ARTIFICIAL INTELLIGENCE (AI) is everywhere and it's as big a dent in cyberspace as it is in the real world. According to PwC's Global Consumer Insights Survey 2018, AI is making waves in the world of retail. "In some ways, AI is the future of retail," says the report. The report found that 45 percent of store operators intended to increase their use of AI within the next three years to transform their business, improving customer engagement boosting how customer insights are generated from social media. Globally, customers too are adopting AI tools in the home to improve their lifestyle and experience.

SINGAPORE - The concept of fast food will be taken to a new level at Pizza Hut with the introduction of a robot which will take your order and process payment - all in the name of quicker service. From Wednesday (March 14) to Sunday, diners at Pizza Hut's Safra Punggol outlet will be the first in South-east Asia to be able to try out the new technology. After the five-day trial, Pizza Hut will take customers' feedback before deciding if the robot, which has a female voice and greets patrons with a "hello", stays at the outlet. To place an order, customers first have to greet the robot and pair their Mastercard Masterpass account embedded in the Pizza Hut Singapore mobile app. They can then verbally tell the robot their orders and show a QR code that will provide their table information.

Communicable diseases represent a critical challenge for resource-strapped public health infrastructures worldwide. As evidenced by the outbreaks of severe acute respiratory syndrome (SARS) in 2003, influenza A H1N1 (or "swine flu") in 2009, Ebola and Middle East respiratory syndrome (MERS) in 2014, and the Zika virus in 2016, infectious diseases can spread rapidly within countries and across national borders. In China alone, the World Bank estimated the economic cost of SARS at $14.8 billion, and while both Europe and the United States were largely spared its ravages, the epidemic impacted global gross domestic product (GBP) by $33 billion. In an era of global air travel and densely concentrated, interconnected populations, most countries remain woefully underequipped to stem the tide of such infections. Public health policymakers are tasked with deciding the nature and timing of appropriate courses of action to prevent, detect, and respond to an infectious disease outbreak.

Next time you go to Japan take Miko along to help you plan your vacation and see the sights. Miko is a super-smart chatbot, which is fueled by artificial intelligence (AI) and designed to take the guesswork out of traveling. It is the star of the Japan Trip Navigator – a new smartphone app created by Microsoft in partnership with two Japanese companies – travel agency, JTB Corp., and navigation firm, NAVITIME Co. Miko (pictured left) acts like virtual travel concierge who is constantly learning about what's hot and where to go. It answers questions by drawing on JTB's extensive knowledge resources and learns from the input of information and images from other travelers who use the app. Users will also be able to make hotel and other bookings on the app.

NEW DELHI: Eminent astrophysicist Stephen Hawking, who died today, had warned that the efforts to develop artificial intelligence (AI) and create thinking machines could spell the end of the human race. Hawking, known for his work on black holes and relativity, was regarded as one of the most brilliant theoretical physicists since Albert Einstein. He was suffering from amyotrophic lateral sclerosis, more commonly known as Lou Gehrig's disease since he was 20. Despite being told that he had just two more years to live in 1963, Hawking continued to make path breaking contributions to science till the age of 76. In the last few years, Hawking repeatedly warned about the threat of climate change, artificial intelligence, population burden and hostile aliens.

There are times when you can actually believe that you have been asleep for a few years. You wake up, switch on your computer and read something that clearly comes from the distant future. The item, in this case, was a headline that read: "Brain machine interface hardware revenues to reach $19 billion by 2027". Surely some time warp must have catapulted us at least five years into the future. As bleary, early morning images of people interfacing with their computers (such an old fashioned term), devices, houses, locks, offices and the environment generally began to clear, slightly saner images took their place.

"What could possibly go wrong?" They are literally just asking for a kaiju movie at this point". The new comes just weeks after another season four prophecy came true, which prompted Black Mirror themselves to respond, "We know how this goes." The third episode of the new season, titled'Crocodile', features a driverless pizza delivery van in its central narrative, the invention of which starts off a chain of events that eventually leads to several deaths. Now, just weeks after the episode's release, Pizza Hut has announced that it is partnering with Toyota to launch its own driverless delivery vehicles.

Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create. Characterization (amplitude and phase) of these pulses is a key ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstrate, numerically and experimentally, the first deep neural network technique to reconstruct ultra-short optical pulses. We anticipate that this approach will extend the range of ultrashort laser pulses that can be characterized, e.g., enabling to diagnose very weak attosecond pulses. Ultra-short laser pulses are the shortest systematic events that can currently be created. They are typically used to measure physical and chemical phenomena. These pulses are currently being used in numerous applications including material and tissue processing, medical-imaging and research of light and matter (Zewail, 2000; Delgado-Ruíz et al., 2011; Malinauskas et al., 2016).

Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks and other supervised learning models, as they often provide significant improvements over stochastic gradient descent (SGD). Rigorously speaking, "fast gradient" methods have provable improvements over gradient descent only for the deterministic case, where the gradients are exact. In the stochastic case, the popular explanations for their wide applicability is that when these fast gradient methods are applied in the stochastic case, they partially mimic their exact gradient counterparts, resulting in some practical gain. This work provides a counterpoint to this belief by proving that there exist simple problem instances where these methods cannot outperform SGD despite the best setting of its parameters. These negative problem instances are, in an informal sense, generic; they do not look like carefully constructed pathological instances. These results suggest (along with empirical evidence) that HB or NAG's practical performance gains are a by-product of mini-batching. Furthermore, this work provides a viable (and provable) alternative, which, on the same set of problem instances, significantly improves over HB, NAG, and SGD's performance. This algorithm, referred to as Accelerated Stochastic Gradient Descent (ASGD), is a simple to implement stochastic algorithm, based on a relatively less popular variant of Nesterov's Acceleration. Extensive empirical results in this paper show that ASGD has performance gains over HB, NAG, and SGD.

Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (V AE). Second, we establish conditions under which such inference networks enable fast amortized inference similar to V AE. Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference. By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods. To analyze real-world data, machine learning relies on models that can extract useful patterns. Deep Neural Networks (DNNs) are a popular choice for this purpose because they can learn flexible representations. Another popular choice are probabilistic graphical models (PGMs) which can find interpretable structures in the data. Recent work on combining these two types of models hopes to exploit their complimentary strengths and provide powerful models that are also easy to interpret (Johnson et al., 2016; Krishnan et al., 2015; Archer et al., 2015; Fraccaro et al., 2016). To apply such hybrid models to real-world problems, we need efficient algorithms that can extract useful structure from the data. For deep learning, stochastic-gradient methods are the most popular choice, e.g., those based on back-propagation.