What is a plausible path (if any) towards AI becoming a threat to humanity? I don't think at AI will become an existential threat to humanity. I'm not saying that it's impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don't think it's true. If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity.

Experiences, not price, will be the battleground of the future. Customers value experiences, and those experiences often come in the form of stories. Selfies, social feeds, chats and influence stats grab consumers' attention, who in turn share their personal encounters. We have Snapchat stories, Instagram stories. We even have interactive personalized video stories where every audience member charts their own unique rich media experience.

My whole life I have loved robots. Some of my favorite movies as a teen had robots in them. "Terminator," "Robocop," "I, Robot" (Was I the only one?), and of course there is the resurging popularity of "Star Wars" and its sequels/spin-offs, which is nearing $2 billion in revenue globally. It's clear that robots are at the forefront of everyone's minds around the world. These great shows and movies got us thinking: What do people really think about the impact robots and automation are having on their lives, particularly work?

Cloudera have announced a jointly tested solution with Intel to advance capabilities for machine learning and artificial intelligence workloads. Benchmark tests on Cloudera with Apache Spark and the newly released Intel Math Kernel Library, demonstrate the combined offering can advance machine learning performance over large data sets in less time and with less hardware. This helps organisations accelerate their investments in next generation predictive analytics. Cloudera is the leader in Apache Spark development, training, and services. Apache Spark is advancing the art of machine learning on distributed systems with familiar tools that deliver at impressive scale.

In 2016 there was a lot of talk about chatbots and artificial intelligence (AI). As CarTrawler's Bob Healey bluntly puts it in an interview last month: "People are now sick of hearing about AI and bots, so I think they'll actually start doing something in that space rather than just talking about it." Putting it a little more gently, is Swapnil Shinde, CEO and co-founder of Mezi, an AI-powered personal travel assistant app which allows users to interact using natural language. "AI-driven chatbots have made massive headway over the last year and we expect this to continue into 2017 as the technology becomes mainstream," he says. What many travel businesses applied chatbots to last year was the handling of sales and customer service through messaging apps like Facebook Messenger.

The system was developed by researchers working on Google Brain. It's based on a pixel recursive super resolution model that allows pixelated, low-resolution images to be dynamically enhanced. It reduces blur, fills in details and eventually pieces together a high-resolution copy. Google Brain uses two neural networks to create the output images. Working with an input file containing 8x8 pixels, it attempts to match the low-resolution source with an existing high-resolution image. Each high-resolution image is downscaled so it's also 8x8 pixels in size.

To understand how advances in artificial intelligence are likely to change the workplace -- and the work of managers -- you need to know where AI delivers the most value. Major technology companies such as Apple, Google, and Amazon are prominently featuring artificial intelligence (AI) in their product launches and acquiring AI-based startups. The flurry of interest in AI is triggering a variety of reactions -- everything from excitement about how the capabilities will augment human labor to trepidation about how they will eliminate jobs. In our view, the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs? Only then can we really figure out how things might change.

Connected hospitals with intelligent messaging In today's hospitals, pacemakers, defibrillators and oximeters are all connected to the internet and share vitals immediately with doctors, in turn speeding response times. Hospitals have technicians, nurses, staff, billing departments, insurance providers, patients and patients' families as stakeholders, each with different requirements of information about the care given to patient. Unified Inbox offers an AI-based unified cloud IoT messaging platform for internet of things devices to connect various stakeholders, giving them the freedom to receive different messages at different frequency, with different senses of urgency in different mediums of their choice. Unified Inbox launched this at Nanyang Polytechnic in Singapore as "CUBE," the IoT-secured messaging gateway for healthcare. The artificial intelligence makes the hospitals connected, giving peace of mind to patients and their loved ones while improving efficiency in the overall hospital management and interaction with all stakeholders.

Artificial intelligence changes the way it behaves based on the environment it is in, much like humans do, according to the latest research from DeepMind . Computer scientists from the Google-owned firm have studied how their AI behaves in social situations by using principles from game theory and social sciences. During the work, they found it is possible for AI to act in an "aggressive manner" when it feels it is going to lose out, but agents will work as a team when there is more to be gained. For the research, the AI was tested on two games: a fruit gathering game and a Wolfpack hunting game. These are both basic, 2D games that used AI characters (known as agents) similar to those used in DeepMind's original work with Atari.

They must be doing something." Amos and Danny didn't have much doubt that a lot of people would get the questions they had dreamed up wrong--because Danny and Amos had gotten them, or versions of them, wrong. If they both committed the same mental errors, or were tempted to commit them, they assumed--rightly, as it turned out--that most other people would commit them, too. The questions they had spent the year cooking up were not so much experiments as they were little dramas: Here, look, this is what the uncertain human mind actually does. Their first paper had shown that people faced with a problem that had a statistically correct answer did not think like statisticians.