It's a site that only lets in the genetically blessed based on some mysterious beauty metric – and today the personal data of 1.1 million BeautifulPeople.com It's only a slice of data from 2015, and the company says the leak's been patched up, but data once stolen can never be controlled: and so 1.1 million names of self-declared Beautiful People will now begin circulating. Like the Ashley Madison hack – which left 39 million people on a dating site for married people exposed and their names suddenly searchable – there's a joy in shaming people who would sign up for such a thing. "Online dating for beautiful people only," the website announces. "BeautifulPeople.com is the largest internet dating community exclusively for the beautiful," it reads.

OSX is much better than Windows, isn't it? That's a common wisdom, and it seemed to be confirmed once more when I installed XGBoost on both OS. Before I deep dive, let me briefly describe XGBoost. It is a machine learning algorithm that yields great results on recent Kaggle competitions. I decided to install it on my laptops, an old PC running Windows 7, and a brand new Mac Pro running OSX.

Here's a scene from our digital future: You sit down to dinner at a restaurant where your server was selected by a "robo-boss" based on an optimized match of personality and interaction profile, and the angle at which he presents your plate, or how quickly he smiles can be evaluated for further review. Or, perhaps you walk into a store to try on clothes and ask the digital customer assistant embedded in the mirror to recommend an outfit in your size, in stock and on sale. Afterwards, you simply tell it to bill you from your mobile and skip the checkout line. These scenarios describe two predictions in what will be an algorithmic and smart machine driven world where people and machines must define harmonious relationships. In his session at Gartner Symposium/ITxpo 2016 in Orlando, Daryl Plummer, vice president, distinguished analyst and Gartner Fellow, discussed how Gartner's Top Predictions begin to separate us from the mere notion of technology adoption and draw us more deeply into issues surrounding what it means to be human in a digital world.

I recently spoke at the Deloitte Shared Services and Outsourcing Executive Forum at Deloitte University. The audience's interest level in automation strategies was high. For the most part, Deloitte doesn't outsource directly, but only provides consulting services to its clients about outsourcing and shared services strategies, so the event and this essay seem appropriate venues to discuss the impact of "cognitive automation" technologies on the outsourcing industry. I use quotation marks around the term "cognitive automation" because though I am told that that is the preferred term these days at Deloitte, I am still getting used to it. I like the term because it suggests a broad approach that goes beyond current automation-oriented technologies and methods.

There's no doubt that there's a lot more "connected things" these days and that means a lot more data. Specifically, technology is moving out of the consumers' hands and into Healthcare, Oil & Gas, Transportation, Aviation and more. The spread of smart devices and sensors creates new forms of value and brings challenges for enterprises seeking to exploit this technology. However, while this boom in data has the potential to advance the industrial space in ways never before thought possible, few companies today have the right technologies in place or the right business models that can truly utilize the power of IoT. So what should enterprises know about the permeation of sensors and connected devices and how can they prepare now or risk being left behind by their faster moving competitors?

Google Glass could be coming to health systems, as startup Augmedix has secured US 17 million in a strategic round of funding that includes investments from Sutter Health, Dignity Health, Catholic Health Initiatives, TriHealth, and one other. This round of funding allows Augmedix to scale Google Glass powered service across the health systems and private clinics. The goal is to allow those in the health systems to have more time to focus on their patients, and less time spent working on documentation. "We believe that delivering quality, personalized patient care shouldn't be at odds with maintaining state-of-the-art electronic health records--both are important," said Ian Shakil, CEO of Augmedix. "With our wearable solution, healthcare organizations have the technology tools necessary to drive EHR efficiencies, enhance services, and improve patient care, all at the same time."

Search for documents and data within the enterprise has been tough. A survey by SearchYourCloud found that people would spend between five and 25 minutes everytime they needed to look for a document. Much of the problem has been that tools for enterprise search have been lacking. Either businesses didn't invest in them, or the tools weren't configured well enough to be useful, or the features of the enterprise search product used were poor. A survey by AIIM found that a quarter of businesses said that they have not invested in any search tools.

Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of Things (IoT) devices, are pushing businesses from those reactive analytics – whether you look back once a month to spot trends or once a day to check for problems – to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it's still current, before conditions change. "There's a demand for real-time dashboards," says Herain Oberoi from Microsoft's Cortana Analytics team. "A lot of businesses want to get the pulse of their business. But dashboards show things that have already happened."

This project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Since deep Q-network is trained on the raw pixel values observed from the game screen at each time step, [3] finds that remove the background appeared in the original game can make it converge faster. The architecture of the network is shown in the figure below. The first layer convolves the input image with an 8x8x4x32 kernel at a stride size of 4. The output is then put through a 2x2 max pooling layer.

In recent years we've seen new, disruptive innovations in the world of wearable technology; advances that will potentially transform life, business, and the global economy. Products like Google Glass, Apple Watch, and Oculus Rift promise not only to change the way we approach information, but also our long established patterns of social interaction. Indeed, we are witnessing the advent of entirely new genre of interface mechanisms that brings with it a fundamental paradigm shift in how we view and interact with technology. Recognizing, understanding, and effectively leveraging today's growing landscape of wearables is likely to be increasingly essential to the success of a wide array of businesses. Cognitive neuroscience is a branch of both psychology and neuroscience, overlapping with disciplines such as physiological psychology, cognitive psychology, and neuropsychology.