Imposing order on the world is seductive, but it flattens out the diversity and rich messiness of human life. Oddly, playing by the rules may help us fight back, argues C. Thi Nguyen in The Score THIS time last year, I wrote an article for New Scientist about the perfect way to cook the classic pasta dish cacio e pepe, according to physicists. The meal's smooth, glossy emulsion of black pepper, pecorino cheese and water is hard to make lump-free. Ivan Di Terlizzi at the Max Planck Institute for the Physics of Complex Systems in Germany and his colleagues cooked cacio e pepe hundreds of times until they produced an exacting and foolproof method. The story proved popular with readers.

Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding the complicated dynamics of underactuated crane systems and the heavy weight of logs to be grasped. This study investigates the feasibility of using reinforcement learning for forestry crane manipulators in grasping and lifting heavy wood logs autonomously. We first build a simulator using Mujoco physics engine to create realistic scenarios, including modeling a forestry crane with 8 degrees of freedom from CAD data and wood logs of different sizes. We further implement a velocity controller for autonomous log grasping with deep reinforcement learning using a curriculum strategy. Utilizing our new simulator, the proposed control strategy exhibits a success rate of 96% when grasping logs of different diameters and under random initial configurations of the forestry crane. In addition, reward functions and reinforcement learning baselines are implemented to provide an open-source benchmark for the community in large-scale manipulation tasks. A video with several demonstrations can be seen at https://www.acin.tuwien.ac.at/en/d18a/

Multi-object grasping is a challenging task. It is important for energy and cost-efficient operation of industrial crane manipulators, such as those used to collect tree logs off the forest floor and onto forest machines. In this work, we used synthetic data from physics simulations to explore how data-driven modeling can be used to infer multi-object grasp poses from images. We showed that convolutional neural networks can be trained specifically for synthesizing multi-object grasps. Using RGB-Depth images and instance segmentation masks as input, a U-Net model outputs grasp maps with corresponding grapple orientation and opening width. Given an observation of a pile of logs, the model can be used to synthesize and rate the possible grasp poses and select the most suitable one, with the possibility to respect changing operational constraints such as lift capacity and reach. When tested on previously unseen data, the proposed model found successful grasp poses with an accuracy of 95%.

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding in a simulated environment. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves various problems of dynamics and path planning, where understanding the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilise a virtual camera to provide an image stream from reconstructed 3D data. We use Cartesian control to simplify domain transfer to real-world applications. Since log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limits on computational resources and time for the challenge of image segmentation and allows for collecting data in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2--5 logs.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' The top Republican on the Senate Energy Committee will warn Thursday against allowing U.S. artificial intelligence capabilities to fall into China's hands when the panel meets for a hearing on the topic. Senators returned to Capitol Hill just days ago after spending the month of August in their home states. AI is expected to be a prominent topic for lawmakers as they race to get ahead of the rapidly advancing technology. It's also the topic at the heart of Thursday's hearing led by Energy Committee Chair Joe Manchin, D-W.Va., and ranking member John Barrasso, R-Wyo., that aims to examine how AI has affected the U.S. energy sector and how the federal government can stay competitive in that lane.

I should like the 1973 film Soylent Green. Actually, I should love it. The movie deals with issues that are even more important now than they were 50 years ago. And as someone who's spent plenty of time exploring the connections between sci-fi films, technology innovation, and the future, I feel a certain professional obligation to sing its praises. Instead, though, I find myself frustrated by the movie and its hyperbolic and naive handling of complex themes.

We've been so worried about whether AI-driven robots will take our jobs that we forgot to ask a much more basic question: will they take our bike lanes? That's the question Austin, Texas, is currently grappling with, and it points to all sorts of unresolved issues related to AI and robots. As revealed in Anaconda's State of Data Science 2021 report, the biggest concern data scientists have with AI today is the possibility, even likelihood, of bias in the algorithms. Leave it to Austin (tagline: "Keep Austin weird") to be the first to have to grapple with robot overlords taking over their bike lanes. If a robot that looks like a "futuristic ice cream truck" in your lane seems innocuous, consider what Jake Boone, vice-chair of Austin's Bicycle Advisory Council, has to say: "What if in two years we have several hundred of these on the road?" If this seems unlikely, consider just how fast electric scooters took over many cities.

Activision Blizzard Inc. said an executive named in a gender-bias lawsuit filed against the company last month by California regulators is leaving the videogame company. J. Allen Brack is immediately stepping down from his role as president of Blizzard Entertainment, the unit behind hit franchises such as World of Warcraft and Overwatch, the company said Tuesday. Two company veterans, Jen Oneal and Mike Ybarra, were named co-leaders of the unit, which it acquired in 2008. "It became clear to J. Allen Brack and Activision Blizzard leadership that Blizzard Entertainment needs a new direction and leadership given the critical work ahead in terms of workplace culture, game development, and innovation," the company said in a statement. Mr. Brack didn't immediately respond to a request for comment.

AI teams not only need to have cutting-edge skillsets to build, test and refine AI models and applications, but they also need to step up as transformational leaders, a new study finds. Artificial intelligence and machine learning have come a long way in recent years, with solid business cases, powerful algorithms, vast compute resources, and rich data sets now the norm for many enterprises. However, AI managers and specialists are still grappling with seemingly insurmountable organizational and ethical issues that are hamstringing their efforts, or even sending things down the wrong path. That's the conclusion of a recent in-depth analysis that looked at the pressures and compromises faced by today's AI teams. The researchers, Bogdana Rakova (Accenture and Partnership on AI), Jingying Yang, (Partnership on AI), Henriette Cramer (Spotify) and Rumman Chowdhury (Accenture), found that most commonly, "practitioners have to grapple with lack of accountability, ill-informed performance trade-offs and misalignment of incentives within decision-making structures that are only reactive to external pressure."

By Sam Nussey2 Min ReadTOKYO (Reuters) – SoftBank's robotics arm said on Monday it will bring a food service robot developed by California-based Bear Robotics to Japan as restaurants grapple with labour shortages and seek to ensure social distancing during the COVID-19 pandemic.Slideshow ( 3 images)The robot named Servi, which has layers of trays and is equipped with 3D cameras and Lidar sensors for navigation, will launch in January, SoftBank Group Corp said.Servi will cost 99,800 yen ($950) per month excluding tax on a three year plan.The launch leverages SoftBank's long experience in bringing overseas technology to Japan but reflects the shift away from CEO Masayoshi Son's earlier focus on humanoid robots.Servi has been tested by Japanese restaurant operators, including Seven & i Holdings at its Denny's chain, as the sector grapples with an aging workforce and deepening labour shortages.SoftBank's humanoid Pepper robot became the face of the company following its 2014 unveiling but failed to find a global customer base.The firm in 2018 announced cleaning robot Whiz, which employs technology from group portfolio company Brain Corp and has sold more than 10,000 units worldwide.SoftBank is touting the use of Whiz as a coronavirus countermeasure, …