For four years, eBay has been collecting customer search data, along with search click-through rates and other customer interaction data, and feeding the information into its machine learning system. In an interview with InformationWeek, Dan Fain, the company's VP of engineering, outlined the steps being taken, and the business motivation behind them. The use-cases are worth exploring for any IT professional looking to help improve a company's bottom line by applying machine learning to customer-facing applications. Search, customer click patterns, language translations, item recommendations, and image analysis are among the key ways machine learning is being put to use at eBay, according to Fain. The first, and most important, application of machine learning is to improve search functions on eBay.com, according to Fain. "We have several machine learning models working behind the scenes to ensure we get the best search results," said Fain in our interview.

Australia's Macquarie Bank has deployed a new digital banking service, inspired by the data strategies used at the likes of Spotify and Netflix and tapping DataStax Enterprise and the NoSQL database. This includes search-how-you-speak technology; the ability to tag and track transactions; upload receipts and warranties; set saving and spending goals; and automatically categorise spending through machine learning technology. "We looked at the experience consumers receive from popular social media sites, search engines and video and music streaming services, and saw an opportunity to use leading edge technology to bring the same personalised and intuitive experience to banking," says Luis Uguina, Chief Digital Officer for Macquarie's Banking and Financial Services Group, who joined the FI in 2014 from Spain's BBVA. "To do this, we needed a scalable technology platform able to provide real-time value, and working with DataStax Enterprise has enabled us to be more nimble and utilise the most up-to-date technological capabilities available."

In the IT world it is very important to get things exactly right, unless of course you are dealing with fuzzy logic. If your banking system, for example, is out by a decimal place, this can lead to some very unhappy customers or some very happy ones and a less than happy bank. There are many areas where calculations and units of measure are vital, just ask one of the Mars exploration landing teams. Not so obvious are making sure that things like translation services are accurate, as Microsoft found out recently. The Bing search engine saw some very red-faced Redmondites when Saudi Arabian users found that "Daesh" has been translated as "Saudi Arabia". For those not familiar with the term, Daesh is one name for Isis.

Technology has fundamentally altered our lives and will continue to do so. Ethical considerations must remain guiding posts at all times. Undoubtedly, how we approach the regulation of emerging technologies will have wide implications for our definition of human dignity and the equality of individuals. The Green Working Group Robotics - Jan Philipp Albrecht, Max Andersson, Julia Reda, Michel Reimon and Terry Reintke - would like to invite you to a public hearing on "Merging of man and machine: questions of ethics in dealing with emerging technology". With this and further discussions we would like to develop a position on how society should respond to questions like How will our lives and our society change with the increasing fusion with modern technology?

Artificial Intelligence: Can a computer learn to be creative? Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning. In this captivating demo, he shows how neural nets trained to recognize images can be run in reverse, to generate them. The results: spectacular, hallucinatory collages (and poems!) that defy categorization. "Perception and creativity are very intimately connected," Agüera y Arcas says.

Numerai is synthesizing machine intelligence to command the capital of an American hedge fund. In the late 90s and early 2000s, new algorithms such as AdaBoost and Random Forests made breakthroughs in machine learning. The principle behind each of these algorithms was very simple: build many decision tree models that each learn something different, and then average them all together to create an ensemble model. These algorithms worked incredibly well. They were easy to understand, and computationally efficient.

The worlds of AI, Deep Learning and Machine Intelligence are rapidly evolving and it's hard to keep up! To help you get up-to-date we've rounded up the latest must-see AI news, interviews and videos. RE•WORK blogs Should We Be Rethinking Unsupervised Learning? Meet the Woman Behind the Personality of Microsoft's AI Assistant'Cortana' Deborah Harrison believes people should come to expect civility, humor, transparency and kindness from interactions with AI, just as they expect the ability to update a calendar. Democratising Deep Learning: Q&A With Prof. Neil Lawrence It has taken 20 years to go from defeating Kasparov at Chess to Lee Sedol at Go, but what have the real advances been across this time?

Sony will have some buzzworthy news of its own Wednesday in New York, when it unveils two new PlayStation 4 video game consoles and provides the finishing details on its PlayStation VR virtual reality system before the headset hits shelves next month. Similar to Apple, Sony's invitation to the event (or "PlayStation Meeting" as they titled it) was incredibly vague, but here's what we're expecting. Rumors over the last few months have pointed to Sony unveiling two new PS4s at the event. The first is a thinner version of the current PS4, dubbed the Slim. According to leaks online last last month, the Slim would largely be a redesign of the current PlayStation 4 with a thinner, sleeker design.

On Monday, Intel announced that it will purchase Movidius, an Irish company that develops computer vision processors. With newly acquired chips and deep learning algorithms, Intel will be able to imbue its RealSense cameras with independent, low-power image processing. In simpler terms, the company could build drones and other devices that can "see" on their own – no human required. At the most basic level, the biological eye is used to gather information and make decisions. Using camera "eyes," computers can also extract complex understanding from visual data.

The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Machine learning's Predictive Analysis. The link takes you to a corresponding product in The Art of Service's store to get started. The Art of Service's predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications. The platform monitors over six thousand technologies and applications for months, looking for interest swings in a topic, concept, technology or application, not just a count of mentions.