At dawn on 17 March the inhabitants of Los Angeles were woken by a mild tremor. Less than three minutes later the Los Angeles Times website published an initial piece on the subject, at first sight a wire drafted in haste by a press agency: "A shallow magnitude 4.7 earthquake was reported Monday morning five miles [8km] from Westwood, California, according to the US Geological Survey. The temblor occurred at 6.25am Pacific time at a depth of 5.0 miles. According to the USGS, the epicentre was six miles from Beverly Hills, California, seven miles from Universal City, California, seven miles from Santa Monica, California, and 348 miles from Sacramento, California. In the past 10 days, there have been no earthquakes magnitude 3.0 and greater centred nearby. This information comes from the USGS Earthquake Notification Service and this post was created by an algorithm written by the author."

Spark Summit West 2016 was held on June 7th-8th in San Francisco. While a lot of it was about the latest and greatest that's coming in Apache Spark 2.0, there was still quite a lot of useful information about the current Spark 1.6 version and ML/DL. Also, my team at Salesforce presented "A graph-based method for cross-entity threat detection" at the Spark Summit. Checkout Slides Video to learn more. Most of the sessions that I attended were in the developer track and here are some notes from those.

Microsoft envisions Cortana doing much more than reminding us to pick up toilet paper on the way home from work. Back in 2014, we Windows Phone fans could barely contain ourselves as we eagerly awaited Cortana's arrival on Windows Phone 8.1. At the time, like many writers, I had a vision of what Cortana would mean for Microsoft and mobile computing. So, well, I wrote about it. Alas, time has moved on, and that initial fervor that fueled the "Cortana conversations" of many Windows phone fans has transitioned through other topics. The Lumia 950 and 950 XL had their time in the limelight. HoloLens enters and re-enters the conversation. Windows 10 updates are a consistent topic, further fueled by Gabe Aul's passing of the torch to Dona Sarkar as the new face for the Insider Program.

For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. So the UK has just given itself a national headache. Whether you think the Brexit was the right decision or a dangerous and unmitigated screw-up (as I do), the consequences of the referendum will be non-trivial and take years to complete. But the mechanics of the UK exiting the European Union aside, the question of how people now feel about the Brexit is interesting. Are they awash in jubilation or has buyer's remorse set in?

As we have written about extensively here at The Next Platform, there is no shortage of use cases in deep learning and machine learning where HPC hardware and software approaches have bled over to power next generation applications in image, speech, video, and other classification and learning tasks. Since we focus on high performance computing systems here in their many forms, that trend has been exciting to follow, particularly watching GPU computing and matrix math-based workloads find a home outside of the traditional scientific supercomputing center. This widened attention has been good for HPC as well since it has brought new attention to the field, which many outside tend to think of as academic (even if many of us know it is far broader than that). But what was interesting this week as we wrap up at the International Supercomputing Conference in Germany, which focused heavily on HPC and AI, was how much deep learning folks need from HPC, but how little there is (yet) to feed AI and machine learning back into supercomputing. To be fair, in our conversations, we asked users at several national labs and supercomputing sites if the emphasis this week at ISC '16 on deep learning was relevant for their workloads.

Machine learning, the artificial intelligence technology behind Tesla's self-driving cars and Amazon's Alexa, has great potential to put big data to work. But the growing field may have another bonus: providing new pathways for women to get into tech. "Not everyone has to have that PhD in computer science," senior machine learning recruiter at Microsoft Amanda Papp mentioned to me in conversation at the International Machine Learning Conference in New York. "We have folks that have more physics backgrounds or biomathematics or permutational biology. As machine learning teams grow, it could lead to more job opportunity at tech companies for women with majors in physical science, biology, and math.

It is the future of shipping - and there's not a single sailor on board. Rolls Royce has revealed planed for fleets of'drone ships' to ferry carry around the world - all controlled from a central'holodeck'. It believes an entirely unmanned ship could take to the seas by 2020. Rolls Royce said it has already begun testing the technology needed to make the ships a reality, and expected them to take to the sea by the end of the decade. Cameras would beam 360-degree views from the drone ship back to operators based in a virtual bridge.

Neuroscientists at DeepMind, a Google-owned AI lab in London, have teamed up with academics at Oxford University and UCL to try and determine how the human brain navigates an underground train network. The group -- whose work was published in the journal Neuron this week -- asked humans to plan a journey in a virtual subway network. Participants were tasked with getting from A to B while MRI scans of their brain were taken. These scans showed which parts of the brain are involved in planning and making decisions. The group, which included Google DeepMind CEO Demis Hassabis, concluded that the brain splits the task of completing a journey into different jobs, with different parts of the brain handling different elements of the task.

Matters of machine intelligence are topics of great interest these days. It reminds me of a statement I read a couple years back from renowned technology writer and author Kevin Kelly: "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Although that prediction proved to be a bit off the mark (or perhaps it's still too early for its time), the idea is certainly compelling. For a better approach to making your company smarter, look to APIs. Nearly every day I hear of a new startup announcing some new intelligence offering.

When people discuss the workplace of the future, they frequently focus on the collaboration technologies and the layout of these new workspaces. We discuss the "intolerance" of millennials and how work tools must change to support the digital native. While the selection of tools is important, this dialogue lacks a discussion of the type of jobs employees will perform and how they'll be trained to do these jobs. We're entering the next generation of the industrial revolution and today's workforce is largely unprepared to fill the next wave of jobs. Many careers in the future will be based on software programing, machine learning and data analysis using tools that barely existed ten year ago.