With advances in cloud storage and cloud computing, actuaries are in a position to leverage their current skills to new areas within and outside of traditional roles at insurance companies. However, there is also the potential to misuse data and advanced analytical techniques. Actuaries must be aware and proficient in their understanding of the appropriate analytic techniques, data, and applications. This paper first presents an introduction to the cloud service models and their impact on the actuarial profession. It then discusses the use of the cloud in terms of financial modeling and actuarial processes, and in terms of the increased ability to collect more data to perform advanced analytics.

Following D-Wave's announcement of Leap 2, a new version of its quantum cloud service for building and deploying quantum computing applications, VentureBeat had the opportunity to sit down with Murray Thom, D-Wave's VP of software and cloud services. We naturally talked about Leap 2, including the improvements the company hopes it will bring for businesses and developers. But we also discussed the business applications D-Wave has already seen to date. Quantum computing leverages qubits to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Based in Burnaby, Canada, D-Wave was the first company to sell commercial quantum computers, which are built to use quantum annealing.

Preparing for a future driven by quantum computing took on new urgency recently when Google announced it had created a quantum computer capable of performing a computation in just a few minutes that would take classical computers thousands of years. The company hailed the breakthrough as the first realization of quantum supremacy -- the moment when quantum computers can solve problems that classical computers cannot. While some took issue with how Google structured its processing test and questioned the legitimacy of its claim, the announcement is at least a sign of progress in quantum and an indicator of what might be coming. The exponential increase in processing power that is theoretically possible with quantum computing has implications for drug discovery, cybersecurity and general AI to name a few areas. Quantum computing and machine learning will enable models that reflect complex conditions far better than today's models are capable of doing, Langione said.

Machine learning has been around for a while, with the earliest techniques developed in the 1950s. It is currently enjoying a particularly high profile, thanks to a whole range of possible applications from self-driving cars through to Go-playing computers. But what exactly is it? I've just finished diving into Josefin Rosen's blog post, a description of how machine learning makes for a smarter life, and asked her to put ML in context. Welcome to join my discussion with Josefin. Josefin Rosén (JR): I think the easiest way of thinking about it is that machine learning is basically a subfield of artificial intelligence.

There is quite a lot of confusion about the differences between machine learning, cognitive computing and artificial intelligence. Is there an easy distinction? Josefin Rosén (JR): I think the easiest way of thinking about it is that machine learning is basically a subfield of artificial intelligence. Then you can think of cognitive computing as artificial intelligence plus elements of natural language processing. So cognitive computing understands input like text, voice and video, and it can reason and create outputs that can be used and consumed by humans, not just computers.

The arrival of artificial intelligence and its ilk -- cognitive computing, deep machine learning -- has felt like a vague distant future state for so long that it's tempting to think it's still decades away from practicable implementation at the point of care. And while many use cases today are admittedly still the exception rather than the norm, some examples are emerging to make major healthcare providers take note. Regenstrief Institute and Indiana University School of Informatics and Computing, for instance, recently examined open source algorithms and machine learning tools in public health reporting: The tools bested human reviewers in detecting cancer using pathology reports and did so faster than people. Indeed, more and more leading health systems are looking at ways to harness the power of AI, cognitive computing and machine learning. "Our initial application of deep learning convinced me that these methods have great value to healthcare," said Andy Schuetz, a senior data scientist at Sutter Health's Research Development and Dissemination Group.