The second largest economy of the Arab world is quietly switching from oil to Artificial Intelligence. In a world first, the UAE on Thursday appointed a minister of Artificial Intelligence, which is also the first such acknowledgement by the Arab world that these indeed are the technologies that are going to shape economies around us. Omar Bin Sultan Al Olama, 27, will spearhead UAE's ambition to be at the forefront of the global technological revolution, which will see it planning to build homes on the planet Mars by 2117. The UAE plans to have a fully functioning city of 600,000 people on Mars. "We aspire in the coming century to develop science, technology and our youth's passion for knowledge," tweeted Sheikh Mohammed bin Rashid al Maktoum, the country's vice president and prime minister, when he announced the project -- known as "Mars 2117" -- earlier this year.
The number jobs in artificial intelligence (AI) in the UK has risen dramatically in the last three years, according to Indeed. Since 2014, the number of available AI roles in Britain has increased by 485% - representing a significant spike in demand for employees with the appropriate skills for the job. Yet Indeed's data also reveals there are over two times as many AI jobs available than there are suitable applicants, with a ratio of 2.3 roles available per candidate searching in the last quarter. Interest in AI roles has risen more steadily by 178% in the past three and a half years, not quite high enough to meet the fivefold surge in postings. The popularity of software in innovations including smart home devices and customer service chat bots demonstrate how the industry is developing at pace.
Roberta doesn't have a last name, a face, or arms. She is the first piece of robotic software to work in the Norwegian company's treasury department, part of Statoil's push toward automation, robotics and artificial intelligence, said Mr. Kjøllesdal, acting head of internal treasury. Finance executives at companies including Nokia Corp. NOK -0.26%, Royal Dutch Shell PLC and Orange SA ORAN -0.62% are developing their own Robertas. Two thirds of large global companies expect to automate some or most of their finance-department tasks over the next two to three years, according to new research by The Hackett Group Inc. Hackett's report is based on benchmark and performance studies at hundreds of large global companies. These new technologies are designed to cut costs, liberate workers from time-consuming, repetitive tasks, and in many cases reduce finance- and treasury-department employee numbers.
Many years ago, during my first assignment at (super) major oil company, I was in charge of significant decisions for wells drilled in an onshore gas field. Each of these wells were drilled quickly, on average taking 5–7 days. The geology was well known, the reservoirs were, generally speaking, economic and the operational risks from drilling were rather low and manageable. Despite the apparent homogeneity (nothing in geology is truly homogeneous) of the regional geology of this field, operations geologists (such as myself), had to do some very manual work. As we approached the reservoir section for each well, I was required to confirm we were approaching the reservoir based on data obtained while drilling.
Mike Brooks proposes the use of machine learning software to improve plant reliability and to reduce unplanned downtime. There is a significant need to carry out failure prevention using data-driven truths instead of guesstimates, evidenced by the fact that a combination of mechanical and process induced breakdowns account for up to 10% of the worldwide $1.4 trillion manufacturing market, according to a 2012 report from The McKinsey Global Institute. While companies have spent millions trying to address this issue and ultimately avoid unplanned downtime, only recently have they been able to address wear and age-based failures. Current techniques are not able to detect problems early enough and lack insight into the reasons behind the seemingly random failures that cause over 80% of unplanned downtime. This is where using machine learning software to cast a'wider net' around machines can capture process induced failures.
The camera on the new Pixel 2 is packed full of great hardware, software and machine learning (ML), so all you need to do is point and shoot to take amazing photos and videos. One of the technologies that helps you take great photos is HDR, which makes it possible to get excellent photos of scenes with a large range of brightness levels, from dimly lit landscapes to a very sunny sky. HDR produces beautiful images, and we've evolved the algorithm that powers it over the past year to use the Pixel 2's application processor efficiently, and enable you to take multiple pictures in sequence by intelligently processing HDR in the background. In parallel, we've also been working on creating hardware capabilities that enable significantly greater computing power--beyond existing hardware--to bring HDR to third-party photography applications. To expand the reach of HDR, handle the most challenging imaging and ML applications, and deliver lower-latency and even more power-efficient HDR processing, we've created Pixel Visual Core.
Color images consist of three layers: a red layer, a green layer, and a blue layer. If you are new to FloydHub, do their 2-min installation, check my 5-min video tutorial or my step-to-step guide - it's the best (and easiest) way to train deep learning models on cloud GPUs. Between the input and output values, we create filters to link them together, a convolutional neural network. We convert RGB colors to the Lab color space.
Fox Firepower: Defense Specialist Allison Barrie shares her top picks of high-tech military vehicles on display at AUSA 2017 including a fuel-cell powered Chevy truck and a self-driving Polaris MRZR. Armored vehicles with laser weapons, silent motorcycles that can run on jet fuel, self-driving ATVs and futuristic Chevy trucks - there were a lot of eye-popping vehicles in the nation's capital this week. Another JLTV featured the Boeing Maneuver Short Range Air Defense (SHORAD) Launcher including a M3P .50 JLTV General Purpose equipped with Rafael Samson RWS Dual Stabilized Remote Weapon Systems with M230 LF, and the Trophy Light Active Protection System. This new General Motors prototype, known as the Chevrolet Colorado ZH2, runs on hydrogen fuel cells.
Recent advancements in machine learning are reaching a level of sophistication that are exceeding the expectations of industry analysts and executives alike. Based on my conversations with business owners and executives worldwide, machine learning is clearing pathways to businesses growth, process optimization, and daily employee empowerment. Extending this further, we are moving to a deeper emphasis on integrated intelligent systems leveraging collaborative workspace tools enabling greater efficiency. As machine learning continues to evolve, businesses will innovate cutting-edge applications and use cases that could drive increased efficiency, intelligence, agility, and customer-centricity.
Smart grids, connected to each other via the cloud, and utilising the IoT, big data analytics and machine learning, can significantly increase the energy efficiency of the existing grid. The result is advanced production optimised for resource consumption and cost including energy, raw materials and water, whilst also enabling connection with customer devices to optimise lifespan performance. Wider 4IR technologies incorporated by the IIoT platform include Virtual Reality product simulators to optimise smart product design, sensor-driven computing, industrial big data analytics, energy efficient robotics, and intelligent machine applications. IoT, sensors, AI and cloud-enabled'precision agriculture' can use on-farm sensors and connected machinery to access real-time data for farmer smart devices that can optimise how much water, energy, fertiliser and feed to use, increasing productivity whilst reducing energy use and product waste.