Applications of spatial data analysis are endless, cutting across many disciplines. With the advent of various sensors today – space, airborne, terrestrial alike, spatial analysis is becoming paramount in order to turn these data into valuable information. However, processing massive amount of data is a challenge as it requires complex procedures and multiple tools. The session shall highlight the latest development in big data analytics as well as some exemplary applications from different domains.

Pepper the robot participates in a Japanese ribbon-cutting ceremony earlier this year. Its manufacturer, SoftBank Robotics, is opening new offices in San Francisco and releasing a development kit for Android programmers. MOUNTAIN VIEW, Calif. - Pepper the robot is coming to our shores later this year, and its creators want the help of Android developers to help make it smarter. Japan-based SoftBank Robotics announced Wednesday at Google I/O, the company's annual developer's conference, that it is opening a new Pepper-focused outpost in San Francisco and unveiling an Android SDK, or software development kit, in the hopes of enticing programmers to write code for the robot. "Pepper is ultimately an unfinished product, and we just wanted to incentivize developers to expand the ways in which people can engage with a humanoid robot," says Steve Carlin, vice president of SoftBank Robotics Americas, which has an existing office in Boston.

Google is expected to dive deeper into virtual reality and artificial intelligence Wednesday during an annual conference that serves as a launching pad for its latest products and innovations. Although Google keeps its plans under wraps until the big event, the conference agenda makes it clear that virtual reality and artificial intelligence, or "machine learning," will be among the focal points.

Pepper is finally coming to America. SoftBank said today that its chatty humanoid robot, unveiled with great fanfare by the company's founder and CEO Masayoshi Son two years ago, is expected to debut in the North American market later this year. SoftBank also announced that a new developer portal is now available to anyone interested in creating applications for the robot. And tomorrow at Google I/O, SoftBank engineers will take the stage, along with Pepper, to introduce a tool that they hope will entice more developers to build apps for the robot: an Android SDK. "We're so excited to see what the development community can bring on to our platform," Steve Carlin, vice president of marketing and business development for SoftBank Robotics America, told IEEE Spectrum, adding that "ultimately what is going to really power Pepper is the creativity of this community."

On Wednesday during the Google I/O opening keynote, the company announced two new messaging platforms: Allo and Duo. Before diving into some of the features that set the two new apps apart from existing Google messaging platforms, let's take a moment to count just how many messaging services Google now has. On second thought, that would take too long--it's far too many. Anyone watching the keynote probably noticed the number of times the various presenters referred to Google's Hangouts and Messenger apps: excatly zero times. Google knows Hangouts and Messenger are dead.

Google CEO Sundar Pichai speaks during Google I/O 2016 at Shoreline Amphitheatre on May 19, 2016 in Mountain View, California. "We're at a seminal moment for AI," Google CEO Sundar Pichai told 7,000 people attending I/O, the tech giant's annual developers conference at a sun-splashed Shoreline Amphitheater, an outdoor concert venue near Google's worldwide headquarters here. Pichai, who has repeatedly stressed the importance of AI and machine learning, anticipates a momentous shift in computing: super-smart machines on every device we use guiding every moment of our days. "We're entering a golden age of machine learning and artificial intelligence," Amazon CEO Jeff Bezos said in a public conversation Wednesday at the Washington Post's Transformers Conference in Washington D.C. Google was an early pioneer in artificial intelligence, which drew on its massive data files derived from consumer searches on Google.com. But it's seen its mantle slip after breakthroughs by its rivals, such as Amazon's Echo and Facebook's bot platform.

SAN FRANCISCO -- Amazon's Jeff Bezos is a Star Trek-loving space geek, but unlike Elon Musk, he's got no plans on for heading to Mars. People haven't really thought this through," he said. Bezos spent 45 minutes in a convivial public conversation at the Washington Post's Transformers Conference in Washington D.C., which was also webcast live. He was interviewed by Martin Baron, executive editor of the Post, which he bought in 2013. After a few jokes about whether or not he'd have a job when the conversation was done, Baron asked Bezos about Amazon's Echo device and its cloud-based voice recognition agent Alexa. The Echo got more competition Wednesday in Mountain View, Calif. Amazon's CEO said the original inspiration for the Echo was the talking computers of Star Trek. While the Echo team is still quite a ways away from reaching that goal, he didn't feel too badly because after all, Star Trek was set more than 250 years in the future. "We still have a couple of centuries, but I don't think we'll need that much time," Bezos said. Bezos said he grew up playing pretend Star Trek every day with his friends when he was in fourth grade in Houston. "We would fight over who got to be Captain Kirk or Mr. Spock, and somebody played the computer too.

Machine Learning is rarely sufficient, in isolation, to segment your target population into (for example) low risk and high risk groups, especially in highly imbalanced fraud-like problems. This is exacerbated when the emphasis is on identifying the negligible risk population, rather than the potential frauds. Following on from Lee Brown's blog, in this post I want to talk about how in an assurance scoring framework you can combine multiple data science techniques to identify the negligible risk cohort that can be fast tracked through processing, allowing investigators to focus their resources elsewhere. The figure below shows how these techniques can be chained together into an assurance scoring framework. Firstly, business rules can be used to solve a variety of problems, and also incorporate business users intuition. I've found that a successful approach is to think of the outcome of a machine learning classification algorithm as providing a high risk and a low risk bucket.

Using text mining and machine learning based on the SAP HANA platform, the initiative aims to help companies review job descriptions, performance reviews and similar people processes for potential bias and suggest changes to encourage equity. The announcement was made at the 28th annual SAPPHIRE NOW conference. These new capabilities will complement existing SAP SuccessFactors offerings that already help address inequity. Analytics and reports focused on diversity and inclusion are available to help organizations identify and track where biases exist in talent acquisition and management processes -- recruiting, compensation, succession and the like -- coupled with guidance on actions to take to address those biases. SAP is also exploring applications for mentoring programs that will help people from historically disadvantaged groups more effectively navigate and develop their careers, as well as tools for balancing family and work that will integrate elements of benefits, scheduling and management into a single process.

As Google announced at its I/O developer conference today, the company recently started building its own specialized chips to expedite the machine learning algorithms. These so-called Tensor Processing Units (TPU) are custom-built chips that Google has now been using in its own data centers for almost a year, as Google's senior VP for its technical infrastructure Urs Holzle noted in a press conference at I/O. Google says it's getting "an order of magnitude better-optimized performance per watt for machine learning" and argues that this is "roughly equivalent to fast-forwarding technology about seven years into the future." Google also manages to speed up the machine learning algorithms with the TPUs because it doesn't need the high-precision of standard CPUs and GPUs. Instead of 32-bit precision, the algorithms happily run with a reduced precision of 8 bits, so every transaction needs fewer transistors. If you are using Google's voice recognition services, your queries are already running on these TPUs today -- and if you're a developer, Google's Cloud Machine Learning services also run on these chips.