All kinds of animals, from fish to crustaceans, hang out in the depths during the day, where the darkness provides protection from predators. At night, they migrate up to the shallows to forage. Then they swim back down again when the sun rises--a great big conveyor belt of biomass. Today in the journal Science Robotics, a team of engineers and oceanographers describes how they got a new autonomous underwater vehicle to lock onto movements of organisms and follow them around the ocean's "twilight zone," a chronically understudied band between 650 feet and 3,200 feet deep, which scientists also refer to as mid-water. Thanks to some clever engineering, the researchers did so without flustering these highly sensitive animals, making Mesobot a groundbreaking new tool for oceanographers.

Jack Morrison and Isaac Roberts (far left and right) previously cofounded and sold 3D scanning company Replica Labs to Occipital. There they met electrical engineer Davis Foster (center), with whom they went on to cofound Scythe Robotics. Self-driving cars get all the hype. But while the category continues to face a long and uncertain path to commercialization, a burgeoning crop of autonomous vehicles is already hitting the market. The latest is Scythe Robotics, a Boulder, Colorado-based company that announced today it is launching a zero-emission, autonomous lawn mower backed by $18.6 million from Inspired Capital, True Ventures and more.

Before I go any further it's probably worth establishing what a Deepfake is and isn't. A technique by which a digital image or video can be superimposed onto another, which maintains the appearance of an unedited image or video. The term is often misinterpreted, and that's potentially as a result of definitions like this. The concept of manipulating images and video in this way is certainly not a new concept. Visual effects artists working on Hollywood films back in the '90s would probably describe parts of their job as something very similar to this.

From 24-27 June, the 3d Soccer Simulation League will be taking place, as part of RoboCup 2021. The league first started in 2004 and teams compete in simulated soccer matches, with an emphasis on the low-level control of humanoid robots. Executive committee member Marco Simões told us about the league, how the competition will work, and how they strive to advance research every year. The 3d Soccer Simulation League is part of the RoboCup Soccer Simulation League, which is a larger league that includes two sub-leagues: the 2d Simulation League and the 3d Simulation League. The 2d Simulation League is about high-level research, AI and the strategies of soccer.

Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. On the other hand, in recent years, increasing research efforts in machine learning and data mining have been dedicated to dealing with data involving interactions from two or more factors. However, few researchers have addressed the heterogeneous relationship in data brought by modulation of multiple factors using techniques of DDP. In this paper, we propose a novel technique, MultiLinear Dirichlet Processes (MLDP), to constructing DDPs by combining DP with a state-of-the-art factor analysis technique, multilinear factor analyzers (MLFA). We have evaluated MLDP on real-word data sets for different applications and have achieved state-of-the-art performance. Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. To introduce dependency into DDP, various techniques have been developed via correlating through components of atomic measures, such as atom sizes [8], [11], [23] and atom locations [6], [10], [28], sampling from a DP with random distributions as atoms [24], operating on underlying compound Poisson processes [18], regulating by Lévy Copulas [16], or constructing those measures through a mixture of several independent measures drawn from DPs [12], [15], [19], [20].

This paper presents a new approach to prevent transportation accidents and monitor driver's behavior using a healthcare AI system that incorporates fairness and ethics. Dangerous medical cases and unusual behavior of the driver are detected. Fairness algorithm is approached in order to improve decision-making and address ethical issues such as privacy issues, and to consider challenges that appear in the wild within AI in healthcare and driving. A healthcare professional will be alerted about any unusual activity, and the driver's location when necessary, is provided in order to enable the healthcare professional to immediately help to the unstable driver. Therefore, using the healthcare AI system allows for accidents to be predicted and thus prevented and lives may be saved based on the built-in AI system inside the vehicle which interacts with the ER system.

We consider the problem of estimating a $d$-dimensional $s$-sparse discrete distribution from its samples observed under a $b$-bit communication constraint. The best-known previous result on $\ell_2$ estimation error for this problem is $O\left( \frac{s\log\left( {d}/{s}\right)}{n2^b}\right)$. Surprisingly, we show that when sample size $n$ exceeds a minimum threshold $n^*(s, d, b)$, we can achieve an $\ell_2$ estimation error of $O\left( \frac{s}{n2^b}\right)$. This implies that when $n>n^*(s, d, b)$ the convergence rate does not depend on the ambient dimension $d$ and is the same as knowing the support of the distribution beforehand. We next ask the question: ``what is the minimum $n^*(s, d, b)$ that allows dimension-free convergence?''. To upper bound $n^*(s, d, b)$, we develop novel localization schemes to accurately and efficiently localize the unknown support. For the non-interactive setting, we show that $n^*(s, d, b) = O\left( \min \left( {d^2\log^2 d}/{2^b}, {s^4\log^2 d}/{2^b}\right) \right)$. Moreover, we connect the problem with non-adaptive group testing and obtain a polynomial-time estimation scheme when $n = \tilde{\Omega}\left({s^4\log^4 d}/{2^b}\right)$. This group testing based scheme is adaptive to the sparsity parameter $s$, and hence can be applied without knowing it. For the interactive setting, we propose a novel tree-based estimation scheme and show that the minimum sample-size needed to achieve dimension-free convergence can be further reduced to $n^*(s, d, b) = \tilde{O}\left( {s^2\log^2 d}/{2^b} \right)$.

Let $\Phi$ be a uniformly random $k$-SAT formula with $n$ variables and $m$ clauses. We study the algorithmic task of finding a satisfying assignment of $\Phi$. It is known that a satisfying assignment exists with high probability at clause density $m/n < 2^k \log 2 - \frac{1}{2} (\log 2 + 1) + o_k(1)$, while the best polynomial-time algorithm known, the Fix algorithm of Coja-Oghlan, finds a satisfying assignment at the much lower clause density $(1 - o_k(1)) 2^k \log k / k$. This prompts the question: is it possible to efficiently find a satisfying assignment at higher clause densities? To understand the algorithmic threshold of random $k$-SAT, we study low degree polynomial algorithms, which are a powerful class of algorithms including Fix, Survey Propagation guided decimation (with bounded or mildly growing number of message passing rounds), and paradigms such as message passing and local graph algorithms. We show that low degree polynomial algorithms can find a satisfying assignment at clause density $(1 - o_k(1)) 2^k \log k / k$, matching Fix, and not at clause density $(1 + o_k(1)) \kappa^* 2^k \log k / k$, where $\kappa^* \approx 4.911$. This shows the first sharp (up to constant factor) computational phase transition of random $k$-SAT for a class of algorithms. Our proof establishes and leverages a new many-way overlap gap property tailored to random $k$-SAT.

Killer whales – also known as orcas – have complex social structures including close'friendships', a new study reveals. Scientists at the University of Exeter used drones to film the animals – one of the world's most powerful predators – in the Pacific Ocean. The team found killer whales (Orcinus orca) spend more time interacting with certain individuals in their pod, and tend to favour those of the same sex and similar age. Results from the new study are based on 651 minutes of video filmed over 10 days. Orcas are the largest member of the dolphin family.

The Association for the Advancement of Artificial Intelligence's 2021 Spring Symposium Series was held virtually from March 22-24, 2021. There were ten symposia in the program: Applied AI in Healthcare: Safety, Community, and the Environment, Artificial Intelligence for K-12 Education, Artificial Intelligence for Synthetic Biology, Challenges and Opportunities for Multi-Agent Reinforcement Learning, Combining Machine Learning and Knowledge Engineering, Combining Machine Learning with Physical Sciences, Implementing AI Ethics, Leveraging Systems Engineering to Realize Synergistic AI/Machine-Learning Capabilities, Machine Learning for Mobile Robot Navigation in the Wild, and Survival Prediction: Algorithms, Challenges and Applications. This report contains summaries of all the symposia. The two-day international virtual symposium included invited speakers, presenters of research papers, and breakout discussions from attendees around the world. Registrants were from different countries/cities including the US, Canada, Melbourne, Paris, Berlin, Lisbon, Beijing, Central America, Amsterdam, and Switzerland. We had active discussions about solving health-related, real-world issues in various emerging, ongoing, and underrepresented areas using innovative technologies including Artificial Intelligence and Robotics. We primarily focused on AI-assisted and robot-assisted healthcare, with specific focus on areas of improving safety, the community, and the environment through the latest technological advances in our respective fields. The day was kicked off by Raj Puri, Physician and Director of Strategic Health Initiatives & Innovation at Stanford University spoke about a novel, automated sentinel surveillance system his team built mitigating COVID and its integration into their public-facing dashboard of clinical data and metrics. Selected paper presentations during both days were wide ranging including talks from Oliver Bendel, a Professor from Switzerland and his Swiss colleague, Alina Gasser discussing co-robots in care and support, providing the latest information on technologies relating to human-robot interaction and communication. Yizheng Zhao, Associate Professor at Nanjing University and her colleagues from China discussed views of ontologies with applications to logical difference computation in the healthcare sector. Pooria Ghadiri from McGill University, Montreal, Canada discussed his research relating to AI enhancements in health-care delivery for adolescents with mental health problems in the primary care setting.