Some may bristle at the concept of artificial intelligence, but it may already be too late to turn back the clock. We've been using crude versions of artificial intelligence for decades, from cruise control in our vehicles to automatic drying cycles in our laundry rooms. But interest in and expansion of AI are increasing, along with concerns about its implications. In a recent essay published on Slate, Microsoft CEO Satya Nadella lays out his framework of responsibilities of AI for machines and for human, summarized as "Nadella's 10 Laws of AI" by Geekwire. Nadella cited Isaac Asimov's Three Rules of Robotics from the 1940s: "First, robots should never harm a human being through action or inaction. Second, they must obey human orders. Third, they must protect themselves."

Third, the agency should ask for transparency in the AI research at both the governmental and the company level. The issue of nuclear proliferation and therefore the creation of the International Atomic Energy Agency (IAEA) followed the secretive Manhattan Project and the use of nuclear bombs to end the war in the Pacific, if humanity really wants to protect itself from the military use of strong AI and its tragic consequences it has to define a set of rules and policies which would maintain research within reasonable and collectively accepted limits. The IAEA imperfectly manages an existing threat, the AI agency would aim at preventing the realization of what could be an even greater danger.

TensorFlow, Google's open source library for machine learning, is now backing Apple's iOS mobile platform. While TensorFlow already has been available for Android, version 0.9, revealed this week, accommodates both iOS and the Raspberry Pi hardware platform for the internet of things. "To build TensorFlow on iOS, we've created a set of scripts, including a makefile, to drive the cross-compilation process," said Pete Warden, Google software engineer. "The makefile can also help you build TensorFlow without using [the Bazel build tool], which is not always available." Mobile capabilities in TensorFlow have been critical, Warden explained.

How do I approach a data science task? How do I approach a data science task? What is needed of me? How do I measure success? How do I approach a data science task?

It is no surprise that data analytics and machine learning are fast becoming key components of every innovative company's toolkit, given the massive increase in the amount of data that companies are generating. Because of the sheer volume and complexity of data being created, it is often beyond human capacity to find relevant trends or insights within what has been tagged as'Big Data'. Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. So previously, when humans were directing computers, they were limited to very direct questions and answers (for example, "what is my top selling item?") and required the person using the machine to dictate which method to use to the solve the problem. Now, machine learning enables computers to find answers in ways that are unguided by human intervention.

Machine learning has undergone a flurry of progress recently thanks to the growth of big data, fast hardware, and clever algorithms. And with tools such as Spark and Hadoop, the JVM is no stranger to machine learning, using tools including h2o, dl4j and other Spark-based libraries. With these tools, developers can harness the power of distributed hardware and deep learning to discover new and untapped patterns and relationships in big data. In this session, learn how to train a classifier to recognize handwritten digits and how you can build your own models using open source data sets. No prior experience is required.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

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.