Learn what the #Chatbot Craze is all about!

Renewable power sources such as wind now provide about one-third of Germany's electricity. The rows of towering wind turbines and legions of glistening solar panels spread across Germany's landscape are striking emblems of the country's shift to non-nuclear, low-carbon power. But although Germany is the world's poster child for renewable energy, its grids cannot yet cope with the erratic nature of wind and solar power. In June, German meteorologists, engineers and utility firms began to test whether big data and machine learning can make these power sources more grid-friendly. "To operate the grid more efficiently and keep fossil reserves at a minimum, operators need to have a better idea of how much wind and solar power to expect at any given time," says Malte Siefert, a physicist at the Fraunhofer Institute for Wind Energy and Energy System Technology in Kassel, Germany, and a leader on the project, called EWeLiNE.

The hedge fund Numerai is unusually large in staff compared to its rivals, boasting over 7,500 developers. But what's most unusual is that those employees can be entirely anonymous. As TechCrunch reports, the San Francisco startup sends out encrypted trading data to coders that have signed up to work for it. They each develop different machine-learning techniques to make forecasts based on the data, then send predictions back to Numerai. If they're useful, the data scientist gets paid in Bitcoin.

It's not only bud tenders and Uber drivers who are in danger of losing their jobs to automation these days. A small company in Ohio called AI Writer has created a neural net that can churn out the search engine optimization (SEO) filler currently being created by content mills in places like India and the Philippines. And they managed to do it straight out of college on a shoestring budget. AI Writer is a neural net (an AI architecture modeled after the human brain) that is not only capable of teaching itself how to write its own unique internet marketing articles, but those articles are polished enough to fool companies interested in buying that content into thinking it was generated by a human. AI Writer was co-founded by Paul DeMott, who had created a small internet marketing company as a summer job in college and Nick Shah, who had studied neurology for about a year and a half at medical school before dropping out to teach himself about artificial intelligence.

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. 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. This keeps a comfort level for us but the hidden risk of underpricing in our premium estimates is hardly given the attention it merits.

Banjo founder Damien Patton explains how he decided to focus on building the world's first "crystal ball" after identifying a suspect during the 2013 Boston Marathon bombing.

Embedded systems can be characterised by connectivity and the internet of things (IoT). Whether they are smart, low energy, edge devices, intermediate gateways or compute nodes, all based on multicore system-on-chip (SoC) architectures requiring performance, reliability and security. As a result the embedded systems industry seems to be headed into two key areas: intelligence and autonomy. A couple areas of interest are machine learning and what I call the "Robotics of Things." In the area of machine learning, a new category of computing architectures called neuromorphic processors, or brain-inspired computing, will become mainstream.

Artificial intelligence is getting a kickstart for the holiday season with an elf-themed chatbot launching at Mall of America. E.L.F, or Experiential List Formulator, is an IBM Watson-enabled platform, created in collaboration with Watson developer partner Satisfi, which helps visitors plan a more personalized shopping experience. It understands and interprets their queries using the Watson Conversation API and AlchemyLanguage API, both through Facebook Messenger or online on mobile and desktop via elf.mallofamerica.com. Visitors to the shopping center in Bloomington, MN, are guided through a series of questions from E.L.F to understand things like how much time they have and what activities they prefer. The service then presents them a series of suggestions, including ideal stores, theme park rides and shows.

One of our greatest challenges in today's society is responding to the impact of technology automation. Over the last decade, technology has increasingly displaced jobs resulting in a reduction of the middle class and the widening gap of income inequality. Other factors such as offshoring play a role in job loss but the impact of technology is in full steam and there is no end in sight. My concern is that our society hasn't come to appreciate the extent of the issue and doesn't have a thoughtful plan to address it. The future of the middle class depends on our ability to comprehend the changing world technology has presented, and how we respond to close the jobs gap.

"A breakthrough in Machine Learning would be worth ten Microsofts." – Bill Gates Machine Learning has been defined, by Arthur Samuel, as "A Field of study that gives computers the ability to learn without being explicitly programmed." In essence, the approach draws upon the tremendous computing power at the disposal of today's "machines" to compare vast amounts of data and iteratively improve decision making from instance to instance as more and more data gets available, and analysed. Clearly data is not in short supply today – there are more than enough scarily large numbers floating around to drive home that point adequately. This availability of data and a desire to leverage it is driving the market for Machine Learning northwards. BCC Research estimated that by 2019 this would reach $ 15.3 Billion, growing at close to 20% annually on average.