LOS ANGELES – It was an otherwise routine flight until, at an altitude of about 1,100 feet east of this city's downtown, the crew aboard the news chopper heard a loud bang. "The pilot and I just looked at each other. 'What was that?'" reporter Chris Cristi of KABC-TV remembers thinking. Not far from their base, they landed Air 7 HD, as their Eurocopter is known to viewers, and discovered a dent in the horizontal stabilizer and next to it, a gash and one-inch hole. There was no blood or feathers as if they had hit a bird.

The state of Utah has given an artificial intelligence company real-time access to state traffic cameras, CCTV and "public safety" cameras, 911 emergency systems, location data for state-owned vehicles, and other sensitive data. The company, called Banjo, says that it's combining this data with information collected from social media, satellites, and other apps, and claims its algorithms "detect anomalies" in the real world. The lofty goal of Banjo's system is to alert law enforcement of crimes as they happen. It claims it does this while somehow stripping all personal data from the system, allowing it to help cops without putting anyone's privacy at risk. As with other algorithmic crime systems, there is little public oversight or information about how, exactly, the system determines what is worth alerting cops to. In its pitches to prospective clients, Banjo promises its technology, called "Live Time Intelligence," can identify, and potentially help police solve, an incredible variety of crimes in real-time. Banjo says its AI can help police solve child kidnapping cases "in seconds," identify active shooter situations as they happen, or potentially send an alert when there's a traffic accident, airbag deployment, fire, or a car is driving the wrong way down the road. Banjo says it has "a solution for homelessness" and can help with the opioid epidemic by detecting "opioid events." It offers "artificial intelligence processing" of state-owned audio sensors that "include but may not be limited to speech recognition and natural language processing" as well as automatic scene detection, object recognition, and vehicle detection on real-time video footage pulled in from Utah's cameras.

You can't miss The Commodore. Dressed in black, from his hat to his shoes, he stands out in any crowd. But he is no fashion celebrity. His real name is Tokologo Phetla, and he is a rare breed in South Africa: an entrepreneur in the field of artificial intelligence (AI). He has developed an artificial intelligence software system, which he named Christopher, in honour of the machine developed by legendary computer scientist Alan Turing during the Second World War to crack the German encryption machine, Enigma.

Love is evidence that we are recognised as individuals, as significant. Love is what we are asked to set aside in the name of progress under a revolutionary regime. Love, too, is imperilled by automation, given how it minimises human contact. Two great loves sit at the heart of Cao Fei's feature-length film Nova from last year: a romance between two computer scientists – one Russian, one Chinese – and the relationship between the latter and his son. Both loves fall foul of Sino-Soviet progress.

NASA's Curiosity rover has shared a stunning panorama of its home. Composed of more than 1,000 images of Mars' landscape taken during the 2019 Thanksgiving holiday, the contains 1.8 billion pixels – deeming it the highest-resolution picture of the Martian planet yet. The rover used its Mast Camera to capture the photos of the Red Planet to produce the high-resolution panorama and relied on its medium-angle lens to for a lower-resolution -nearly 650-million-pixel panorama that includes the rover's deck and robotic arm. NASA's Curiosity rover has shared a stunning panorama of its home. Composed of more than 1,000 images of Mars' landscape taken during the 2019 Thanksgiving holiday, the contains 1.8 billion pixels – deeming it the highest-resolution picture of the Martian planet yet Both panoramas showcase'Glen Torridon,' a region on the side of Mount Sharp that Curiosity is exploring. The images were snapped between November 24 and December 1, but before NASA staff left for the holiday, the programmed the rover with certain tasks such as what angles of the planet to capture and to ensure the pictures were in focus.

Caption Health, a leading medical AI company, announced that its flagship product, Caption AI, the first AI-guided medical imaging acquisition system, is now available for pre-order by healthcare providers. Caption AI is a transformational new technology that enables healthcare practitioners--even those without prior ultrasound experience--with the ability to perform ultrasound exams quickly and accurately, by providing expert guidance, automated quality assessment, and intelligent interpretation capabilities. Caption AI comes equipped with Caption Guidance software, which uses artificial intelligence to provide real-time guidance and feedback on image quality to enable capture of diagnostic quality images. This announcement follows the recent groundbreaking marketing authorization of Caption Guidance software by the U.S. Food and Drug Administration (FDA). The safety and effectiveness of Caption Guidance was clinically validated in a multi-center prospective pivotal trial at Northwestern Medicine and Minneapolis Heart Institute at Allina Health with registered nurses with no prior ultrasound experience.

The Defense Department has adopted a series of ethical principles intended to guide the development and use of artificial intelligence on and off the battlefield, including taking "deliberate steps to minimize unintended bias" and ensuring the ability to "deactivate" systems that aren't behaving as expected. The principles are based on the recommendations of the Defense Innovation Board, which spent 15 months consulting with AI experts in industry, government, and academia, according to a Feb. 24 DOD release. The Joint Artificial Intelligence Center will coordinate the implementation of these principles across the department. "The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order," Defense Secretary Mark Esper said in a release. "AI technology will change much about the battlefield of the future, but nothing will change America's steadfast commitment to responsible and lawful behavior."

This paper considers safe robot mission planning in uncertain dynamical environments. This problem arises in applications such as surveillance, emergency rescue, and autonomous driving. It is a challenging problem due to modeling and integrating dynamical uncertainties into a safe planning framework, and finding a solution in a computationally tractable way. In this work, we first develop a probabilistic model for dynamical uncertainties. Then, we provide a framework to generate a path that maximizes safety for complex missions by incorporating the uncertainty model. We also devise a Monte Carlo method to obtain a safe path efficiently. Finally, we evaluate the performance of our approach and compare it to potential alternatives in several case studies.

Probability models have been proposed in the literature to account for "intelligent" behavior in many contexts. In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles. The backward flow provides precious background information to the agent's behavior, viz., inferences coming from the future determine the agent's actions. Probability tensors are layered in time in both directions in a manner similar to convolutional neural networks. The discussion is carried out with reference to a set of simulated grids where, despite the apparent task complexity, a solution, if feasible, is always found. The original model proposed by Attias has been extended to include non-absorbing obstacles, multiple goals and multiple agents. The emerging behaviors are very realistic and demonstrate great potentials of the application of this framework to real environments.

Importance: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: Investigate prediction of county-level mortality rates in the U.S. using satellite images. Design: Satellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Main Outcomes and Measures: County mortality was predicted using satellite images. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Conclusion and Relevance: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Tools that are able to identify image features associated with health-related outcomes can inform targeted public health interventions.