Big data is in many ways still a wild frontier, requiring wily smarts and road-tested persistence on the part of those hoping to find insight in all the petabytes. On Tuesday, IBM announced a new platform it hopes will make things easier. Dubbed Project DataWorks, the new cloud-based platform is the first to integrate all types of data and bring AI to the table for analytics, IBM said. Project DataWorks is available on IBM's Bluemix cloud platform and aims to foster collaboration among the many types of people who need to work with data. Tapping technologies including Apache Spark, IBM Watson Analytics and the IBM Data Science Experience launched in June, the new offering is designed to give users self-service access to data and models while ensuring governance and rapid-iteration capabilities. Project DataWorks can ingest data faster than any other data platform, from 50 to hundreds of Gbps, deriving from sources including enterprise databases, the internet of things (IoT) and social media, according to IBM.

Anti-money laundering, fraud prevention and sanctions processing are three of the key areas people working in in the payments industry see potential for artificial intelligence (AI) to play a role. In a survey of senior payment professionals in banks and corporates, 63% of those polled identified these three areas as potentially benefiting from AI that can learn from previous experience and behaviour patterns. That's according to Pelican, a company that – it should be noted - helps financial institutions apply application technologies to transactions. Formerly known as ACE software solutions, Pelican says it combines tools such as natural language processing and machine learning to profile payments and apply human-like logic to transactions. Pelican CEO Parth Desai says it's time the industry "wakes up" to the need to replace manual processes and legacy systems for payments processing. He says the gradual appearance of AI into everyday life will raise consumer expectations of faster results and services in their banking products too.

"Powered by advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery," it said. "Einstein's models will be automatically customized for every single customer, and it will learn, self-tune, and get smarter with every interaction and additional piece of data." John Ball, the program's general manager, said in a news article from USA Today that Salesforce Einstein AI will take the burden of the executives and sales team to find patterns in customer behavior in the hopes of sustaining or expanding that relationship. Without getting too technical, the computer will study the data and craft a model as well as detect patterns "that aren't as easy for humans to detect." The Salesforce Einstein AI unburdens businesses without the resources to hire expert personnel.

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.