There are more than 30 million research papers out there, and more than 3,000 papers are published every day. Put simply, you haven't a chance in hell to read all of them. So what's a poor researcher to do when set a challenge in a brand new field of research? Once the wave of blind panic and urge to drink copious amounts of gin has dissipated, you reach for a technology solution. Iris believes it has just the thing.

Salesforce Einstein has ushered in a new era of curiosity about AI. It wasn't the first commercial AI usage, and others will crop up alongside it, but the buzz these past couple of weeks is a surefire indication that companies are really considering AI as a viable technology for short-term gain as well as far-fetched, long-term big ideas. I look at the opportunity in front of us and I think to myself: "Yes! The energy is contagious, but with it comes more questions than answers. As marketers, what does it all mean?

Hot on the heels of Bank of America's announcement yesterday that it is launching a new chatbot named Erica, Mastercard has unveiled its own personal finance bot named KAI at Money 20/20. Mastercard's artificial intelligence-powered bot responds to more than 1,000 queries about personal finances and other subjects. It was developed by Kasisto, a spinoff of Siri creator SRI International. Available to US consumers later this year, Mastercard KAI will converse about a customer's account history and account balance, as well as make payments, identify spending patterns and set alerts for specific kinds of spending. In addition to the Mastercard bot for banks, the financial services provider is also launching a bot for merchants.

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests. The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they're called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world's best player of the strategy board game Go or classifying cat photos, yet know one fully understood why. It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

Idling in rush-hour traffic can be mind-numbing. It also carries other costs. Traffic congestion costs the U.S. economy $121 billion a year, mostly due to lost productivity, and produces about 25 billion kilograms of carbon dioxide emissions, Carnegie Mellon University professor of robotics Stephen Smith told the audience at a White House Frontiers Conference last week. In urban areas, drivers spend 40 percent of their time idling in traffic, he added. The big reason is that today's traffic signals are dumb.

Mitch and Freada Kapor have long been champions of equality. They talk about their hope for a more diverse tech workforce in Silicon Valley. Mitch was the man behind Lotus Notes and has gone on to be a big promoter of social issues. During those three intensive days spent brainstorming new technologies, she realized she was not the only one. Huaranca was one of just two Latinos in a room of 100 people.

Google's ad tracking data has been extended – potentially to watch over much of your life. Until recently, the site had important restrictions on the way that it could use your data. And perhaps one of the most important ones was the fact that Google worked hard to keep a massive database of web-browsing records separate from the personal information that it stores about people. Because of its huge range of products, Google knows a lot about specific people: for most internet users, it likely has their name, their addresses and a range of other personal information. And through its DoubleClick advertising network it has a huge set of information about people's web browsing history.

Spring XD addresses the new demands of big data and real-time data pipelining, but it sets a foundation for much more. Data Science, Machine Learning and Predictive Analytics are becoming more common across industries. The most successful and innovative companies are currently exploring live data streaming scenarios instead of the traditional batch collection, storage, ETL-like transformations and offline analytical solutions. Two main reasons are demanding this change. First, some data is really only valuable in the moment it's connected--as the half-life of its business value degrades quickly.

At a dinner I attended some years ago, the distinguished differential geometer Eugenio Calabi volunteered to me his tongue-in-cheek distinction between pure and applied mathematicians. A pure mathematician, when stuck on the problem under study, often decides to narrow the problem further and so avoid the obstruction. An applied mathematician interprets being stuck as an indication that it is time to learn more mathematics and find better tools. I have always loved this point of view; it explains how applied mathematicians will always need to make use of the new concepts and structures that are constantly being developed in more foundational mathematics. This is particularly evident today in the ongoing effort to understand "big data"--data sets that are too large or complex to be understood using traditional data-processing techniques.

Las Vegas - 25 Oct 2016: IBM (NYSE: IBM) today announced IBM Watson Data Platform to help companies gain more valuable insights from data. The platform delivers the world's fastest data ingestion engine and cognitive-powered decision-making to data professionals, allowing them to collaborate in the IBM Cloud, with the services they prefer. IBM is also making IBM Watson Machine Learning Service available – making machine learning simple with an intuitive, self-service interface. "Machine learning is incredibly powerful, but many of today's data professionals lack the skills to fully exploit it for business and the ability to effectively collaborate on datasets," said Bob Picciano, Senior Vice President, IBM Analytics. "Watson Data Platform applies cognitive assistance for creating machine learning models, making it far faster to get from data to insight. It also, provides one place to access machine learning services and languages, so that anyone, from an app developer to the Chief Data Officer, can collaborate seamlessly to make sense of data, ask better questions, and more effectively operationalize insight."