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.

"Really, we're building collision avoidance for industrial drones," said Alexander Harmsen, CEO and co-founder of Iris Automation. "We see this huge need for industrial drones for mining exploration, pipeline inspection, agricultural surveying, forestry, or even package delivery." Without a way to avoid mid-air collisions, drones risk crashing into a Cessna, a flock of geese or a 747. Worst case scenario: a drone gets sucked into a jet engine causing catastrophic engine failure as high-velocity bits of metal penetrate fuel tanks, hydraulic lines and the cabin. Iris Automation's solution is an AI computer that blends real-time images and 3D maps to track incoming objects.

First, it is important for procurement organisations and their leaders to embrace the reality and potential for AI and cognitive procurement. Understand that, like most technologies, AI may bring changes, but it also presents significant opportunities. Next, based on the capabilities outlined above, think about what projects or processes in your organisation could most benefit from cognitive procurement. As you apply AI to certain procurement tasks and processes, you'll begin developing internal capability and expertise. It will also change the profile and enhance the skill set of your procurement professionals.

Google has announced that it had acquired a visual recognition machine learning technology start-up, Moodstocks for an undisclosed amount. Incorporated in 2009, the French start-up originally introduced on-device image recognition in 2012, which enables smartphones to automatically recognize the content their camera detects. With the recent acquisition, Google had made a substantial investment in the machine learning technologies since accurate object recognition is one of the difficult problems for machine learning. For the past 3 years, the small team of researchers and engineers based in Google has taken advantage of deep learning to extend the reach of their object recognition algorithms, licensing machine-readable executable object code including the launch of a software development kit for OEMs to enable them to embed the code into their products. The Moodstocks API, meanwhile, enable users to integrate visual search into their applications in addition to analyzing the images related to color, shape, and texture.