorithmic solutions have been only studied from a non-convex perspective. Our aim here is to theoretically characterize the performance of convex approaches under such setting. Our key novelty is the expression of the recovery error in terms of the model-based norm, while assuring that solution lives in the model. To achieve this, we show that sparse model-based matrices satisfy a group version of the null-space property. Our experimental findings on synthetic and real applications support our claims for faster recovery in the convex setting -- as opposed to using dense sensing matrices, while showing a competitive recovery performance.

GTC will showcase the vital role GPU technology plays in some of the industry's biggest trends, including artificial intelligence, virtual reality and self-driving cars. About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. Certain statements in this press release including, but not limited to, statements as to: the impact of GPU technology in artificial intelligence, virtual reality and self-driving cars are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended October 25, 2015.

GTC will showcase the vital role GPU technology plays in some of the industry's biggest trends, including artificial intelligence, virtual reality and self-driving cars. About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. Certain statements in this press release including, but not limited to, statements as to: the impact of GPU technology in artificial intelligence, virtual reality and self-driving cars are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended October 25, 2015.

SANTA CLARA, CA--(Marketwired - Mar 31, 2016) - NVIDIA (NASDAQ: NVDA) today announced that the three keynote addresses at its upcoming GPU Technology Conference (GTC) will be webcast live on the NVIDIA blog. GTC will showcase the vital role GPU technology plays in some of the industry's biggest trends, including artificial intelligence, virtual reality and self-driving cars. This year's event will feature more than 500 sessions with speakers from Alibaba, Audi, Baidu, Boeing, Facebook, Google, Microsoft, Oracle, Pixar, Raytheon, Samsung, Siemens, SpaceX and Twitter, among many others. GTC also includes a daylong event, the Emerging Companies Summit, on April 6, focused on GPU-based startups. Nearly 100 startups will participate this year, including an onstage competition among a dozen companies vying for 100,000.

Bart Peintner has been closely involved with important developments in artificial intelligence through its recent resurgence. At SRI, one of the world's hotbeds of AI research, he pursued work that pressed the limits of natural language processing and user-behavior modeling. Now, as CTO and co-founder of startup Loop AI Labs, he is furthering the cause of unsupervised machine intelligence -- also known as deep machine learning -- for applications. It's important because teaching machines to do human's work can be labor intensive. When did you start Loop AI Labs, and what was the underlying goal?

P-values for coefficients of cylinders, horsepower and acceleration are all greater than 0.05. This means that the relationship between the dependent and these independent variables is not significant at the 95% certainty level. I'll drop 2 of these variables and try again. High p-values for these independent variables do not mean that they definitely should not be used in the model. It could be that some other variables are correlated with these variables and making these variables less useful for prediction (check Multicollinearity).

The 6sense patented approach enables, in certain embodiments, the collection of intent and/or static, profile fit data, transformation of unstructured data into structured data, calculation of buyer intent signals, mapping of unknown prospects to known buyers, and the ability to determine where buyers are in their journey (awareness, consideration, decision, purchase). The patent supports what 6sense views as the future of B2B marketing and sales: Omni-channel connectivity, visibility, attribution and the predictions to target the right audience at the right time, when they have demonstrated a need and propensity to purchase. "Among our accomplishments over the last several years, receiving this patent tops the list. After years of hard work and a five-year-long filing process, I hope this patent communicates to our customers, prospects and the wider industry that we truly were the first to market," said Kahlow. "When we started this journey, I knew we were onto something that no one else was thinking about, let alone doing yet. Aside from what this milestone means for 6sense, if it can serve as an inspiration to any woman at a time when women hold only a small fraction of technology patents, I've succeeded beyond my wildest dreams; I know at the core of it all, my purpose in life is to inspire women and girls."

The robots are coming, or so media have proclaimed in recent months. The world of work is about to undergo a revolution as advances in technology mean that many jobs humans do now will likely be done by machines instead in a matter of years. How many roles will go and what sectors will be most affected is open to debate but it seems certain widespread change is upon us. According to a World Economic Forum (WEF) report published in January, more than seven million jobs are at risk from advances in technology in the world's largest economies over the next five years. If anything this is a conservative estimate.

The artificial intelligence (AI) 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. The machine learning technology is expected to account for the largest share of the overall Artificial Intelligence (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.

As the tech world pivots on game-changing applications, data scientists rise to the occasion. Such is the case with Holden Karau, principal software engineer of Big Data at IBM and coauthor of Learning Spark. When asked about the current renovations within Spark, Karau said she sees this time as an "opportunity to get rid of dead weight" by streamlining certain processes. For example, she cited getting functional and relative queries to talk to each other within Spark. Two area of expansion include sequencing and machine learning.