Artificial Intelligence has become a topic of intense interest throughout the cloud developer and enterprise IT communities. Accordingly, attendees at the upcoming Cloud Expo @ThingsExpo at the Javits Center in New York, June 6-8, will find fresh new content in a new track called Cognitive Computing Artificial Intelligence, Machine Learning, Deep Learning. We are now in the third, and most successful, generation of groundbreaking Artificial Intelligence (AI) research and deployments. With it comes new capabilities for Machine Learning (ML) and Deep Learning (DL), as organizations of all sizes work to create and use new insights from their existing operations while also seeking to develop new ways of making their systems and their organizations much smarter. Cloud Expo is still accepting submissions for this new track, so please visit www.cloudcomputingexpo.com for the latest information.

H2O.ai, an AI company that provides industry-leading data products for enterprise businesses, today announced it has been named by Gartner, Inc., the leading provider of research and analysis on the global machine learning industry, as a Representative Provider of deep learning platform provider that allows users to create their own deep learning solutions. Gartner's January 2017 Innovation Insight for Deep Learning report listed H2O's Deep Water as one provider of deep learning platforms, placing it alongside other offerings from Caffe, Ersatz Labs, Facebook's Torch, Google TensorFlow, Intel's Nervana, The Microsoft Cognitive Toolkit, Theano and Skymind's Deeplearning4j. "We believe our inclusion in this report validates and enforces our standing in the industry," said SriSatish Ambati, co-founder and CEO of H2O.ai. "We're excited to continue expanding our suite of deep learning solutions." H2O.ai launched in 2011 with the goal of democratizing data science by open sourcing deep learning and AI for everyone.

BARCELONA, Spain--(BUSINESS WIRE)--Today, global skincare brand Olay celebrated its Mobile World Congress debut with the global launch of Olay Skin Advisor, a new platform designed to help women better understand their skin and find the products best-suited to their personal skincare needs. Rooted in a suite of artificial intelligence technologies, Olay Skin Advisor marks the first application of deep learning in the beauty industry, arming women with the knowledge they need to care for their skin and better navigate the often-confusing beauty aisle. "Shopping for skincare has never been more overwhelming, as women are faced with thousands of products and promises," said Dr. Frauke Neuser, Principal Scientist for Olay. "Olay's research shows that browsing the shelf is the #1 purchase influencer for women, yet 1/3 of women do not find what they are looking for. We saw an opportunity to help women understand their skin better than ever, before they even step foot in the store. Our solution is Olay Skin Advisor, which uses artificial intelligence to deliver a smart skin analysis and personalized product recommendation, taking the mystery out of shopping for skincare products."

Amazon is setting its sights on another industry, this time it's call centers. In doing so, the company plans to use Alexa, Lex and also Polly in the said market. According to an article at Geek Wire, Amazon Web Services is developing a suite of cloud-based tools to sell to the enterprises that would manage call centers. Aside from its big plans of invading the call center industry, the company plans to incorporate its digital assistant Alexa to answer some questions on the phone as well as via text message. Amazon also plans to employ Lex, a chatbox building service that uses deep-learning technology similar to Alexa, and a text-to-speech program named Polly as well.

How they're different and why are they all essential to the Internet of Things. After all, it's been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like "Machine Learning" and "Deep Learning," sometimes used interchangeably with artificial intelligence. I'll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they're different. Then, I'll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion.

Baidu Research presents Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. The biggest obstacle to building such a system thus far has been the speed of audio synthesis – previous approaches have taken minutes or hours to generate only a few seconds of speech. We solve this challenge and show that we can do audio synthesis in real-time, which amounts to an up to 400X speedup over previous WaveNet inference implementations. Synthesizing artificial human speech from text, commonly known as text-to-speech (TTS), is an essential component in many applications such as speech-enabled devices, navigation systems, and accessibility for the visually-impaired. Fundamentally, it allows human-technology interaction without requiring visual interfaces.

The computing industry progresses in two mostly independent cycles: financial and product cycles. There has been a lot of handwringing lately about where we are in the financial cycle. Financial markets get a lot of attention. They tend to fluctuate unpredictably and sometimes wildly. The product cycle by comparison gets relatively little attention, even though it is what actually drives the computing industry forward. We can try to understand and predict the product cycle by studying the past and extrapolating into the future.

Last week, MIT showed off Smashbot, an AI trained to beat humans at Super Smash Bros Melee. Playing as Captain Falcon, Smashbot beat a number of highly-ranked players. Smashbot was built by a team led by Vlad Firoiu. Through a combination of deep learning algorithms and practice against the in-game AI, Smashbot has learned how to play the game from scratch. It's not a real surprise it can outdo humans, as it has faster reaction times.

We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU's architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures' hyperparameters in a Bayesian optimization scheme for both synthetic and real-world tasks.