There comes a time in every tech journalist's year when the Muggles venture out, track us down, and beseech us for holiday gift recommendations. At the top of the list is the quintessential request: "I'd like to get them a powerful computer they can use at school to edit video, plot the orbit of the moon, and play those fancy games. What can I get for under $50?" Here's a great lineup of gift ideas and resources to get you started. It's at this point when every intrepid tech journalist takes on a pained and guilty expression and channels the internal Ebenezer. For, of course, as the messengers, it is our fault that such wonderments aren't available for the mere price of nearly free. Though we break it to our friends as gently as possible, we know that somewhere deep down, they blame us for ruining Christmas. In this 2020, oh glorious year of what the hell happened to us, I bring you wonderments, both inexpensive and unusual.

The robotic Chang'e 5 mission will land on the moon, gather rock samples and then blast off back to Earth - the first such mission in over 40 years. It could be a rehearsal for landing humans on the moon

More than a dozen companies have long been approved to test out self-driving cars in California. Now, they can also charge passengers if they launch a robotaxi service. On Thursday, November 19, the California Public Utilities Commission (CPUC) approved both the ability to launch robotaxi services and charge for them after many months of these companies -- such as Cruise, Waymo, Aurora Innovation, Pony.ai, and Zoox -- lobbying for such policies. Of course, the companies still have to jump through various stacks of paperwork in order to be granted such approvals, but all in time. Waymo has been operating such a service in Arizona, Waymo One, for more than a year.

Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials. When we think about the materials that will enable next-generation technologies, it's probably not the case that there is one ultimate material waiting to be found that will solve all our problems. The problems we need to solve (producing and storing clean energy, mitigating climate change, desalinating water, etc.) are complex and varied. Even zooming in to the next-generation of electronics, computers, and nanotechnology, there probably isn't a single perfect material to exploit in the same way that silicon has been used in all our familiar devices.

Computer simulations are a critical part of the product design optimization process, allowing engineers to test various configurations and select the best design among the many different alternatives. But even at a facility like the U.S. Department of Energy's (DOE) Argonne National Laboratory, with its state-of-the-art resources, simulations can be very expensive and take a long time to run. With the goal of accelerating this design process, a research team in Argonne's Energy Systems (ES) division, comprised of postdoctoral appointee Opeoluwa Owoyele and research scientist Pinaki Pal, recently developed a new design optimization tool called ActivO. The new tool can drastically reduce the time needed to find the best design. It employs a novel machine learning technique that helps users focus on how to most efficiently target computational resources.

To help its service technicians more efficiently repair and maintain its models, Mercedes-Benz USA is outfitting all of its authorized American dealerships with HoloLens 2 headsets. The devices are equipped with Microsoft Dynamics 365 Remote Assist, a mixed reality app that that lets users collaborate during hands-free video calls from their own computers. Organizations have long known the importance of business resiliency, but becoming resilient requires time and preparation, and the pandemic has forced many organizations to evolve at a pace few could have imagined. To recover and thrive within this new context presents new challenges. That is why we are partnering with customers to support faster adoption of digital capabilities.

Puzzle #86: How many yams do three shipwrecked sailors end up with? What will it take to get a covid-19 vaccine to the world? US Navy's huge uncrewed robot ship has journeyed through Panama Canal

Creating nanomaterials with flame spray pyrolysis is complex, but scientists at Argonne have discovered how applying artificial intelligence can lead to an easier process and better performance. During a tour of the Manufacturing and Engineering Research Facility at the U.S. Department of Energy's Argonne National Laboratory, Marius Stan, the Intelligent Materials Design lead in Argonne's Applied Materials Division (AMD), encountered a new experimental setup. As he watched the machine in the experiment, which relies on flame to produce nanomaterials, he had a thought: Could artificial intelligence be used to optimize this complex process? When asked to explain the process, Stan put it simply: "It's where scientists put chemicals in a flame and wait for a miracle--for particles to appear at the end of the process, particles that have important properties for a variety of applications." Flame spray pyrolysis is a technology that enables the manufacturing of nanomaterials in high volumes, which in turn is critical to producing a wide range of industrial materials, like chemical catalysts, battery electrolytes/cathodes and pigments.

We are accelerating fast into an Artificial Intelligence (AI) driven digital era. Not a moment goes when digital is not part of our daily lives. And that's not just about smart devices at home or collaborating on MS Teams or Zoom meetings but extends to cars we drive, payments we make or shopping we do. While so much of our lives are surrounded and enhanced by digital experiences, when it comes to the most crucial resource that helps companies achieve goals and scale to new heights, that is human resources, AI is a tiny component. It will be a pity if we can't extend and use the very tools that make our lives so much better when it comes to talent or human resources management.

The nominal price of steel, for example, has increased by 167% since the turn of the century while energy costs have climbed more than 2.5 times their prices in 2000 (1). In order to be more efficient while managing tight margins, manufacturers have begun exploring digital transformation solutions such as Artificial Intelligence (AI) and Machine Learning (ML). These technologies help factories unlock previously hidden opportunities while helping solve problems faster than ever before. Getting started with these initiatives, however, remains a challenge. In this eBook, we will explore the concept of Prescriptive AI: a data-driven process that helps manufacturers discover new ways to reduce costs and increase productivity.