At BlizzCon earlier this month in Anaheim, California, Blizzard announced an ambitious new project in collaboration with DeepMind, a leading artificial intelligence research company acquired by Google in 2014. After creating the AlphaGo AI that bested the world's top Go player earlier this year, DeepMind's next groundbreaking challenge will be StarCraft II. If DeepMind is able to build an AI that could learn how to beat top players such as Byun "ByuN" Hyun Woo in the complex real-time strategy, tactics and resource management of this game, it would be a giant step forward in AI research. And with DeepMind's interest in using its research to solve hard problems in areas such as healthcare and energy efficiency on a massive scale, this Starcraft II project could impact the whole world. Soon after AlphaGo's Go victory, there were signs that DeepMind would take on StarCraft next. This was not lost on legendary StarCraft player/commentator and former competitive chess player Dan "Artosis" Stemkosi, for whom StarCraft seemed like the logical next step for AI research after games like chess and Go.

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?"

Google DeepMind's artificial intelligence team, alongside researchers at the University of California, Berkeley, has trained AI machines to interact with objects in order to evaluate their properties without any prior awareness of physical laws. The research project drew inspiration from child development and sought to train AI to mirror human capacity to interact with physical objects and infer properties such as mass, friction, and malleability. The study, entitled Learning to perform physics experiments via deep reinforcement learning, explained that while recent advances in AI have achieved'superhuman performance' in complex control problems and other processing tasks, the machines still lack a common sense understanding of our physical world – 'it is not clear that these systems can rival the scientific intuition of even a young child.' Lead researcher Misha Denil and his team set about various trials in different virtual environments in which the AI was faced with a series of blocks and tasked with assessing their properties. In the first simulation, called Which is Heavier, the AI was given a set of four blocks which were the same size but varied in mass.

The way we watch sport, the way we shop, the way we meet each other, the way we interact with machine intelligence -- everything we can imagine is now possible by adding a crystal clear layer of visual genius over our real world. The Fourth Transformation is based on two years of research and about 400 interviews with technologists and business decision makers. It explains the technology and product landscape on a level designed to be interesting and useful to business thinkers and general audiences. Mostly it talks about how VR and AR are already being used, or will be used in the next one-to-three years. It explains how this massive and fundamental transformation will be driven, nit just by Millennials, but by the generation following them, which the authors have named the Minecraft Generation.

ORLANDO, Fla., October 18, 2016 View All Press Releases Gartner Reveals Top Predictions for IT Organizations and Users in 2017 and Beyond Analysts Explore the Digital Future at Gartner Symposium/ITxpo 2016, October 16-20 in Orlando Gartner, Inc. today revealed its top predictions for 2017 and beyond. Gartner's top predictions for 2017 examine three fundamental effects of continued digital innovation: experience and engagement, business innovation, and the secondary effects that result from increased digital capabilities. "Gartner's top strategic predictions continue to offer a provocative look at what might happen in some of the most critical areas of technology evolution. At the core of future outcomes is the notion of digital disruption, which has moved from an infrequent inconvenience to a consistent stream of change that is redefining markets and entire industries," said Daryl Plummer, managing vice president, chief of research and Gartner Fellow. "Last year, we said digital changes were coming fast. This year the acceleration continues and may cause secondary effects that have wide-ranging impact on people and technology."

Palantir CEO Alex Karp Says Going Public Is'A Possibility' Citi Ventures, an arm of the bank that combines investments in startups with innovation developed in its six Citi Global Innovation Labs around the world, has announced a strategic investment in Feedzai, a machine learning company with a focus on real-time fraud prevention in ecommerce and banking. Feedzai said the investment will support its continued expansion of offerings to the financial services ecosystem. "Citi Ventures is actively exploring is the application of machine learning, which we are looking across multiple sectors including security, customer service, compliance, and data and analytics," said Ramneek Gupta, managing director and co-head of investing. In machine learning, it has invested in Feedzai, Cylance and Ayasdi "current portfolio companies deploying machine learning in innovative ways," Gupta said. "We have used Ayasdi's technology in several use cases and also provided valuable feedback and suggestions that helped Ayasdi evolve its product architecture for deployment into our own complex data environments. Our partnerships have also enabled us to learn a great deal from Cylance and Feedzai."

How is Machine Learning different from other kinds of programming? Is Machine Learning a new concept? How is Machine Learning used in marketing? As mentioned earlier, a wealth of relevant data is essential for successful machine learning projects, as more data equates to more intelligent predictions and concrete pattern recognition. Traditionally, the scale of analytics which is now possible with the inclusion of machine learning was exclusively available to larger enterprises.

The list of activities in which artificial intelligence machines have bested humans is increasing at an alarming rate. Face recognition, object recognition, chess, Go, various video games, and numerous other tasks have all fallen in this battle. So it's natural to ask about the types of tasks that machines still have difficulty with. Where do humans still rule the roost? Today, we get an answer of sorts thanks to the work of Mohit Iyyer at the University of Maryland in College Park and a few pals.

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?"

In 2017, artificial intelligence will play a big role in HR. It's a complete evolution that will help HR face challenges facing leadership and talent management now. Like the cloud, it's a technological sea-change when we really need it. But in HR we're not sure what to do with it yet -- so I'm here to help. The world of work is marked by profound disruptions right now.