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.

There are two types of chatbots, one functions based on a set of rules, and the other more advanced version uses machine learning. Bots are created with a purpose. A store will likely want to create a bot that helps you purchase something, where someone like Comcast might create a bot that can answer customer support questions. You start to interact with a chatbot by sending it a message. Click here to try sending a message to the CNN chatbot on Facebook.

Artificial intelligence is not a term anyone should take lightly. By a strict definition, it means a computer system is able to perform tasks that normally require human intervention. As companies like HP, Staples, CNN, and Expedia start to roll out Facebook chatbots to help you with mundane tasks, don't expect anything like Roy Batty from Blade Runner. It's more like one of those robotic dogs that can perform a limited set of tricks (stand up and sit but not roll over) and are designed not to imitate a human or even replicate a human function but only to steer humans toward online information, making a purchase, or...making a purchase. While the programming for that chatbot became a puppet for Internet trolls, at least it could pretend to talk like a human.

With Earth Day come and gone the initiative to save our planet is fresh in our minds. And that's not limited to global warming and putting a stop to pollution; poaching remains a big problem when it comes to preserving the world around us. Luckily researchers have been testing out how we can use scientists to help the fight against poaching. According to Science Daily, organizations such as the National Science Foundation (NSF) and the Army Research Office have teamed up to look into how artificial intelligence (AI) could be used to stop poaching and illegal logging. Led by scientists at the University of South Carolina (USC), researchers have found ways to use game theory, or the mathematical theory of conflict and cooperation, to protect parks in a more proactive way.