But with the rollout of iOS 10, Apple will begin using differential privacy to collect and analyze user data from its keyboard, Spotlight, and Notes. Roth is a computer science professor who has quite literally written the book on differential privacy (it's titled Algorithmic Foundations of Differential Privacy) and Federighi said Roth described Apple's work on differential privacy as "groundbreaking." Differential privacy builds on the introduction of deep linking in iOS 9 to improve Spotlight search. Although iOS 10 will only use differential privacy to improve the keyboard, deep linking, and Notes, Smith points out that Apple may use the strategy in maps, voice recognition, and other features if it proves successful.

Apple is stepping up its artificial intelligence efforts in a bid to keep pace with rivals who have been driving full-throttle down a machine learning-powered AI superhighway, thanks to their liberal attitude to mining user data. Not so Apple, which pitches itself as the lone defender of user privacy in a sea of data-hungry companies. While other data vampires slurp up location information, keyboard behavior and search queries, Apple has turned up its nose at users' information. The company consistently rolls out hardware solutions that make it more difficult for Apple (and hackers, governments and identity thieves) to access your data and has traditionally limited data analysis so it all occurs on the device instead of on Apple's servers. But there are a few sticking points in iOS where Apple needs to know what its users are doing in order to finesse its features, and that presents a problem for a company that puts privacy first.

Anirudh Koul's grandfather was slowly losing his ability to see. By 2014, he was having a hard time recognizing Koul's face in their weekly Skype calls bridging the vast distance between the Silicon Valley, where Koul is a data scientist at Microsoft, and the elderly man's home in New Delhi. So Koul started reading up on the challenges of vision loss and thinking about how the recent advances in deep learning, a potential-packed area of machine learning, could help give people a new way to recognize what's around them without actually seeing it. That was the modest beginning of Seeing AI. Two years later, Microsoft CEO Satya Nadella introduced the budding technology to thundering applause at this year's Build conference.

Recent developments in energy management systems and the IoT (Internet of Things), have enabled easy, and low cost visibility of real time energy consumption data of not only main power lines but also individual devices. For anyone skilled in the art of energy management, it is obvious that such data contains incredible value that can help facility managers significantly increase the operational and energy efficiency of their sites. However, due to the shortage and cost of analytical resources, it is always a great challenge to practically and easily deliver such valuable insights out of so much data. As more and more devices are being monitored, the task becomes nearly impossible to manage manually. An article which I recently published as part of the latest research work we're doing in Panoramic Power, introduces an innovative data-science approach that helps automatically generate actionable energy and operational efficiency insights out of real time device level energy consumption data, using machine learning techniques.

Today, we are all connected--often, even MORE connected than we'd like to be. We have our phones, our tablets, our laptops, even our cars--each creating a daily explosion of data. Not to mention data that's generated from transactions, sensor activity, customer behavior, and so on. So, it's not that surprising to understand that malicious attacks are becoming more severe and complex. When a breach occurs, months can go by without detection.

Wanna know more about machine learning and predictive analytics? We're thrilled to welcome Rob Craft - Group Product Manager at Google Cloud Platform during lunch break! Coming from San-Francisco, Rob Craft is the lead Product Manager for the Cloud Intelligence team in Google Cloud Platform. He is responsible for Cloud Machine Learning, Cloud Search, Internet of Things, and Cloud Pub/Sub. Rob will discuss how you can leverage the power of ML whether you have a machine learning team of your own or if you just want to use ML as a service.

Microsoft and Google have announced plans to expand their machine learning capabilities, through acquisition and new research offices respectively, reports Telecoms.com.

ZUMATA, a Singapore-based hotel distribution and technology company, along with DHISCO, the world's leading hospitality distribution company headquartered in Dallas, Texas, today announced a reciprocal agreement aimed to increase each other's hotel inventory while accelerating their mutual geographic expansion of distribution. ZUMATA, through its extensive network of wholesale partners and channel managers will supplement DHISCO's inventory by providing over 500,000 instantly bookable hotel properties. For ZUMATA, DHISCO will facilitate distribution of this hotel inventory to its large customer base largely based in North America and other Western markets. "DHISCO has been an industry powerhouse for years, and yet their management is keenly focused on innovation," said ZUMATA CEO Josh Ziegler. "This partnership underscores their commitment to staying at ahead of their competition by embracing the latest technological advancements. For us, this partnership represents a significant opportunity for our partners to gain access to DHISCO's amazing distribution. Complementing inventory and distribution, our artificial intelligence, or AI, powered technology will add exciting new capabilities that can increase customer satisfaction while increasing performance and conversions for all of us."

In transaction banking, the focus on technological development has centred on the possibilities of blockchain technology. However, this has overshadowed the arrival of AI into transaction-banking platforms. AI and machine learning are helping to further reduce manual checks and processes. The first target for implementation is sanctions and compliance. As companies become increasingly international, irrespective of size, checking against sanctions has become an essential activity for more than just the MNCs. AI can learn through experience what can pass through the sanctions filter, and what compliance obligations need to be checked.

The amount of data being handled in chip design is growing significantly at each new node, prompting chipmakers to begin using some of the same concepts, technologies and algorithms used in data centers at companies such as Google, Facebook and GE. While the total data sizes in chip design are still relatively small compared with cloud operations--terabytes per year versus petabytes and exabytes--it's too much to sort through using existing equipment and approaches. "You can take many big data approaches to handle this, but there may be a business problem if you do," said Leon Stok, vice president of EDA at IBM. He said EDA doesn't have the kind of concentrated volume necessary to drive these kinds of techniques, and typically that problem is made worse because the data is often different between design and manufacturing. But for those working on designs, the amount has grown significantly at a time when extracting key data in various parts of the design flow is crucial.