Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. It only takes 10 Spotpower (SP) to haul a truck across the Boston Dynamics parking lot ( 1 degree uphill, truck in neutral). These Spot robots are coming off the production line now and will be available for a range of applications soon.

Artificial intelligence systems can – if properly used – help make government more effective and responsive, improving the lives of citizens. Improperly used, however, the dystopian visions of George Orwell's "1984" become more realistic. On their own and urged by a new presidential executive order, governments across the U.S., including state and federal agencies, are exploring ways to use AI technologies. As an AI researcher for more than 40 years, who has been a consultant or participant in many government projects, I believe it's worth noting that sometimes they've done it well – and other times not quite so well. The potential harms and benefits are significant.

Engineers at Missouri University of Science & Technology in Rolla are developing algorithms that could provide early warnings for motorists about flooded roads. The system could warn drivers to stay off flooded roads. Researchers began the yearlong project to use artificial intelligence to enhance flood evacuation plans in February for transportation agencies in the Midwest, including the Missouri Department of Transportation. The work focuses on the Meramec River basin in eastern Missouri and the areas of Nebraska and northwest Missouri that experienced record-breaking floods in late March from the Missouri River. Artificial intelligence could help deliver that information to motorists faster so to prevent people from being stranded on flooded roads, said Suzanna Long, the chair of engineering management and systems engineering at Missouri S&T.

Despite concerns over facial recognition's impact on civil liberties, public agencies have continued to apply the tool liberally across the U.S. with one of the biggest deployments coming to an airport near you. The U.S. Department of Homeland Security (DHS) said that it plans to expand its application of facial recognition to 97 percent of all passengers departing the U.S. by 2023, according to the Verge. By comparison, facial recognition technology is deployed in just 15 airports, according to figures recorded at the end of 2018. In what is being referred to as'biometric exit,' the agency plans to use facial recognition to more thoroughly track passengers entering and leaving the country. The U.S. Department of Homeland Security (DHS) said that it plans to expand its application of facial recognition to 97 percent of all passengers departing the U.S. by 2023 The system functions by taking a picture of passengers before they depart and then cross-referencing the image with a database containing photos of passports and visas.

The UK government's plan to prevent children and teenagers from viewing pornographic content online has a major flaw that means not all porn will be blocked. Critics have called the so-called porn ban "disastrous" for people's privacy, as it will require people to share their personal data online in order to visit porn sites. But the new rules, which come into effect on 15 July, can be skirted by visiting sites that are not subject to the age verification checks. We'll tell you what's true. You can form your own view.

In 1909, a poet named Filippo Marinetti was driving along in his brand new Fiat when he came across two cyclists in the road. Marinetti swerved to avoid hitting his fellow travelers, sending his car into a ditch and completely destroying the vehicle. Here's how Marinetti described the encounter: The words were scarcely out of my mouth when I spun my car around with the frenzy of a dog trying to bite its tail, and there, suddenly, were two cyclists coming toward me, shaking their fists, wobbling like two equally convincing but nevertheless contradictory arguments. Their stupid dilemma was blocking my way--Damn! Ouch! ... I stopped short and to my disgust rolled over into a ditch with my wheels in the air … You can already tell from this account that Marinetti was a bit of an eccentric.

Your reporting on the use of facial recognition in China for "minority identification" is a stark reminder that the battle over the future of artificial intelligence will not simply be about who gathers the top scientists or who is first to innovate. It will also be about who is able to preserve fundamental rights during a period of rapidly changing technology. The White House has already made some progress on this front, highlighting American values, including privacy and civil liberties, in an executive order earlier this year, and backing an important international framework at the Organization for Economic Cooperation and Development. But there is much more to be done. The United States must work with other democratic countries to establish red lines for certain A.I. applications and ensure fairness, accountability and transparency as A.I. systems are deployed.

During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold. In this blog, I'll be exploring how converging exponential technologies (AI, robotics, drones, sensors, networks) are transforming the future of disaster relief--how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives. When it comes to immediate and high-precision emergency response, data is gold. Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet. Aside from democratizing the world's information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geo-tagged data, particularly those most vulnerable to natural disasters.

The White House is deliberately engaging with "like-minded international allies" to assist in the stewardship of artificial intelligence and help the world recognize its full potential, Assistant Director for AI in the Office of Science and Technology Policy Lynne Parker said Thursday. "There are a lot of conversations internationally right now on AI," Parker said at the National Academy of Public Administration's Forum on Artificial Intelligence, held in Washington. "And we are leading many of those conversations." Parker explained that the Organisation for Economic Co-operation and Development, the G7 and G20 international forums and organizations within the United Nations are all addressing the appropriate use of AI at a global level, and America's top federal officials are actively involved in those efforts. For instance, she noted OECD is likely to publish recommendations on governments' use of AI in May.

It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered through a digital health platform, including a mobile app and clinician web dashboard. Anonymized insurance claims data were used from a commercially insured population across several U.S. states and combined with inferred sociodemographic data and data derived from the patient-held mobile application itself. Our approach involves the creation of two models and the comparative analysis of the methodologies and performances therein. We first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) healthcare expenditure for patients (N = 1,242). This enables the classification of impactability if differences in predicted versus actual costs meet a predetermined threshold. A random forest machine learning model was then trained to accurately categorize new patients as impactable versus not impactable, reaching an overall accuracy of 71.9%. We then modify these parameters through grid search to define the parameters that deliver optimal performance. A roadmap is proposed to iteratively improve the performance of the model. As the number of newly onboarded patients and length of use continues to increase, the accuracy of predicting impactability will improve commensurately as more advanced machine learning techniques such as deep learning become relevant. This approach is generalizable to analyzing the impactability of any intervention and is a key component of realising closed loop feedback systems for continuous improvement in healthcare.