Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference procedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to-program transformations. These transformations perform exact inference as well as generate probabilistic programs that compute expectations, densities, and MCMC samples. The resulting inference procedures are about as accurate and fast as other probabilistic programming systems on real-world problems.

The lip-syncing system, developed by researchers at the University of Washington (UW), works by converting audio files of an individual's speech into realistic mouth shapes. Then, by building on research from UW's Graphics and Image Laboratory team with a new mouth synthesis technique, the researchers were able to superimpose and blend realistic mouth shapes and textures on an existing video of that person. Then, by building on research from UW's Graphics and Image Laboratory team with a new mouth synthesis technique, the researchers were able to superimpose and blend realistic mouth shapes and textures on an existing video of that person. 'We're simply taking real words that someone spoke and turning them into realistic video of that individual,' said Dr Dr Steve Seitz, a co-author of the research However, in the future, the researchers want to improve the algorithms so they can generalize across situations and recognize a person's voice and speech patterns with less data – for example just an hour of video to learn from.

To dance is human; people of all ages and levels of motor ability express movements in response to music. Professional dancers exert a great deal of creativity and energy toward developing their skills and different styles of dance. How dancers move in beautiful and sometimes unexpected ways can delight, and the synchrony between dancers moving together can be entrancing. To us as a neuroscientist and biomechanist (Lena), and a rehabilitation scientist and dancer (Madeleine), understanding the complexities of motor skill in a ballet move, or the physical language of coordination in partner dance, is an inspiring and daunting challenge. Understanding how dancers move has important real-world implications, too. In our work, we're studying gait and balance in different populations, as well as how holding hands – such as in partner dance – can actually help people walk and balance better.

CHICAGO--(BUSINESS WIRE)--According to Bloomberg, "The Street is paved with gold, which presents a challenge to deploying the record $1.5 trillion in dry powder, or investable funds, private equity firms are holding globally." At The Alliance of M&A Advisors' Summer Conference, taking place July 19-21 at the Drake Hotel in Chicago, buyers and sellers in the private middle market will come together and make deals at this unprecedented time of change. A featured portion of this 19th annual M&A conference features Artificial Intelligence (AI) and Machine Learning (ML) experts sharing their experience gained from research, consulting, and analysis of more that 20,000 deals offering game-changing tech innovations that will change how deal making is done forever. The Alliance is showcasing a new Center for Excellence platform hosted by Dr. Kiran Garimella with a special session moderating a discussion with leaders from global solution providers: D&B Hoovers and ansarada. Dr. Kiran Garimella, co-founder and columnist, is a former Global CIO and Chief Architect at GE, VP for Business Process Management at Software AG, and an expert in building Centers of Excellence.

To ride the rising wave of AI, financial services companies will have to navigate evolving standards, regulations and risk dynamics--particularly regarding data rights, algorithmic accountability and cybersecurity. The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics.

By Ashim Roy For a population of 128 crores, India has 10.12 lakh doctors. Of these, a recent KPMG report says, 74 per cent cater to only a third of the urban residents. In other words, there are only about 2.63 lakh doctors catering to the majority of Indians residing on the fringes of urban settlements and in rural areas. On the other hand, India has more than 90 crore mobile phone connections. Studies show that in 2008 the number of devices connected to the Internet was more than the number of people on Earth!

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Brain training apps such as Lumosity and Elevate are supposedly useful in order to keep your cognitive skills sharp, but there's been quite a bit of doubt cast on whether they are actually useful. Now, scientists led by University of Pennsylvania psychologist Joseph Kable are chiming in. As published in The Journal of Neuroscience, the team "found no evidence that cognitive training influences neural activity during decision-making, nor did we find effects of cognitive training on measures of delay discounting or risk sensitivity." The team's interest in the issue was in regard to instant gratification. Specifically, they wanted to see whether cognitive training could change behavior, leading users to prefer delayed or less risky rewards.

Why do 97% of marketing influencers believe the future of digital marketing will involve human marketers working with machine learning-powered automation? Thought leaders in PPC, social and mobile marketing explain in this in-depth survey. Are disciplines such as search engine marketing, social and mobile marketing all trending towards a fully automated world where artificial intelligence (AI) robots take over our jobs? In a survey of top influencers in online marketing with expertise in paid search, social and mobile, 97% of respondents suggested that the future of marketing will actually be smart marketers working hand-in-hand with machine learning-based automation solutions. To help us understand the growing role of machine learning in marketing, we spoke with some of the top influencers in the space, including Michael Brenner (@brennermichael) of Marketing Insider Group, Serena Ehrlich (@serena) of BusinessWire, Adelyn Zhou (@adelynzhou) of TOPBOTS and Chris Messina (@chrismessina), creator of the hashtag - among others. Selected responses to the survey are below. Interested in learning more about how new technology and academic disciplines such as machine learning and data science are changing digital forever?

A new study has found it's actually surprisingly easy to model how humans make them, opening a potential avenue to solving the conundrum. In the face of such complexities, programming self-driving cars to mimic people's instinctive decision-making could be an attractive alternative. For a start, building models of human behavior simply required the researchers to collect data and feed it into a machine learning system. By basing the behavior of self-driving cars on a model of our collective decision making we would, in a way, share the responsibility for the decisions they make.