Firaxis is offering the first peek at "Civilization VI," the next chapter in the decades-old franchise that lets you play your way to global domination -- and it's coming with a major new twist. The game, in which players take on the roles of historical figures to build and expand empires, has always been densely packed -- particularly when it comes to building cities. Players have been able to stack their cities with specialty buildings like universities and monuments, but buildings have been packed together in a single tile. This adds a different dimension to creating your cities, giving players a sense of city layout and a chance to craft the character of a metropolis much more easily. With the luxury of space, you will be able to do much more focused city management, by setting up specialized districts within your city walls. You can set up a downtown, or an education district, for example.

To help with his class this spring, a Georgia Tech professor hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor's overworked teaching staff. But, in fact, Jill Watson was an artificial intelligence bot. Ashok Goel, a computer science professor, did not reveal Watson's true identity to students until after they'd turned in their final exams. "I feel like I am part of history because of Jill and this class!"

Search giant Google announced Wednesday that it would cut payday loan providers from its advertising platforms, citing the potentially damaging effects to borrowers of short-term, high-interest cash loans. "Research has shown that these loans can result in unaffordable payment and high default rates for users so we will be updating our policies globally to reflect that," Google's head of global product policy, David Graf,f said in an announcement posted to the company's blog. The average payday loan borrower spends five months of the year in debt, paying more in fees than originally received, according to research compiled by the Pew Charitable Trusts. "Our hope is that fewer people will be exposed to misleading or harmful products," Graff said. The policy change, which follows a similar move by Facebook, won plaudits from advocacy groups concerned with the impact of payday loans on low-income borrowers.

It is also being used to identify the drugs that particular patients are more likely to respond to, most recently with Berg, who are hoping to identify the biological makeup of cancer patients who are likely to respond best to their drugs. Berg CEO and co-founder Niven Narain is confident of its success - 'With use of Berg's Interrogative Biology platform, we will be applying our precision medicine approach where output from this trial will allow us to match patients to this given combination based on their biological profile.'

The annual March Machine Learning Mania competition sponsored by SAP challenged Kagglers to predict the outcomes of every possible match-up in the 2016 men's NCAA basketball tournament. Nearly 600 teams competed, but only the first place forecasts were robust enough against upsets to top this year's bracket. In this blog post, Miguel Alomar describes how calculating the offensive and defensive efficiency played into his winning strategy. I earned a Master's Degree in Computer Science from UIB in Mallorca, Spain. For nearly 20 years, I have been involved in software development, business intelligence and data warehousing.

Our next Healthcare focused events include the Women in Machine Intelligence & Healthcare Dinner in London on 12 Oct and the Deep Learning in Healthcare Summit in Boston on 11 & 12 May 2017.

The Deep Learning Summit is the next revolution in artificial intelligence. How can multiple levels of representation and abstraction help to make sense of data such as images, sound, and text.

We are pretty certain that you must have come across multiple articles/blogs on artificial intelligence. It's plausibility of being able to ever evolve. But let's just take a moment here, and rather going forward take a step backwards and understand how and why exactly did artificial intelligence come into existence. The idea of machines being able to think, analyse, predict and understand like humans (even better and faster) has been a fantasy since centuries. So it would rather be tough to say where and how exactly did the concept of artificial intelligence came into being, but one of the first algorithm of machine learning (a sub-set of Artificial Intelligence) was designed in 1970, but it's true potential was unleashed in a famous paper in 1986 by David Rumelhart, Geoffrey Hinton and Ronald Williams (and is still vastly used as a basic model for machine learning) and the first ever breakthrough idea to be sprouted was in 1950.

I've never seen robots or AI as an evil job replacing or apocalypse causing fear monger. Rather robots or AI in this instance can help people solve grandiose problems. For instance, we have been using machine learning to play games, answer questions, perform data entry, optimize businesses, fly drones etc. Yet why haven't we used the same technology to solve a few very simple problems: keep robots standing up, improve robotic dexterity, and improve the dexterity of exoskeletal limbs? It isn't that humans aren't intelligent enough, it's just that machines are more precise, never get bored, or lose focus.

The strong interest in deep learning neural networks lies in the ability of neural networks to solve complex pattern recognition tasks – sometimes better than humans. Once trained, these machine learning solutions can run very quickly – even in real-time – and very efficiently on low-power mobile devices and in the datacenter. However training a machine learning algorithm to accurately solve complex problems requires large amounts of data that greatly increases the computational workload. Scalable distributed parallel computing using a high-performance communications fabric is an essential part of what makes the training of deep learning on large complex datasets tractable in both the data center and within the cloud. Very simply, the single node TF/s parallelism delivered by Intel Xeon processor and Intel Xeon Phi devices described in the previous article in this series is simply not enough for many complex machine learning training sets.