Acumen Research and Consulting, recently published report "Wearable Artificial Intelligence (AI) Market - Global Industry Analysis, Size, and Forecast, 2019 - 2026" LOS ANGELES, May 03, 2019 (GLOBE NEWSWIRE) -- The Global Wearable Artificial Intelligence (AI) Market size is estimated to grow at CAGR above 27 % over the forecast time frame 2019-2026 and reach the market value around USD 185 billion by 2026. The key drivers for development will be increased demand for AI assistants, increased operations in the Healthcare industry, the emergence of IoT and the integration of wireless technologies and the growth of wearable component technology. As the majority of intelligent wearable equipment lacks basic safety mechanisms, an increasing concern for data security in smarts is preventing the growth of the wearable artificial intelligent market. Moreover, the cost of production is high and the consumption of batteries is limited. In the forecast period, the earwear market is projected to grow at more than 43 percent.

When the folks at the Jockey Club released their list of over 42,000 registered racehorses in an effort to assist owners in "identifying an appropriate name for their Thoroughbreds," they probably had no idea that the information would be fed to a neural network that would try to put them out of a job. In the latest post for AIWeirdness, artificial intelligence savant Janelle Shane fed the Jockey Club's dataset to two of her neural networks and tasked them with devising names for racehorses. Turns out the neural networks are pretty good at the job, coming up with names like Long Range Toddy, Gunmetal Gray, Maximus Mischief, Bankit, Network Effect, and--just kidding! Those are all names of actual racehorses running in the Kentucky Derby on Saturday. It just shows where the bar is set when it comes to the odd process of naming thoroughbreds.

Artificial intelligence–connected sensors, tracking wristbands, and data analytics: We've seen this type of tech pop up in smart homes, cars, classrooms, and workplaces. And now, we're seeing these types of networked systems show up in a new frontier--prisons. Specifically, China and Hong Kong have recently announced that their governments are rolling out new artificial intelligence (AI) technology aimed at monitoring inmates in some prisons every minute of every day. In Hong Kong, the government is testing Fitbit-like devices to monitor individuals' locations and activities, including their heart rates, at all times. Some prisons will also start using networked video surveillance systems programmed to identify abnormal behavior, such as self-harm or violence against others.

The magic of artificial intelligence began with realistic images of human faces, food, and Airbnb home stays but now it seems to be reaching new heights as a Kyoto-based firm, Data Grid has developed complete human bodies based on the images of thousand other Japanese celebrities and models. Every model that you can see in the picture and video was generated from scratch with the help of DataGrid's neural network. Although the company didn't reveal much details about the process in their demo but the idea indeed has a lot of potential. These virtual models can be considered as an ideal alternative for brands and online stores who currently spend a hefty amount on photo shoots every now and then, in order to showcase their products in the best way possible. Also read: This Is How You Can Make Yourself Invisible for AI Surveillance Systems This all wasn't achieved at first attempt as previously DataGrid started off by developing images that looked like the Japanese celebrities.

Will machines ever learn to be fair? Who will decide what that means? And what will the consequences be if they're not made to be fair? Artificial intelligence is increasingly being used by organizations to make decisions about how people are treated. AIs aren't human, but in this series on AI Fairness, we examine how they can, and should, be made to behave humanely.

Taking to the stage in Las Vegas last week for the Dell Technologies World summit, the computer giant founder shared a utopian dream for a data-driven future where technology is used as a force for good. For him, unprecedented recent advances in artificial intelligence (AI) mean a lot more than just smarter phones and faster Game of Thrones downloads. The rollout of 5G networks and predicted exponential growth in computing power could lead to seismic benefits for humanity, he claims. "From life expectancy and vaccinations to infant mortality to literacy in school, humankind has made astonishing progress during the last three and a half decades," he said. "The next three decades will hold even more progress.

The idea of Kickstarter first formed in the mind of Perry Chen in 2001. A native New Yorker, Chen was 25, living in New Orleans and working as a musician. He wanted to bring a pair of DJs he loved down to perform during Jazz Fest. He sorted out a venue, organised things with their management, but in the end the event didn't happen – Chen didn't have the funds to pay for the show if not enough people turned up. In his frustration, a thought occurred to him: "What if people could go to a website and pledge to buy tickets for a show? And if enough money was pledged, they would be charged and the show would happen. Over the years that followed, Chen held on to that simple idea. He moved back to New York in 2005, still more intent on making music than starting an internet company – he had no background in technology – but the thought wouldn't go away. He became friends with a music journalist, Yancey Strickler, who got sold on the idea, too. They talked about it with, Charles Adler, a designer and DJ, and the three of them formulated ideas and spoke to mates of mates who knew code or to people who might help fund such a thing. Eventually, in April 2009, eight years after the idea had first come to Chen, the three of them launched their website and waited at their laptops to see if other people thought it was a good idea too. In the first few days, a few emails trickled in, from people pitching ideas, wondering how the thing might work. And then, after a couple of weeks, a young singer-songwriter from Athens, Georgia, launched a project to fund her album, Allison Weiss Was Right All Along. "My name is Allison Weiss and I'm recording a new EP this summer.

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already in one place}, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with "minimal" communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing.

General approach for time series classification is splitting time series to equal size Multivariate time series classification is a high segments using a fixed-length sliding window and extracting value and well-known problem in machine learning handcrafted features from the segments for classification community. Feature extraction is a main step tasks. The features are usually statistical measurements or in classification tasks. Traditional approaches employ features extracted from another domain such Fourier and handcrafted features for classification while Wavelet domain (Jiang & Yin, 2015; Ravi et al., 2017; Lin convolutional neural networks (CNN) are able et al., 2003). In multivariate time series classification, commonly, to extract features automatically. In this paper, information is extracted separately from each variate, we use dilated convolutional neural network for and the features are concatenated for the classification task multivariate time series classification.

We develop a vector space semantics for verb phrase ellipsis with anaphora using type-driven compositional distributional semantics based on the Lambek calculus with limited contraction (LCC) of J\"ager (2006). Distributional semantics has a lot to say about the statistical collocation-based meanings of content words, but provides little guidance on how to treat function words. Formal semantics on the other hand, has powerful mechanisms for dealing with relative pronouns, coordinators, and the like. Type-driven compositional distributional semantics brings these two models together. We review previous compositional distributional models of relative pronouns, coordination and a restricted account of ellipsis in the DisCoCat framework of Coecke et al. (2010, 2013). We show how DisCoCat cannot deal with general forms of ellipsis, which rely on copying of information, and develop a novel way of connecting typelogical grammar to distributional semantics by assigning vector interpretable lambda terms to derivations of LCC in the style of Muskens & Sadrzadeh (2016). What follows is an account of (verb phrase) ellipsis in which word meanings can be copied: the meaning of a sentence is now a program with non-linear access to individual word embeddings. We present the theoretical setting, work out examples, and demonstrate our results on a toy distributional model motivated by data.