Big data can't deliver on its potential unless enterprises have the right tools to extract insights. Woodside, an Australia-based oil and gas giant, realizes this and is using advanced machine learning technology to leverage its data via predictive analysis. Front and center in the company's toolkit is IBM Watson, a cutting-edge machine learning and natural language processing platform that analyzes vast amounts of unstructured data. According to CIO, Woodside is using a variety of big data tools -- including Amazon Web Services (AWS), Apache Spark and Watson -- to improve operational efficiency and predict potential catastrophes at its production facilities. Elsa Jordan, principal data scientist at Woodside, told attendees of the Chief Analytics Officer Forum in Sydney how the company has implemented these data science technologies in recent years and how the Watson engine has become a key component of the organization's big data platform.

Michael Kropp typically spends his days behind the wheel of a big, freight-hauling truck, navigating the high-speed curves, offramps, and stop-and-go traffic typical of European highways. On a recent trip to Rotterdam, he was able to relax and take in the sights. Kropp was one of about 30 drivers participating in a test of a new automated driving technology called platooning, which links trucks via Wi-Fi, GPS, sensors, and cameras so they can travel semiautonomously behind one another. The leading rig dictates speed and direction, while the rest automatically steer, accelerate, and brake in a closely spaced convoy. "It was a little eerie to hand over part of my role as driver," says Kropp, a 55-year-old test driver for Daimler who piloted the second vehicle in the caravan.

Lucy, a golden retriever from Connecticut, is a dog of the future. Imagine this: As she trots down a suburban street, a girl with a scooter can't help but stare. Attached to Lucy's collar is a leash, and attached to her leash is a small quadcopter drone. When the drone moves to the left, she looks up at it and follows along. It's not that Lucy's owners are lazy, or that they've doled out their tasks to robotic underlings.

One of the intern we hired 6 months ago (she now work full time), submitted her CV with only 1 skills listed above (Monte-carlo simulation). She did a Physics' MSc at University College London and ended up doing a 3 month intern at CERN at the end of last year after she completed her study. She did analysis on data generated from the Higgs Boson experiment using monte carlo. We looked at her CV and I said to my CTO, bring her in for interview. Her CV would probably be put in the bin if she applied for job somewhere else because hers didn't show machine learning skills or statistic skills or majority of the skills from the above 22 skills, on her CV.

Artificial intelligence (AI) has a lot to offer over human beings as a brand representative. It doesn't need incentives, bonuses, or stock options. However, just like your junior brand manager, it can sometimes tweet abhorrent content you would rather forget. One crisp spring Wednesday, Microsoft unveiled Tay, an artificial intelligence chatbot meant to simulate an energetic young woman with "zero chill." The experiment ended quickly, and poorly, when Tay became a crude, racist monster.

So when it came to tapping historical and streaming data stores to improve operational efficiency, as well as predict and circumvent potential issues in its production facilities, the company went for the most cutting-edge machine learning technology on offer: IBM's Watson. Speaking at the recent Chief Analytics Officer Forum in Sydney, Woodside's principal data scientist, Elsa Jordan, shared with attendees the company's journey to build a data science capability from scratch in one year that could be utilised by employees right across the organisation. Woodside established its data science practice in January 2015 and as part of its approach, is running the largest commercial instance of the Watson advisor engine. "Our premise was think big, prototype small, scale fast," Jordan said. "Key to that was using machine learning algorithms. What appealed was that we could learn from history, predict from streaming data and keep on learning as new information becomes available."

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

In 2025, in accordance with Moore's Law, we'll see an acceleration in the rate of change as we move closer to a world of true abundance. Here are eight areas where we'll see extraordinary transformation in the next decade: In 2025, 1,000 should buy you a computer able to calculate at 10 16 cycles per second (10,000 trillion cycles per second), the equivalent processing speed of the human brain. The Internet of Everything describes the networked connections between devices, people, processes and data. By 2025, the IoE will exceed 100 billion connected devices, each with a dozen or more sensors collecting data. This will lead to a trillion-sensor economy driving a data revolution beyond our imagination. Cisco's recent report estimates the IoE will generate 19 trillion of newly created value. With a trillion sensors gathering data everywhere (autonomous cars, satellite systems, drones, wearables, cameras), you'll be able to know anything you want, anytime, anywhere, and query that data for answers and insights. SpaceX, Google (Project Loon), Qualcomm and Virgin (OneWeb) are planning to provide global connectivity to every human on Earth at speeds exceeding one megabit per second. We will grow from three to eight billion connected humans, adding five billion new consumers into the global economy. They represent tens of trillions of new dollars flowing into the global economy. And they are not coming online like we did 20 years ago with a 9600 modem on AOL. Existing healthcare institutions will be crushed as new business models with better and more efficient care emerge. Thousands of startups, as well as today's data giants (Google, Apple, Microsoft, SAP, IBM, etc.) will all enter this lucrative 3.8 trillion healthcare industry with new business models that dematerialize, demonetize and democratize today's bureaucratic and inefficient system. Biometric sensing (wearables) and AI will make each of us the CEOs of our own health. Large-scale genomic sequencing and machine learning will allow us to understand the root cause of cancer, heart disease and neurodegenerative disease and what to do about it. Robotic surgeons can carry out an autonomous surgical procedure perfectly (every time) for pennies on the dollar. Each of us will be able to regrow a heart, liver, lung or kidney when we need it, instead of waiting for the donor to die. Billions of dollars invested by Facebook (Oculus), Google (Magic Leap), Microsoft (Hololens), Sony, Qualcomm, HTC and others will lead to a new generation of displays and user interfaces.

Business documents written in foreign languages are no longer the problem they once were thanks to technologies like Google Translate, but what about contracts written in legalese? That's where LawGeex hopes to help with an AI-based online tool. LawGeex offers what it calls the world's first contract review platform based on artificial intelligence. The goal, it says, is to help businesses and individuals "get a fair deal" before signing an agreement. Toward that end, it combines machine-learning algorithms with crowdsourced data, text analytics, and the knowledge of expert lawyers to make in-depth contract reviews accessible to everyone.

Photographs used in Tsvetkov's study. You may think you do, but a recent experiment by a Russian photographer suggests otherwise. In a project entitled, "Your face is big data," Rodchenko Art School student, Egor Tsvetkov, began by photographing about 100 people who happened to sit across from him on the subway at some point. He then used FindFace, a facial-recognition app that taps neural-network technology, to try to track them down on Russian social media site VK. It was ridiculously easy to find 60 to 70 per cent of the subjects aged between 18 and 35 or so, he found, although for older people it was more difficult.