Multifidelity surrogate modelling combines data of varying accuracy and cost from different sources. It strategically uses low-fidelity models for rapid evaluations, saving computational resources, and high-fidelity models for detailed refinement. It improves decision-making by addressing uncertainties and surpassing the limits of single-fidelity models, which either oversimplify or are computationally intensive. Blending high-fidelity data for detailed responses with frequent low-fidelity data for quick approximations facilitates design optimisation in various domains. Despite progress in interpolation, regression, enhanced sampling, error estimation, variable fidelity, and data fusion techniques, challenges persist in selecting fidelity levels and developing efficient data fusion methods. This study proposes a new fusion approach to construct multi-fidelity surrogate models by constructing gradient-only surrogates that use only gradients to construct regression surfaces. Results are demonstrated on foundational example problems that isolate and illustrate the fusion approach's efficacy, avoiding the need for complex examples that obfuscate the main concept.

As a teenager in Maryland in the 1950s, Mary Allen Wilkes had no plans to become a software pioneer -- she dreamed of being a litigator. One day in junior high in 1950, though, her geography teacher surprised her with a comment: "Mary Allen, when you grow up, you should be a computer programmer!" Wilkes had no idea what a programmer was; she wasn't even sure what a computer was. The first digital computers had been built barely a decade earlier at universities and in government labs. By the time she was graduating from Wellesley College in 1959, she knew her legal ambitions were out of reach. Her mentors all told her the same thing: Don't even bother applying to law school. "They said: 'Don't do it.

For its inaugural re:Mars conference, Amazon invited attendees it calls "dreamers and builders" -- business leaders, scientists and others who are making contributions to the field of artificial intelligence and machine learning. During the Wednesday keynote at the Las Vegas event, Amazon brandished its own AI credentials, making the case to its potential customers and partners that its teams -- across the business -- are pushing forward the stat of the art. Across every step of its e-commerce operations, AI is at work: Amazon shared the way it uses AI to power e-commerce forecasting, and it showcased StyleSnap, an AI-powered feature that lets shoppers in the Amazon app takes a picture of a piece of clothing and find similar items for sale. Amazon also revealed newest fulfillment center robots, called Pegasus and Xanthus, as well as a new drone that Amazon says will start commercial deliveries within months. For in-store shopping, Amazon revealed new details about the technology that drives its Amazon Go stores.

LAS VEGAS (AP) " Amazon said Wednesday that it plans to use self-piloted drones to deliver packages to shoppers' home in the coming months. The online shopping giant did not give exact timing or say where the drones will be making deliveries. Amazon said its new drones use computer vision and machine learning to detect and avoid people or clotheslines in backyards when landing. "From paragliders to power lines to a corgi in the backyard, the brain of the drone has safety covered," said Jeff Wilke, who oversees Amazon's retail business. Wilke said the drones are fully electric, can fly up to 15 miles (24 kilometers), deliver in 30 minutes and carry goods that weigh up to 5 pounds (2.3 kilograms), like a paperback or toothpaste. Amazon has been working on drone delivery for years.

LAS VEGAS - Amazon.com Inc. has new drones that in coming months will deliver packages to customers in 30 minutes or less, a step toward a goal that has eluded the retailer for years. The new drone takes off and lands vertically like a helicopter, is more stable than prior models and can spot moving objects better than humans can, making it safe, Jeff Wilke, the chief executive of the company's consumer business, said at the company's "re:MARS" conference in Las Vegas on Wednesday. Wilke did not say where customers might see the drone in action, but Amazon made its first customer delivery by drone in the United Kingdom in 2016. For years, the world's largest online retailer has promised that packages would be landing on shoppers' doorsteps via these small aircraft, but hype around the service has long outpaced reality. The company has worked to ensure that hard-to-see wires would not trip up its vehicles, for instance, and it has faced tough regulations limiting commercial flights, particularly in the United States.

In a series of "groundbreaking first" the University of Maryland Medical Center in Baltimore successfully delivered and transplanted a donor kidney into a patient using an unmanned drone to transport the donated organ. Within a matter of months, your future Amazon orders could be delivered by a self-driving drone. Officials with the online shopping giant unveiled the latest Prime Air drone design Wednesday at Amazon's re:MARS Conference (Machine Learning, Automation, Robotics and Space) in Las Vegas. "We've been hard at work building fully electric drones that can fly up to 15 miles and deliver packages under five pounds to customers in less than 30 minutes," Jeff Wilke, CEO of Amazon Worldwide Consumer, wrote in a blog post Wednesday. "And, with the help of our world-class fulfillment and delivery network, we expect to scale Prime Air both quickly and efficiently, delivering packages via drone to customers within months."

AMAZON'S SIX-PAGE memos are famous. Executives must write one every year, laying out their business plan. Less well known is that these missives must always answer one question in particular: how are you planning to use machine learning? Responses like "not much" are, according to Amazon managers, discouraged. Machine learning is a form of artificial intelligence (AI) which mines data for patterns that can be used to make predictions.

From time to time, usually on garbage night, the animals wander into Sivasubramanian's backyard to pillage his trash. But try as they might, he and his family had never managed to spot the intruders. "My wife really wanted to see these bears in action," says Sivasubramanian, Amazon's VP of machine learning. "She will always try to stay up looking for bears to visit, and she wants me to give her company." He founded his solution in DeepLens, a new video camera system from Amazon Web Services that lets anyone with programming skills employ deep learning to automate various tasks.

Donald Trump has hammered Amazon.com Inc.'s share price in recent weeks by trying to pick fights online with Chief Executive Officer Jeff Bezos. But the actual object of his Twitter onslaught is the domain of Jeff Wilke, Bezos' right-hand man for most of the past 18 years. Wilke runs Amazon's worldwide consumer division, meaning he's in charge of both selling people stuff and figuring out how to deliver the items as efficiently as possible. Often, the company uses the U.S. Postal Service.

Google's latest bombshell of a research project is a smart contact lens diabetics can use to read blood sugar levels through the tears in their eyes. Thanks to a tiny microchip, Google says, the lens can provide a new glucose reading as often as once a second. Some have pointed out that the project follows in the footsteps of rival Microsoft, but this is merely another reason to applaud Google, a company with a knack for bringing research into the real world that we'll likely never see from Microsoft. The lens Google unveiled on Thursday seems to draw heavily, if not directly, from work done by Microsoft. As TechCrunch notes, one of the Googlers behind the project, Babak Parvis, once collaborated with the Redmond software giant on a lens the company fashioned for tracking blood sugar without needles.