Science fiction has accustomed us to metallic, humanoid robots, but there are better models out there. Scientists have created a completely wireless, soft robotic octopus, dubbed the "Octobot." The robot is made of supple silicon and is powered by a chemical reaction. So far Octobot can only wave its legs up and down somewhat feebly, but it represents a major leap forward in the field of soft robotics. For some time, scientists observing the natural world have noticed that there are more promising robot models than humans.

It is projected that urban India will contribute about 75% of the national GDP in the next 15 years. With the rapid rate of urbanization and depleting resources, it is essential for India to rethink its strategy to see how it can best optimize the capabilities of its physical infrastructure. By leveraging global best practices in smart city development and ICT (Information and Communication Technology), India is in a unique position to leapfrog a few stages of growth to become a global superpower. In 2015, the Indian government announced an ambitious plan to develop 100 smart cities, with a commitment of spending 7.27 billion over the next five years. The infrastructure overhaul would include creating intelligent transport systems, smart grids, smart waste management, and smart water grids/solutions.

On Sunday, Salesforce.com, which sells online software for sales and marketing, announced it would be adding A.I. to its products. Its system, called Einstein, promises to provide insights into what sales leads to follow and what products to make next."

When you think "Artificial Intelligence" -- what comes to mind? Do you think of Ex Machina human-like robots taking over the world? Or maybe you envision having your own butler like Tony Stark's Jarvis in The Avengers. In movies, artificial intelligence is depicted as being overly futuristic. In reality, AI has already had a profound impact on everyday life. In the coming years we can expect to see this reach grow immensely.

It was December 2012, and Doug Burger was standing in front of Steve Ballmer, trying to predict the future. Ballmer, the big, bald, boisterous CEO of Microsoft, sat in the lecture room on the ground floor of Building 99, home base for the company's blue-sky R&D lab just outside Seattle. The tables curved around the outside of the room in a U-shape, and Ballmer was surrounded by his top lieutenants, his laptop open. Burger, a computer chip researcher who had joined the company four years earlier, was pitching a new idea to the execs. He called it Project Catapult. The tech world, Burger explained, was moving into a new orbit.

Last time we concluded by noticing that minimizing the empirical risk (or the training error) is not in itself a solution to the learning problem, it could only be considered a solution if we can guarantee that the difference between the training error and the generalization error (which is also called the generalization gap) is small enough. That is if this probability is small, we can guarantee that the difference between the errors is not much, and hence the learning problem can be solved. In this part we'll start investigating that probability at depth and see if it indeed can be small, but before starting you should note that I skipped a lot of the mathematical proofs here. You'll often see phrases like "It can be proved that …", "One can prove …", "It can be shown that …", … etc without giving the actual proof. This is to make the post easier to read and to focus all the effort on the conceptual understanding of the subject. In case you wish to get your hands dirty with proofs, you can find all of them in the additional readings, or on the Internet of course!

Artificial intelligence technology is finding its way to every corner of the modern enterprise. Last week Google set out to help organizations embrace the trend faster by acquiring a startup called API.AI Inc. that has developed a cloud-based chatbot builder. The platform provides pre-packaged communications modules, a repository of encyclopedic data and other components that can be quickly cobbled together into a working artificial intelligence. API.AI claims that companies can use its service to build everything from Slack-based bots focused on a specific task to standalone virtual assistants capable of taking a wide range of different requests. Google will likely make the technology part of its public cloud in the wake of the acquisition to try to gain an edge over better-established providers like Amazon.com Inc. Oracle Corp. is pursuing a similar goal with the competing chatbot development tool that it unveiled at its annual customer conference last week.

Over the summer, I spent some time in Silicon Valley, the home of technology and innovation. I took part in a program at Singularity University, a group whose mission is to help all of us understand how to utilize cutting-edge technologies to positively impact the world around us. I spend every day talking and thinking about technology and ways to make it do more for UBS Wealth Management's clients and staff. For two decades I've worked with different technology and I've seen some amazing changes – I remember not only when we didn't have emails on our phones in our pockets, but when we didn't even have emails at all! So given my experience and what I do day-in, day-out, I love that the speed at which technology is developing still astounds me.

Microsoft's Ignite conference is bringing 23,000 IT professionals to Atlanta, Georgia this week and the company is using this opportunity to talk about how it plans to bring more intelligence to its tools and platforms. As companies gather more and more data, Microsoft argues, it's becoming imperative that the tools these companies use also become smarter. This means bringing better analytics to the workplace to help individual employees become more productive, but also bringing machine learning-powered tools to services like its CRM service Dynamics 365. One thing the company's representatives stressed repeatedly during a small press event ahead of the conference was that Microsoft doesn't believe AI technologies will replace humans but instead empower them. We'll see how that works out in the long run.

The retail industry is inundated with buzzwords that describe the best way to engage with consumers -- such as omnichannel marketing, customer journey, and a 360-degree view -- to provide a more personalized and optimized experience. All of these buzzwords point to a common problem most retail marketers are aching to solve: How can they use customer data to create timely insight into what customers are doing in-store and/or online and convert that into strategies and actions that increase sales? It's an age old question with new age solutions, and the retail industry is now leveraging artificial intelligence (A.I.) to usher in a unique way of thinking about this problem. In fact, a new chart shows there are 45 A.I. companies focused on retail across a range of verticals, including recommendations, merchandising, search, conversational commerce, and especially multichannel marketing. Using omnichannel retailing, the ability to connect with a customer both online and offline, is ideal because omnichannel customers have a 30 percent higher lifetime value than single channel customers.