We already know that algorithms are ruling the world. Consider it kind of God or Ghost from the machine powering them all, for good or evil - you decide. Soon Machine Learning Algorithms will be able to accurately guide forward-looking business decisions and reveal behaviors never before seen. Gartner says that Automation and Machine Learning will shape the future of governments. Artificial Intelligence and Machine Learning is the Trend no 1 in Garner's Top 10 Strategic Technology Trends for 2017 Can computers really learn mom's art of cucumber sorting?

Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at 20th Cloud Expo, Ed Featherston, director / senior enterprise architect at Collaborative Consulting, will discuss the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine. Speaker Bio Ed Featherston is a director/senior enterprise architect at Collaborative Consulting. He brings 37 years of technology experience in designing, building, and implementing large complex solutions. He has significant expertise in systems integration, Internet/intranet, and cloud technologies, Ed has delivered projects in various industries, including financial services, pharmacy, government and retail.

TOKYO SoftBank Group Chairman and CEO Masayoshi Son showed me a photo on his iPhone and said, "I will never forget this scene for the rest of my life." The photo showed a group of white yachts in a bay under an endless blue sky in Marmaris, a port town in southern Turkey. Son swiped the screen, and a selfie photo of him in chino pants and a casual shirt appeared. Several hours before the photos were taken on July 4, Son met Simon Segars, CEO of ARM Holdings, and Stuart Chambers, chairman of the British computer chip design company, on the second floor of a restaurant overlooking the bay. Chambers had arrived in Marmaris, a popular resort town, after receiving an unexpected phone call from Son while yachting with his family in the Mediterranean Sea.

For example, if you were to train solely on content without any view into how the system is making its decisions, that system might learn that the phrase "Greek bank" is negative, due to the deluge of negative stories associated with Greek banks over the years, even though the phrase is not inherently negative. This is a common problem with systems that attempt to analyze sentiment with a single model and will skew results over time. The Lexalytics HSDTrainer can consume any text corpus that has been appropriately marked up for sentiment, and then return a list of phrases and suggested scores for that text corpus, allowing analysts to both rapidly and transparently train sentiment. Emoji Analytics -- With Salience 6.2, social marketers can now analyze the meaning and sentiment of content that includes the latest emojis released in Unicode 9.0. For example, if a food manufacturer releases a new product that elicits social media posts with the new "nauseated face" emoji, Lexalytics can score the content as negative and alert the customer. Conversely, those same marketers can search for anything that mentions "nausea," and that emoji will return a hit.

Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, it is relatively insufficient to empower the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability. In semi-supervised learning, we use the predictions of a max-margin classifier as the missing labels instead of performing full posterior inference for efficiency; we also introduce additional max-margin and label-balance regularization terms of unlabeled data for effectiveness. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objectives in different settings. Empirical results on various datasets demonstrate that: (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; (2) in supervised learning, mmDGMs are competitive to the best fully discriminative networks when employing convolutional neural networks as the generative and recognition models; and (3) in semi-supervised learning, mmDCGMs can perform efficient inference and achieve state-of-the-art classification results on several benchmarks.

Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training methods. In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images. This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods.

When technologists think of "supercomputer" they usually imagine government-owned computers like China's TaihuLight or public-private partnerships like the DOE-IBM's planned Summit. Private in-house supercomputers are much rarer. That makes Nvidia's new supercomputer, the DGX SaturnV, even more surprising. Not only has Nvidia revealed its own supercomputer, but the machine cracked TOP500's list of the 500 most powerful computers and took the number one spot as the world's most energy-efficient supercomputer. It relies on numerous Nvidia's 125 DGX-1s, the "AI supercomputer in a box" units built for deep learning.

Ever since Facebook, the world's largest social networking site made its Messenger mobile texting app available, businesses have begun experimenting with chatbots. In the near future, we should expect businesses to adopt chatbot platforms in the same way they are currently embracing mobile and Internet of Things (IoT) platforms. A chatbot is a software that impersonates a user, a service which allows people to interact through a chat interface using textual or audio means. Chatbots offer brands an opportunity to reach out to an audience and engage them in dialogues that might translate into immediate actions. Functions that depend on interaction and conversations, such as customer service or rapid information access, are good fits for chatbots across different industries. For example, you could ask a bot on a travel site to book you a hotel based on your preference.

Through machine learning, researchers have repeated the historic criminology experiment of telling criminals apart from law-abiding people using facial recognition. Physiognomy, the ability to judge a person's character from appearance alone, has been around since ancient Greece and was widely accepted by philosophers. Although the theory has generally been disbanded, studies still crop up now and again. Xiaolin Wu and Xi Zhang, Chinese researchers from Shanghai Jiao Tong University, released a controversial paper on arXiv, an online open-sourced pre-print journal – it has not been published officially. They have singled out three features that can supposedly tell if a person is more likely to be a delinquent or not by probing upper lip curvature, eye inner corner distance, and the angle from nose tip to two mouth corners (nose-mouth angle). It's bad news for those who have smaller mouths, curvier upper lips and closer-set eyes, as you look more like a crook, apparently.

This post was featured in Information Age and can be read here. Artificial Intelligence, intelligence as exhibited by machines, is not something that is new to this world. Nearly twenty years ago IBM's supercomputer Deep Blue beat world chess champion Gary Kasparov. The win was symbolically significant and a sign that Artificial Intelligence was catching up with human intelligence. Fast forward twenty years and the application of AI technologies is something we encounter on a regular basis.