This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. 'In pricing, are we swapping specific risk for systematic risk?'[1] The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty.

One of the most intriguing public discussions to emerge over the past year is humanity's wrestling match with the threat and promise of artificial intelligence. AI has long lurked in our collective consciousness -- negatively so, if we're to take Hollywood movie plots as our guide -- but its recent and very real advances are driving critical conversations about the future not only of our economy, but of humanity's very existence. In May 2014, the world received a wakeup call from famed physicist Stephen Hawking. Together with three respected AI researchers, the world's most renowned scientist warned that the commercially-driven creation of intelligent machines could be "potentially our worst mistake in history." Comparing the impact of AI on humanity to the arrival of "a superior alien species," Hawking and his co-authors found humanity's current state of preparedness deeply wanting.

Wang Jun spent 16 years expanding the world's understanding of what living things are made of -- sequencing genomes including those of the giant panda and potatoes. Now he's attempting to build on that: using DNA as one component to create online avatars that could act as health-care test dummies for people. Asia's biggest internet company believes he's onto something. Wang's iCarbonX wants to construct a "digital you" containing biological samples such as saliva, proteins and DNA; bolstered by environmental measurements such as air quality; and lifestyle factors such as workout regimes and diet. The Shenzhen, China-based company is developing algorithms to analyze the data, with the intention of recommending tailored wellness programs, food choices and possibly prescription medicines.

As Silicon Valley investors and tech giants continue to pour cash into burgeoning artificial intelligence technologies such as machine learning and chatbots, the relatively nascent A.I. industry is emerging as the latest mega-hot new ticket in town--the heir to online delivery apps, anything-hailing services, and virtual reality start-ups. But much like another buzz-worthy predecessor, Big Data, many A.I. cheerleaders and investment check signatories probably don't quite understand it. But in Silicon Valley, when has that ever stopped anyone? The obsession in the Valley with artificial intelligence is almost palpable. Just last week, Twitter announced its acquisition of London-based machine learning start-up Magic Pony Technology.

The biggest trend in technology right now is the race to make machine learning more sophisticated and as the experts say, "less stupid." Machine learning is the ability of computers to learn something without being explicitly programmed. Based on human neural learning, machine learning takes millions of examples and uses layers to build on itself to eventually interpret something without a human first identifying it. Machine learning is infiltrating every aspect of life. From machine translation, email spam filters, ATM check depositing, category filters for photos, emails and apps to facial recognition, machine learning is everywhere taking our basic human actions and instantly making sense of them through technology.

In the future, the US Air Force hopes to have armed drones flying in formation with human pilots, responding to their verbal and digital commands to fight the enemy and strike targets. That would require an artificial intelligence capable of interpreting commands and applying knowledge of combat tactics--something that is already being proven in a project funded by the Air Force Research Lab. ALPHA, an artificial intelligence trained by a retired Air Force expert in air combat, was originally developed as what amounts to ultimate video game AI--an autonomous simulated enemy for use in training fighter pilots. The AI is so good that it has consistently beaten human pilots in simulated air combat--even when heavily handicapped by simulated physics. And now AFRL is investigating using ALPHA as the AI for Unmanned Combat Aerial Vehicles (UCAVs) in the physical world, potentially flying missions alongside human pilots.

Towards making systems forget with machine unlearning.

Microsoft's acquisition of LinkedIn is the largest in giant's history and was hardly expectable[1]. Brilliant analysis of the deal may be found in multiple sources. However, one may suggest that the current narrative is too focused on immediate results and current opportunities rather than the future options for the software giant. A dive into one of the Bill Gates' favorite books may add valuable insights and explain the role of LinkedIn in the Microsoft's push into artificial intelligence (AI). However, the deal of that magnitude deserves to be considered from the perspective of the future.

Microsoft has a birthday present for Windows 10 users: more capabilities for its Cortana digital assistant and new ways to ditch passwords. The company is also changing the notices it sends to users of previous versions, following complaints that it was too aggressive in pushing them to get the free Windows 10 upgrade. Microsoft's "Anniversary Update," scheduled for release Aug. 2, will let users activate Cortana with a spoken command ("Hey Cortana") even while their screen has gone into sleep mode. Cortana will be able to recall more types of information, such as frequent flier numbers or parking locations. Users can also ask Cortana to remember specific photos, such as a wine bottle to buy again later.

Professor Soibelman and his research group at the Sonny Astani Department of Civil and Environmental Engineering will have access to Crunchflow's large metadata database that has anonymous performance information from thousands of projects and employees allocated in professional service organizations. The researchers' mission is to find in this data preliminary patterns and results that help to understand how companies can boost efficiency levels. They also want to demonstrate the potential for extracting knowledge from such a database. Crunchflow's CEO Daniel Knijnik believes that the collaboration will place the company in the forefront of a technology that has the potential to revolutionize how artificial intelligence is applied to business. "We will have top researchers entirely dedicated to innovation, using the latest and most modern technology", he said.