For anyone who has yet to experience Virtual Reality, I implore you, seek out a VR experience of some description. Whether Goggle Cardboard or Oculus Rift, it will likely be mind blowing. Why else would everyone who's anyone be talking about it being the future? When it comes to quality VR becoming an actual living room reality, though, there are many questions. Will there ever be a reason to?

Today with the growth of the internet of things there is a surge in usage of fast internet, smartphones, portable devices, smart TV, smart wearable and connected cars among retail consumers. This indicates the rise of connected consumers. This has made the huge impact in the retail landscape and the retail experience is evolving from multi-channel to Omni-channel. The connected consumer requires connected retail. Omni-channel retailing means establishing a presence on many channels and platforms (i.e. Physical stores remain the foundation of retailing, evidenced by the fact that - in the internet era, 90% of retail sales still take place in physical stores. According to eMarketer, the total retail sales in the US topped 4.53 trillion in 2013, of this brick-and-mortar sales still command a vast majority of the retail market nearly 4.27 trillion. According to A.T. Kearney, 95 percent of all retail sales are captured by retailers with a brick-and-mortar presence in total retail. Two-thirds of consumers who purchase online use the store before or after the transaction. The role of the brick & mortar store is changing, NOT going away. Stores remain at the heart of retailers' relationships with consumers, even in today's "Omni Channel" world where online and physical sales appear to rule. According to a report, When it comes to product trial and tests, respondents indicate a clear preference for physical stores. Despite the growing popularity of e-commerce and digital retailing are capturing headlines in news media and inspiring spirited debates (blah, blah..) retailers plan how best to invest for future success, including a plan to build an another planet. The e-commerce retail market share is only 263 billion. It is only 5.8% of the market share. Small basket, single channel, ineffective delivery, can't see, can't feel or try made it as a hard business model. Is pure play e-commerce is an unprofitable business model? Investment on e-commerce is "river of no returns"? Are we nearing the end of the e-commerce bubble? Is M-commerce the future of retail? Drop out, make an app, go viral and make money?

PyData Chicago 2016 This tutorial provides you with a comprehensive introduction to machine learning in Python using the popular scikit-learn library. We will learn how to tackle common problems in predictive modeling and clustering analysis that can be used in real-world problems, in business and in research applications. And we will implement certain algorithms as scratch as well, to internalize the inner workings This tutorial will teach you the basics of scikit-learn. We will learn how to leverage powerful algorithms from the two main domains of machine learning: supervised and unsupervised learning. In this talk, I will give you a brief overview of the basic concepts of classification and regression analysis, how to build powerful predictive models from labeled data.

Quantum physics has some spooky, anti-intuitive effects, but it could also be essential to how actual intuition works, at least in regards to artificial intelligence. In a new study, researcher Vedran Dunjko and co-authors applied a quantum analysis to a field within artificial intelligence called reinforcement learning, which deals with how to program a machine to make appropriate choices to maximize a cumulative reward. The field is surprisingly complex and must take into account everything from game theory to information theory. Dunjko and his team found that quantum effects, when applied to reinforcement learning in artificial intelligence systems, could provide quadratic improvements in learning efficiency, reports Phys.org . Exponential improvements might even be possible over short-term performance tasks.

Speech recognition software isn't perfect, but it is a little closer to human this week, as a Microsoft Artificial Intelligence and Research team reached a major milestone in speech-to-text development: The system reached a historically low word error rate of 5.9 percent, equal to the accuracy of a professional (human) transcriptionist. The system can discern words as clearly and accurately as two people having a conversation might understand one another. By combining Microsoft's open-source Computational Network Toolkit, and being a little bit over-obsessed with this project, the team was able to beat its goal of human parity by years in just months, according to Microsoft's blog. They hit the parity milestone around 3:30 a.m., when Xuedong Huang, the company's chief speech scientist, woke up to the breakthrough. It's a difference you'll likely notice when you're talking to an AI assistant in the near future says Huang, as speech recognition becomes a mainstream user interface.

There was lots to get excited about this evening thanks to the launch of the Centre for the Future of Intelligence (CFI), Cambridge University's new 10m initiative to tackle the big questions around the rise of artificial intelligence (AI). The Cambridge cosmologist Professor Stephen Hawking was guest of honour and took the floor to have his say. He has been outspoken about the potential benefits- and dangers- of AI. Speaking ahead of the event, he said: "The rise of powerful AI will be either the best or the worst thing to ever happen to humanity. We do not yet know which. "The research done by this centre will be crucial to the future of civilisation and of our species." Prof Hawking addressed assembled guests and dignitaries in Cambridge, at the newly-refurbished David Attenborough Building. He said: "We spend a great deal of time studying history, which let's face it, is the history of stupidity.

They're practically everywhere, and it's no wonder: according to some analysts, the fitness tracker market is one poised to reach 19 billion in 2018. But some companies are positioned better than others. On Wednesday, one of the arguable forerunners, Moov, took the wraps off the Moov HR, an activity tracker that measures heart rate with pinpoint accuracy. The Moov HR builds on the foundation of the startup's previous sensor, the Moov Now, which garnered praise for its innovative approach to fitness. Rather than simply measuring the number of steps you've taken, the calories you've burned, and distance you've walked, it fed that data into an artificial intelligence that dynamically guided you through goal-oriented workouts.

The logo of Microsoft is pictured in Issy-les-Moulineaux, France, August 8, 2016. New York: In a major breakthrough in the field of speech recognition, Microsoft researchers have created a technology that accurately recognises the words in a conversation like humans do. The team from Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard "Switchboard" speech recognition task.

In 360 BC, Plato wrote, "Whatever comes into existence, always comes as a whole." In 2016, scientists are using the same philosophy to develop a new automation model for robotic systems. "No phenomenon exists entirely alone. Each is a part of other phenomena," wrote Hua Chen, an associate professor at Hohai University, Chanzhou Campus, with YangQuan Chen, a professor at the School of Engineering, University of California, Merced. In a paper published in IEEE/CAA Journal of Automatica Sinica (JAS), Chen and Chen proposed a new model for designing control systems used in automation.

Artificial intelligence is a "momentous development," said George Lee, co-chairman of the global technology, media and telecom group at Goldman Sachs. "As awesome as the internet has been, it will be best remembered as really the predicate for machine learning," said Lee, who's also chief information officer of Goldman's investment banking division. He appeared on CNBC's "Squawk Alley" on Wednesday from Goldman's Builders Innovators Summit in Santa Barbara, California. The internet enabled computing scale in a network and serves as a way to "collect data that's used to train all these algorithms," Lee said, predicting machine learning will "change our world … and even the course of our species in ways that are hard to predict today." Analyzing data is "one of the magic pieces" of machine learning, he said.