If you're also single and looking for love, then Maple Match – a new matchmaking website that promises to "Make dating great again" – may be the catch-all solution you've been waiting for. According to its website, Maple Match aims to make it "easy for Americans to find the ideal Canadian partner to save them from the unfathomable horror of a Trump presidency." "After more than 35,000 hits and more than 4,500 signups in just four days, we are confident that Maple Match will fulfill a clear need in the dating space," site founder and CEO Joe Goldman told Tech Times. He added that the site aims to be operational "as soon as possible." Even Canadians – who are known for being unflinchingly polite – have been vocal about their dislike for Donald Trump, though perhaps that dislike is more out of concern than anything else.

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process. They developed a framework that uses uncertainties to iteratively guide the next experiments to be performed in search of a shape-memory alloy with very low thermal hysteresis (or dissipation).

I frequently ask young people, particularly undergraduates, what they plan to do with their future. The responses are typically vague and void of direction. Most responses involve waiting for someone else to provide the guidance. You do not have to wait. You can get started today.

Adjusted R² measures the goodness of fit of a regression model. Higher the R², better is the model.

Overall it was an inspiring week with many deep engaging conversations. Visitors to the Microsoft area were excited to see customer success stories from us and our partners and learned about the details of the Azure services that were used to build them. Hannover Messe this year featured many smart technologies and digital transformation breakthroughs tailored to the world of manufacturing and we look forward to being back next year with even more exciting updates to share.

The news and information ecosystem is in the midst of change -- again. Mobile-first consumption is on the rise, smart homes are becoming mainstream and connected cars will soon take over the roads of major cities around the world. Smart devices will require "smart content." It's only a matter of time before artificial intelligence becomes the backbone of the media industry of the future. Today, most people find information via search or social.

This story was delivered to BI Intelligence "E-Commerce Briefing" subscribers. To learn more and subscribe, please click here. The online marketplace has acquired AI company Expertmaker to enhance the way products display on eBay's pages, according to Internet Retailer. This purchase is part of eBay's structured data push for sellers, which utilizes eBay's standard way of categorizing and displaying products for sale on its marketplace. The use of structured data is not mandatory, but eBay has been strongly encouraging sellers to adopt it. As of the first quarter of 2016, 60% of listed items on eBay used the structured data rules, according to the company's earnings report.

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Celaton's inSTREAM software streamlines the handling of unstructured unpredictable (and structured) content such as correspondence, claims, complaints and invoices that organizations receive by email, social media, fax and paper. This minimizes the need for human intervention and ensures that only accurate, relevant and structured data enters business systems. Unique to inSTREAM is its ability to learn through the natural consequence of processing information and collaborating with people. Capgemini's extensive knowledge and experience in business process services will also enable Celaton to accelerate and improve inSTREAM's capabilities. The cooperation will enable Capgemini to increase efficiency, shorten turnaround times and enhance quality in areas where incoming documents and queries need to be processed, improving overall customer satisfaction.

When that quadrotor fencing video showed up everywhere last month, we asked Ross Allen, the Stanford PhD candidate (and fencer) responsible for the research, if he'd be willing to talk to us about it. He said sure, except his thesis defense was that Friday, so would we mind waiting a bit? It's been a bit, and after a successful defense, Dr. Allen is somehow not sick and tired of robots and answered a bunch of our questions about quadrotors, swords, and why mixing them is such a great idea. Here's the video that you (and a couple hundred thousand other people) probably saw a few weeks ago: The swordplay is cool, but even cooler is the fact that this is the first demonstration of truly "real-time kinodynamic planning" on a quadrotor system navigating an obstructed environment. Or at least, that's what this recent paper from Allen (along with his colleague Marco Pavone) at Stanford's Autonomous Systems Laboratory says.