Artificial intelligence (AI) is becoming increasingly ubiquitous within the technology industry, with capabilities that are much more practical than most consumers may think. Smarter e-mail spam filters and autonomous vehicles are just two examples of how deep-learning systems and AI technologies enable machines to better interact with their surrounding environment and provide vast benefits to users. By using a series of layers within a neural network to analyze data, deep-learning systems continue to change the way computers see, hear, identify, and even respond to objects in the real world. While the combination of such skills has made it possible for machines to perform increasingly complicated, human-like functions, the future of deep learning is now being dictated by user-driven input methods. Neural-network algorithms are among the most interesting machine-learning techniques.

April Blackburn, CIO of the Florida Department of Transportation, discussed the opportunities for cognitive analytics and connected devices across the state's IT systems. The agency is already plotting the course for a tech-centric strategic plan and is involved in data sharing with private industry to improve transit in the state. Eyragon Eidam is the assistant news editor for Government Technology magazine, and covers legislation, social media and public safety. He can be reached at eeidam@erepublic.com.

Facebook's recently introduced chat bots have already been utilized to help users order Whoppers and fries -- and now, they're even assisting in the beer-brewing process, CNET reports. London-based company IntelligentX is utilizing said chat bots, along with artificial intelligence, to make its beers taste better. After tasting one of four beers -- Amber AI, Black AI, Golden AI, or Pale AI -- consumers then give feedback to a Facebook chat bot. The drinkers' evaluations are then run through an algorithm (appropriately called ABI, or Artificial Beer Intelligence), which turns them into suggestions on how the brewers can improve the next batch of beer. "In turn, using reinforcement learning and something called bayesian decision making, the AI will learn from experience when a tweak is successful," CNET explains.

To understand the real benefits of artificial intelligence, it is instructive to look at why it is so difficult and costly to manage the compliance burden in the traditional way. In the area of AML, for example, financial institutions have transaction monitoring systems that generate alerts when potentially unusual activity is detected. In order to be thorough and avoid heavy fines, the systems are extremely sensitive and thus generate large numbers of false positives. This means that compliance staff must scrutinize each alert, investigate the activity, and determine whether it is unusual and rises to the level of being reportable in the form of a Suspicious Activity Report (SAR). The problem is magnified since financial institutions typically have multiple systems, making it necessary to compile data from numerous sources in an investigation.

You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI. If your company isn't using machine learning to detect anomalies, recommend products or predict churn, you will start doing it soon. Because of the rapid generation of new data, availability of massive amounts of compute power and ease of use of new ML platforms (whether it is from large technology companies like Amazon, Google and Microsoft or from startups like Dato), we expect to see more and more applications that generate real-time predictions and continuously get better over time.

In my own experiments I've found ShakeWeight to really help generalization when using LambProp. You can train neural nets with trillions of layers on MNIST and not over fit. Plus you get your heart rate up and work the forearms.

In March, Microsoft revealed that it was using the open-world game Minecraft to train AI agents to learn how to do things like climbing a hill. The company also promised to make it available to the public so they could work on their own artificial intelligence projects and research, and it's finally available today. Project Malmo (formerly known as Project AIX) is a Minecraft mod that works on Windows, Mac and Linux, and supports just about any programming language you might want to use. So yes, that means you will need to know how to code – but Microsoft says that even novice programmers can get in on the action. You can learn more about Project Malmo here and grab the mod from this GitHub repository to try it for yourself.

Howard Forman is a professor of radiology, economics, public health and management at Yale University. My clinical specialty of radiology revolves around image interpretation. We interpret X-rays, MRIs, PET scans, ultrasound, computed tomography and other diagnostic images. Though two people may appear similar, no two diagnostic images are ever identical. Every liver, brain, or gall bladder is a bit different.

With recent developments like the launch of Facebook's chatbot store, Apple's acquisition of Emotient, and the release of Viv, a virtual assistant from the founders of Siri, there's no doubt that artificial intelligence (AI) has started to quickly proliferate across consumer applications. Nonetheless, many of these new applications are still more novelty than necessity as their functionality is rudimentary at best, though we've come to rely on them daily – from Amazon product recommendations to Facebook facial recognition (auto tagging). Until consumer-facing AI can usher in new technological advancements that provide deeper and more human-like interaction, it still has a long way to go before it reaches its tipping point. Enterprise AI, however, offers immediate applications that help solve problems that many companies and workers face today, such as data overload. Companies across a wide range of industries have already taken advantage of AI capabilities in order to help improve both internal and external processes.