Today's consumers are a tough nut to crack. They tend to look around before they buy, talk to their entire social network about their purchases, demand to be treated as unique and want to be sincerely thanked for buying products. Big Data allows us to profile these increasingly vocal and inconstant little'tyrants' in a far-reaching manner so that we can engage in an almost one-on-one, real-time conversation with them. This is not actually a luxury. If we don't treat them like they want to, they will leave us in the blink of an eye.

It's a fact of nature that a single conversation can be interpreted in very different ways. For people with anxiety or conditions like Asperger's, this can make social situations extremely stressful. But what if there was a more objective way to measure and understand our interactions? Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) say that they've gotten closer to a potential solution: an artificially intelligent wearable system that can predict if a conversation is happy, sad or neutral based on a person's speech patterns and vitals. "Imagine if, at the end of a conversation, you could rewind it and see the moments when the people around you felt the most anxious," says graduate student Tuka Alhanai, who co-authored a related paper with PhD candidate Mohammad Ghassemi that they will present at next week's Association for the Advancement of Artificial Intelligence (AAAI) conference in San Francisco.

Customer journey mapping, AI, VR and design; how do our experts see UX changing in 2017? With the rise of AI over the next 12 months, it will be interesting to see how brands can be consistent throughout a journey. For example, I can order an Uber from Alexa (Amazon's AI) on my Amazon Echo, get a notification on my Apple watch when it arrives and split the fare with my friend on messenger. How will Uber ensure their brand values are met with so many different interfaces? What was once a slick looking app now turns into a disjointed journey lost in a web of'human' interactions.

The potential of AI is often praised by academics, technologists and the press, but there is little information about how to adapt to this new technology and its influence on modern-day jobs. How should managers prepare themselves to ensure that they will excel in this new world of artificial intelligence? Accenture recently conducted a research, surveying 1,770 managers from 14 countries and 37 interviewed executives responsible for the digital transformation of their company. By identifying this data, the researchers established five practices that is essential for a manager to master, to be successful. The survey, conducted by Vegard Kolbjornsrud, Richard Amico and Robert J. Thomas, found that managers spend more than half their time on administrative coordination and control tasks, despite what levels they are managing.

One of the biggest misconceptions about artificial intelligence is the belief that today's AIs possess generalized intelligence. We are really good at leveraging large data sets to accomplish specific tasks, but fall flat at replicating the breadth of human intelligence. If we're going to move toward generalized intelligence, Facebook wants to make sure we know how to evaluate progress. In a paper, Facebook's AI Research (FAIR) lab outlines just that as part of its CommAI framework. First, the team argues that a proper generalized AI should be able to fluently communicate in natural language with humans. Researchers in the field of natural language processing have been working on the problem of human computer interaction through language for some time now, but we have a long way to go before our computational systems can rival humans.

We now have all the tools in place--big-pipe broadband connectivity, cloud services, smart devices, super-powerful servers and the burgeoning internet of things--to put IT into an entirely new high gear and move our culture ahead in the new century. So what, then, must enterprises do next to find competitive advantage? We are seeing it now--the introduction of more functionality through artificial intelligence. AI is now a part of more apps and in more places than we've ever seen before: wearables, cars, productivity apps, military, health care, home entertainment--the list is lengthy. However, as the tidal wave around AI grows, the noise can distract organizations from accurately assessing which AI technology is right for them.

Chatbots are invading the business world. And while customer support is the most obvious use case, bots today are underused as efficiency drivers, and they can have a much larger impact on all aspects of a business for those who learn how to leverage them. In fact, with businesses increasingly adopting a multitude of cloud solutions, chatbots are uniquely positioned to help companies bring together different software into one streamlined communication channel. Booming growth in open APIs has made this easier than ever across a wide array of applications. The payoff will be significantly greater efficiency: Companies using support bots report efficiency gains in excess of 30 percent.

Jeremy Waite, Evangelist at IBM Watson, kicked off speaking at a recent DMA event by highlighting the fact that by 2019 there will be 1m new devices coming online every hour. With so much smart tech in the hands of consumers, will we end up marketing to machines or algorithms? He asked the audience to think about how we can use AI to create more meaningful relationships with our customers and use the power of marketing to make a difference. While it is easy to get overexcited about technology, we still have situations where 85% of enterprises are not sharing data within their own sales teams. Furthermore, the Gartner Hype Cycle for Emerging Technologies indicates that two of the top trends over the next five years will be cognitive expert advisors and machine learning.

The Economist Originally published January 7, 2017 Here is an excerpt: Another important biological hurdle that AI can help people surmount is complexity. Experimental science progresses by holding steady one variable at a time, an approach that is not always easy when dealing with networks of genes, proteins or other molecules. AI can handle this more easily than human beings. At BERG Health, the firm's AI system starts by analysing tissue samples, genomics and other clinical data relevant to a particular disease. It then tries to model from this information the network of protein interactions that underlie that disease.

The University of Southern California has recently created the Center on Artificial Intelligence for Social Solutions or CAISS, specifically to develop uses of artificial intelligence--AI--for use cases of interest to social entrepreneurs. The Center is a collaboration between the USC Viterbi School of Engineering and the USC School of Social Work. Eric Rice, an Associate Professor from the School of Social Work, has been tapped to lead CAISS. The potential for AI to be an effective tool for entrepreneurs with no background in technology is confirmed by Rice himself. When asked about his LinkedIn profile URL, he acknowledged not only that he doesn't have one, but also said, "I'm a bit of a Luddite."