Delivery robots suffered a setback this week when New York City made it clear they're not welcome there. On Monday, November 25, just a few days after a FedEx "SameDay Bot" autonomous robot was spotted trundling along a Manhattan street, lawyers for the New York City Department of Transportation sent a strongly worded cease-and-desist letter to the shipping giant, CNN reported. The letter warned FedEx that its last-mile delivery robot breached multiple traffic rules, adding that any further outings made by the machine could result in serious consequences for the firm. "You are hereby directed to immediately cease and desist operating your SameDay Bots on the streets and sidewalks in the City of New York," lawyers said in the letter. "Failure to do so may result in the seizure of the property, notices of violation and/or the commencement of legal action."

Fox News Flash top headlines for Nov. 27 are here. Check out what's clicking on Foxnews.com A Georgia man is accused of scamming a Virginia woman out of more than $6.5 million after wooing her into a romantic relationship through an online dating site. Nnamdi Marcellus MgBodile, 35, from Marietta, Georgia, was charged with 20 counts of bank fraud, money laundering and conspiracy to commit bank fraud, according to a Justice Department press release on Wednesday. MgBodile and others also allegedly tried to scam a company out of $350,000 using an email scheme, according to the DOJ.

The U.S. Department of Energy's Argonne National Laboratory is working to use machine learning and artificial intelligence to build a better battery. Should the DOE's efforts prove fruitful, it could be a positive development for the medical device industry, where batteries have proven to be a technological stumbling block when it comes to device miniaturization. Argonne researchers created a database of approximately 133,000 small organic molecules that could form the basis of battery electrolytes with a computationally intensive model called G4MP2, which represents 166 billion larger molecules that scientists wanted to probe for electrolyte candidates, according to a news release. The researchers applied a machine-learning algorithm to relate the known structures from the small data set to more coarsely modeled structures from the larger set, using a less computationally taxing modeling framework based on density functional theory. It is less accurate than G4MP2, but density functional theory provides a good approximation, according to the DOE.

Trillium is looking for an outstanding team member to lead and manage NASA FDL operations in the US. Your role will be to manage a multi-stakeholder program, work with experts in Artificial Intelligence and the space sciences and produce world-class results for the benefit of humankind. The right person for this role will be proactive and comfortable working with multiple stakeholders - including NASA, Google Cloud, Intel, Nvidia, IBM, Lockheed Martin and other leaders in AI as well as our deployment partners that use the results of our projects. This role includes working closely with our delivery partner, the SETI Institute, based in Mountain View, California and NASA Ames. If you're a born problem solver, adaptable, and great with people, please send us an application.

The next time you get sick, your care may involve a form of the technology people use to navigate road trips or pick the right vacuum cleaner online. Artificial intelligence is spreading into health care, often as software or a computer program capable of learning from large amounts of data and making predictions to guide care or help patients. It already detects an eye disease tied to diabetes and does other behind-the-scenes work like helping doctors interpret MRI scans and other imaging tests for some forms of cancer. Now, parts of the health system are starting to use it directly with patients. During some clinic and telemedicine appointments, AI-powered software asks patients initial questions about their symptoms that physicians or nurses normally pose.

Thales launched InnovDays in 2012 as a way for its customers and partners to gain exclusive insights into the technological excellence of its research facilities. Since then, successive InnovDays events have turned the spotlight on the latest technological innovations developed by more than 29,500 Thales engineers and researchers. This press release features multimedia. InnovDays is a reflection of Thales's objective of leveraging science and high technology to build a future we can all trust. By harnessing the unprecedented precision and power of quantum physics and augmenting the performance of its systems thanks to artificial intelligence, Thales is developing the new generation of technologies that will shape the world of tomorrow.

Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together--or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. Instead, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.

With software eating the world, it's time for the United States to create a cabinet-level position focused on digital. Whether it was the flawed rollout of the ACA website, the identified need for cybersecurity protection of our energy grid, or AI on the battlefield, the executive branch needs digital leadership and expertise to help guide the country. Here are 10 issues that require coordination, oversight, and/or financial support at the national level with links to relevant content. How do you ensure that machine learning algorithms aren't biased – by race, by age, or by gender? When can you trust an AI black box algorithm and when isn't it suitable?

But that's not what happened. Flash forward 18 months and the end of the process couldn't be more dissimilar from its start. The nervous energy had been replaced with exhaustion. Our optimism that we'd be able to provide an outline for the ways that the New York City government should be using automated decision systems gave way to a fatalistic belief that we may not be able to tackle a problem this big after all. The people in this room were going to decide what role AI should play and what safeguards we should have."

Pepper has become the de facto robot of the decade. It's 2019 and we still don't have adorable robot butlers in our homes to deliver ice cream while we lounge on the sofa or tidy up our floor-drobe after an especially busy week. And yet, as the decade draws to a close, we're also living in the most exciting era for robotics we've ever seen. Not only are the robots we're building more advanced than ever, but also we're having discussions about the roles robots should play in our lives, whether they should have rights and what our relationship with them should look like. The 2010s have given us robots that can care for us, robots that can wow us and robots that give us the willies.