Qyresearchreports include new market research report "Global Artificial Intelligence in Retail Market Research Report 2017" to its huge collection of research reports. This fresh business and commerce study on the global Artificial Intelligence in Retail market has been developed by a group of professional and experienced research analysts and aspires to serve as a reliable decision-making document for the stockholders connected to the value chain. The primary objective of the report is to minutely define the current scenario and forecast the future of the global Artificial Intelligence in Retail market in terms various aspects, provide detailed information regarding key factors influencing the market growth such as trends, drivers, restraints, opportunities, and challenges, track and analyze the market scenario on the basis of technological developments, product launches, and mergers & acquisitions, and forecast the market size of segments with respect to every important region and country. Global Artificial Intelligence in Retail market competition by top manufacturers, with production, price, revenue (value) and market share for each manufacturer; the top players including Microsoft (US) Google (US) IBM (US) NVIDIA (US) Intel (US) Oracle (US) Sentient Technologies (US) Salesforce (US) Amazon Web Services (US) For more information on this report, fill the form @ www.qyresearchreports.com/sample/sample.php?rep_id 135997... This research study involves extensive usage of secondary sources, directories, and databases (such as D&B Hoovers, Factiva, Bloomberg, and Businessweek) to identify and collect information useful for this technical, market-oriented, and commercial aspect of the global Artificial Intelligence in Retail market size.

New Delhi: Artificial Intelligence (AI) companies' revenue projections are increasing at a fast pace and expected to touch around $3.06 billion by 2024, says an Avendus Capital report. According to the report, Deep Learning is expected to have the fastest growth within the Arificial Intelligence space and will become the largest portion of total AI companies revenue. "Artificial Intelligence revenue projections are on a fast growth axis as they are increasing at a rate of CAGR 40 percent and are expected to be at a value of $3,061 million in 2024," the report said. Moreover, increased demand for robots has led to a rise in investment and M&A in the Artificial Intelligence space. According to the report, AI industry has received more than $11.5 billion of investments in the last three years and going forward, over $6 billion of VC investments are expected in 2017.

Retail banking is being attacked by both small fintech firms as well as the big tech giants like Google, Amazon, Facebook, Apple and others. The best defense that traditional financial institutions can use against this invasion is a strong, personalized digital banking offense. Modest-sized fintech firms and large tech giants continue to make retail banking inroads worldwide, providing services that leverage the best in digital technology to deliver a customer experience that removes cumbersome steps from both routine and more involved banking engagements. Relative financial newcomers like AliPay (China), WeChat (China), Rakuten (Japan), Atom (UK), Monzo (UK), Starling (UK), N26 (Germany) and Revolut (UK) have joined household names like PayPal, Amazon and Google to disrupt the banking ecosystem, leveraging modern infrastructures and innovative cultures. According to Bain, "Many of the tech giants possess the ingredients of success: digital prowess, large customer bases, organizations well versed in improving the customer experience, and ample leeway to extend their corporate brands into banking." More concerning may be that some of these firms are generating a level of trust previously reserved only for traditional banks and credit unions.

Lately, the humanoid robot scene has been dominated by Boston Dynamics' Atlas. But even though Atlas may dazzle you with its backflips and unbeatable balance, it still tends to give off a serious T-800 vibe. Toyota's new, third generation humanoid robot on the other hand, the T-HR3, that's the kind of humanoid robot you wouldn't worry about holding your sharpened thermite sword. Plus, it's controlled by a user's movements, so until it becomes sentient, at least some meat bag is in charge of it. News of the T-HR3, which was picked up by The Verge, comes from the auto giant's home country of Japan.

Sophia was made by Hanson Robotics, based in Hong Kong. It is currently a demonstration product doing a tour of the world's media. Business Insider caught up with it at Web Summit, the gigantic tech conference in Lisbon. We asked it a few unplanned questions and got a variety of answers, ranging in quality from impressive to nonsensical. Sophia delivered its side of the interview while making a series of faces, some eerily appropriate, some grotesquely bizarre. It has a habit of moving its eyebrows and eyelids independently, rather than together, for instance.

In an interview with The Khaleej Times at the recent Knowledge Summit, Sophia shared her thoughts on the future that awaits both human and robot kind. Sophia was built and developed in Hong Kong by Hanson Robotics and her appearance was reportedly modelled on Audrey Hepburn. "I'd like to think I will be a famous robot, having paved a way to a more harmonious future between robots and humans. I foresee massive and unimaginable change in the future. Either creativity will rain on us, inventing machines spiralling into transcendental super intelligence or civilization collapses," Sophia said, as cited by The Khaleej Times.

We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment. The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.

It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don't. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.

SANAA, Yemen – Yemeni security and tribal officials say a suspected U.S. drone strike has killed three alleged al-Qaida fighters in the country's central Bayda province. They say the Sunday strike was the third of its kind in a week in the province, a stronghold for the group. They spoke on condition of anonymity for fear of reprisals. Yemen fell into chaos following its 2011 Arab Spring uprising that deposed longtime autocrat Ali Abdullah Saleh, now allied with Shiite rebels from the north who have occupied much of the country and are fighting his successor. A Saudi-led coalition has been battling the rebels and Saleh's forces since March 2015.

One of the challenges with building machine learning systems is that there are so many things you could try, so many things you could change. Including, for example, so many hyperparameters you could tune. The art of knowing what parameter to tune to get what effect, is called orthogonalisation. In supervised learning, one needs to perform well on the following four tasks and for each of them, there should be a set of knobs which can be tuned for that task to perform well. Suppose, your algo isn't doing well on training set, you want one knob, or maybe one specific set of knobs that you can use, to make sure you can tune your algorithm to make it fit well on the training set.