Google's DeepMind algorithm just got one step closer to behaving like a human. Human smart DeepMind has gotten scarily smart in recent months – and we're not just talking "computer smart," but more like "human smart."

Computers are becoming increasingly self-aware. Technological progress based on complex algorithms, combined with greater computing and processing power, has removed several key constraints. Through machine learning and deep learning techniques, computer scientists are now able to train computers to recognize patterns when presented with new image and audio files. Already, intelligent systems are capable of moderately accurate transcriptions from video feeds, as demonstrated by artists in Amsterdam, and we can expect the accuracy rate to continue to rise. Machine learning is still a nascent technology, but self-learning machines have huge potential to help scientists and researchers by identifying trends in the data from lab experiment instrumentation for materials innovation.

HELSINKI--(BUSINESS WIRE)--Tieto (HEX:TIE1V) (STO:TIEN) has appointed Artificial Intelligence as a member of the leadership team of its new data-driven businesses unit. The AI, called Alicia T, is the first AI to be nominated to a leadership team in an OMX-listed company. AI will help the management team to become truly data-driven and will assist the team in seeking innovative ways to pursue the significant opportunities of the data-driven world. Tieto established its new data-driven businesses unit in July 2016 to help Nordic organisations to create innovative data-driven services and new business models. In support of this goal, Tieto is also investigating the opportunities AI can present to the new unit's leadership team.

This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm can handle both general convex (possibly non-smooth) regularizers and general convex constraints. When the empirical data loss is strongly convex, we establish linear convergence rate, give explicit expressions for step-size choices that guarantee convergence to the optimum, and bound the associated convergence factors. The expressions have an explicit dependence on the degree of asynchrony and recover classical results under synchronous operation. Simulations and implementations on commercial compute clouds validate our findings.

Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond to context-dependent tied states or senones. The present work addresses some limitations of GMM-HMM senone alignments for DNN training. We hypothesize that the senone probabilities obtained from a DNN trained with binary labels can provide more accurate targets to learn better acoustic models. However, DNN outputs bear inaccuracies which are exhibited as high dimensional unstructured noise, whereas the informative components are structured and low-dimensional. We exploit principle component analysis (PCA) and sparse coding to characterize the senone subspaces. Enhanced probabilities obtained from low-rank and sparse reconstructions are used as soft-targets for DNN acoustic modeling, that also enables training with untranscribed data. Experiments conducted on AMI corpus shows 4.6% relative reduction in word error rate.

It could be mistaken for a hovering UFO, but this DIY flashlight consists of ten separate 100-watt LEDs mounted on a Freefly Alta drone. Daniel Riley of Stratus Productions designed'the world's brightest flashlight' last year and recently updated the creation with new lights that are much more accurate and brighter. The filmmaker replaced the cheap eBay LEDs with Yuji high CRI LED chips, which illuminates the night sky with a flying spotlight. Daniel Riley designed'the world's brightest flashlight' last year and recently updated by replacing the cheap eBay LEDs with Yuji high CRI LED chips, which are bright enough to light up the sky in the dead of night Riley's original creation was awarded the title of'world's brightest flashlight' in 2015 by the Guinness Book of World Records. The design is an ultra-powerful 1000W light that puts out a whopping 900000 lumens. Recently, he replaced the cheap eBay LEDs with Yuji high CRI LED chips, which are bright enough to light up the sky in the dead of night.

More recently, research has suggested that quantum effects could offer similar advantages for the emerging field of quantum machine learning (a subfield of artificial intelligence), leading to more intelligent machines that learn quickly and efficiently by interacting with their environments. In a new study published in Physical Review Letters, Vedran Dunjko and coauthors have added to this research, showing that quantum effects can likely offer significant benefits to machine learning. "The progress in machine learning critically relies on processing power," Dunjko, a physicist at the University of Innsbruck in Austria, told Phys.org. "Moreover, the type of underlying information processing that many aspects of machine learning rely upon is particularly amenable to quantum enhancements. As quantum technologies emerge, quantum machine learning will play an instrumental role in our society--including deepening our understanding of climate change, assisting in the development of new medicine and therapies, and also in settings relying on learning through interaction, which is vital in automated cars and smart factories."

For years, the retail industry has been talking about the rise of mobile and how it may disrupt everything. But according to a new report released today by App Annie -- an app analytics and market intelligence company -- the future is already here. Mobile, especially in the U.S., has already taken over. Looking at both online-first and "bricks-and-clicks" retailers, the study shows that, while mobile is affecting everyone, the U.S. is apparently leading the way. "Most surprising is how well U.S. bricks-and-clicks retailers are doing relative to others outside of the U.S.," Danielle Levitas, SVP of research and marketing communications at App Annie, told me.

What happens when two human political journalists compete against a computer over which can do the best job predicting the issues that will dominate the news in the presidential election? Well, you are about to find out. The two humans and the computers each got to predict five issues per presidential candidate that would get the most coverage in the news and on the blogs between Sept. 12 and Oct. 12. (The time frame covers only a few days of the release of the Donald Trump's lewd comments about women. So the final list isn't dominated by that controversy.) The two humans are Simon Maloy of Salon.com and Jonah Goldberg of the National Review.

The best books about creativity can simultaneously spark new ideas in the reader while also giving them a compelling narrative to follow. As the White House staff polishes the silver for what is likely to be the last state dinner of the Obama administration, it's worth taking a look back at how the first couple -- the youngest to occupy the White House since JFK and Jackie -- has shaped the institution of the state dinner. We talk a lot about the U.S. publishers who are on Flipboard, but we've also been busy in Europe, where our readership has surpassed 20 million monthly active readers. For Syrian students now living in Turkey, the path through higher-education is far from smooth. For the first time in history, Washington has accused a foreign government of trying to influence the US election.