Rolling Horizon Evolutionary Algorithms (RHEA) are a class of online planning methods for real-time game playing; their performance is closely related to the planning horizon and the search time allowed. In this paper, we propose to learn a prior for RHEA in an offline manner by training a value network and a policy network. The value network is used to reduce the planning horizon by providing an estimation of future rewards, and the policy network is used to initialize the population, which helps to narrow down the search scope. The proposed algorithm, named prior-based RHEA (p-RHEA), trains policy and value networks by performing planning and learning iteratively. In the planning stage, the horizon-limited search assisted with the policy network and value network is performed to improve the policies and collect training samples. In the learning stage, the policy network and value network are trained with the collected samples to learn better prior knowledge. Experimental results on OpenAI Gym MuJoCo tasks show that the performance of the proposed p-RHEA is significantly improved compared to that of RHEA.

Taha Kass-Hout, MD, former and first-ever CIO for the FDA and a senior leader of artificial intelligence at Amazon, detailed how the tech giant is using AI for its healthcare services in a recent interview with STAT. A core component of Amazon's healthcare strategy is to support clinicians, according to Dr. Kass-Hout. "I hope we see that with AI we're finally getting to understand what patient has a disease, rather than what disease a patient has -- and truly start personalizing care to that level," Dr. Kass-Hout told STAT. "From a patient perspective and consumer perspective, AI is going to empower them, and for providers and healthcare systems, it's going to augment clinicians and bridge gaps." Dr. Kass-Hout also provided an update on how healthcare companies are using AWS' EHR-mining software Comprehend Medical, which launched in November 2018.

Comparing different algorithms is hard. For almost any pair of algorithms and measure of algorithm performance like running time or solution quality, each algorithm will perform better than the other on some inputs.a For example, the insertion sort algorithm is faster than merge sort on already-sorted arrays but slower on many other inputs. When two algorithms have incomparable performance, how can we deem one of them "better than" the other? Worst-case analysis is a specific modeling choice in the analysis of algorithms, where the overall performance of an algorithm is summarized by its worst performance on any input of a given size. The "better" algorithm is then the one with superior worst-case performance. Merge sort, with its worst-case asymptotic running time of Θ(n log n) for arrays of length n, is better in this sense than insertion sort, which has a worst-case running time of Θ(n2). While crude, worst-case analysis can be tremendously useful, and it is the dominant paradigm for algorithm analysis in theoretical computer science. A good worst-case guarantee is the best-case scenario for an algorithm, certifying its general-purpose utility and absolving its users from understanding which inputs are relevant to their applications. Remarkably, for many fundamental computational problems, there are algorithms with excellent worst-case performance guarantees. The lion's share of an undergraduate algorithms course comprises algorithms that run in linear or near-linear time in the worst case.

Blogging birds is a novel artificial intelligence program that generates creative texts to communicate telemetric data derived from satellite tags fitted to red kites -- a medium-size bird of prey -- as part of a species reintroduction program in the U.K. We address the challenge of communicating telemetric sensor data in real time by enriching it with meteorological and cartographic data, codifying ecological knowledge to allow creative interpretation of the behavior of individual birds in respect to such enriched data, and dynamically generating informative and engaging data-driven blogs aimed at the general public. Geospatial data is ubiquitous in today's world, with vast quantities of telemetric data collected by GPS receivers on, for example, smartphones and automotive black boxes. Adoption of telemetry has been particularly striking in the ecological realm, where the widespread use of satellite tags has greatly advanced our understanding of the natural world.14,23 Despite its increasing popularity, GPS telemetry involves the important shortcoming that both the handling and the interpretation of often large amounts of location data is time consuming and thus done mostly long after the data has been gathered.10,24 This hampers fruitful use of the data in nature conservation where immediate data analysis and interpretation are needed to take action or communicate to a wider audience.25,26 The widespread availability of GPS data, along with associated difficulties interpreting and communicating it in real time, mirrors the scenario seen with other forms of numeric or structured data. It should be noted that the use of computational methods for data analysis per se is hardly new; much of science depends on statistical analysis and associated visualization tools. However, it is generally understood that such tools are mediated by human operators who take responsibility for identifying patterns in data, as well as communicating them accurately.

The EksoVest supports the wearer's arms during lifting. Millions of people Suffer from the effects of spinal cord injuries and strokes that have left them paralyzed. Millions more suffer from back pain, which makes movement painful. Exoskeletons are helping the paralyzed to walk again, enabling soldiers to carry heavy loads, and workers to lift heavy objects with greater ease. An exoskeleton is a mechanical device or soft material worn by a patient/operator, whose structure mirrors the skeletal structure of the operator's limbs (joints, muscles, etc.).

Newly leaked concept images have revealed the first real glimpse into Alphabet's plans for an Orwellian smart city on the Toronto waterfront. Sidewalk Labs, an offshoot of Google's parent company, reached an agreement with the city back in 2017 to develop a futuristic community known as Quayside, complete with robotic waste-sorting systems, sensor-lined pavement, digital infrastructure, and wireless 5G connectivity all throughout. The plans have sparked both concerns and curiosity from the public, fueled further by a lack of information on how it will ultimately come to fruition, aside from a series of simple sketches. The new images published by Sidewalk Labs this month and leaked by Toronto Star now reveal stunning plans for a dozen timber towers and modular pavement in the development, allowing it to evolve to the city's changing needs. Sidewalk Labs also detailed a system of underground tunnels where robots can transport waste and freight out of the public's sight.

Edge computing has a very bright future - it is absolutely inevitable. If you are unconvinced, we invite you to read until the end. For the sake of this post - we will use edge and fog computing interchangeably. This is becoming a less controversial position based on a recent exclusive interview with Chuck Byers, OpenFog CTO. The OpenFog Consortium announced at an IoT Evolution Expo keynote in Fort Lauderdale, Florida last month, that their merger with the Industrial Internet Consortium or IIC was finalized.

Artificial intelligence is taking over more and more jobs. The NYT reports that more and more journalism is actually being written by robots including nearly 1/3 of Bloomberg articles. "robot reporters have been prolific producers of articles on minor league baseball for The Associated Press, high school football for The Washington Post and earthquakes for The Los Angeles Times… Last week, The Guardian's Australia edition published its first machine-assisted article, an account of annual political donations to the country's political parties. And Forbes recently announced that it was testing a tool called Bertie to provide reporters with rough drafts and story templates… The Wall Street Journal and Dow Jones are experimenting with the technology to help with various tasks, including the transcription of interviews… Patch [is] a nationwide news organization devoted to local news, [with] 110 staff reporters and numerous freelancers who cover about 800 communities… In a given week, more than 3,000 posts on Patch -- 5 to 10 percent of its output -- are machine-generated… "One thing I've noticed," Mr. St. John said, "is that our A.I.-written articles have zero typos."

If you've been keeping up with the news lately, reading blogs or the occasional legal industry related tweets on twitter, you've heard the deafening cries and dizzying excitement about artificial intelligence, machine learning and chatbots. You see, it all started with Alan Turing, the original computer scientist on which the movie The Imitation Game was based. Turing developed his test in 1950 for intelligence in a computer. The idea is if a human is unable to distinguish machine from another human being through engaging it in a dialog of questions and and replies, then the computer could be deemed to be "intelligent." Turing predicted by the year 2000, a computer would pass the test on five-minute keyboard conversations 30% of the time.

It's hard not to get the sense that you're getting a glimpse of the future of movies with a ticket to The Wandering Earth, a Chinese space spectacular that earned more than $300 million in its first week when it opened in its native country earlier this month, and has since opened in U.S. theaters and been acquired by Netflix. Set 2,500 years in the future, it's a hopeful but pragmatic sci-fi picture that understands what American blockbusters are still loath to admit: Responding to climate change will pose infrastructural challenges on a massive order and require drastic measures on a planetary scale. Perhaps it takes a country like China, which is accustomed to a manic rate of construction and grandness of organizational possibility, to seriously consider how dramatically humanity will have to reimagine our ways of life to survive such a catastrophic force. But in this film's dystopian future, it's not heat that threatens life but a life-snuffing frost. You can manage your newsletter subscriptions at any time.