The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together. However, optimal SP policies commonly contain arbitrary conventions ("handshakes") and are not compatible with other, independently trained agents or humans. This latter desiderata was recently formalized by Hu et al. 2020 as the zero-shot coordination (ZSC) setting and partially addressed with their Other-Play (OP) algorithm, which showed improved ZSC and human-AI performance in the card game Hanabi. OP assumes access to the symmetries of the environment and prevents agents from breaking these in a mutually incompatible way during training. However, as the authors point out, discovering symmetries for a given environment is a computationally hard problem. Instead, we show that through a simple adaption of k-level reasoning (KLR) Costa Gomes et al. 2006, synchronously training all levels, we can obtain competitive ZSC and ad-hoc teamplay performance in Hanabi, including when paired with a human-like proxy bot. We also introduce a new method, synchronous-k-level reasoning with a best response (SyKLRBR), which further improves performance on our synchronous KLR by co-training a best response.

Demystifying the delay propagation mechanisms among multiple airports is fundamental to precise and interpretable delay prediction, which is crucial during decision-making for all aviation industry stakeholders. The principal challenge lies in effectively leveraging the spatiotemporal dependencies and exogenous factors related to the delay propagation. However, previous works only consider limited spatiotemporal patterns with few factors. To promote more comprehensive propagation modeling for delay prediction, we propose SpatioTemporal Propagation Network (STPN), a space-time separable graph convolutional network, which is novel in spatiotemporal dependency capturing. From the aspect of spatial relation modeling, we propose a multi-graph convolution model considering both geographic proximity and airline schedule. From the aspect of temporal dependency capturing, we propose a multi-head self-attentional mechanism that can be learned end-to-end and explicitly reason multiple kinds of temporal dependency of delay time series. We show that the joint spatial and temporal learning models yield a sum of the Kronecker product, which factors the spatiotemporal dependence into the sum of several spatial and temporal adjacency matrices. By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation. Furthermore, a squeeze and excitation module is added to each layer of STPN to boost meaningful spatiotemporal features. To this end, we apply STPN to multi-step ahead arrival and departure delay prediction in large-scale airport networks. To validate the effectiveness of our model, we experiment with two real-world delay datasets, including U.S and China flight delays; and we show that STPN outperforms state-of-the-art methods. In addition, counterfactuals produced by STPN show that it learns explainable delay propagation patterns.

Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a need to annotate very long recordings but the expert annotator time is limited. This paper presents an overview of the second edition of the few-shot bioacoustic sound event detection task included in the DCASE 2022 challenge. A detailed description of the task objectives, dataset, and baselines is presented, together with the main results obtained and characteristics of the submitted systems. This task received submissions from 15 different teams from which 13 scored higher than the baselines. The highest F-score was of 60% on the evaluation set, which leads to a huge improvement over last year's edition. Highly-performing methods made use of prototypical networks, transductive learning, and addressed the variable length of events from all target classes. Furthermore, by analysing results on each of the subsets we can identify the main difficulties that the systems face, and conclude that few-show bioacoustic sound event detection remains an open challenge.

New research that harnessed the power of artificial intelligence has identified the specific crater on Mars that ejected the ancient Black Beauty meteorite. The researchers named the Mars crater after the Australian city of Karratha, which is home to one of the oldest terrestrial rocks. The discovery offers never-known details about the Martian meteorite NWA 7034, nicknamed'Black Beauty,' which was found in Africa in 2011, according to researchers. 'For the first time, we know the geological context of the only brecciated Martian sample available on Earth,' says Dr. Anthony Lagain. 'For the first time, we know the geological context of the only brecciated Martian sample available on Earth, 10 years before the NASA's Mars Sample Return mission is set to send back samples collected by the Perseverance rover currently exploring the Jezero crater,' lead author Dr. Anthony Lagain, from Curtin University's Space Science and Technology Center in the School of Earth and Planetary Sciences, says in a statement.

Meta has announced that it has built and open-sourced'No Language Left Behind' NLLB-200, a single Artificial Intelligence (AI) model that is the first to translate across 200 different languages, including 55 African languages with state-of-the-art results. Meta is using the modelling techniques and learnings from the project to improve and extend translations on Facebook, Instagram, and Wikipedia. In an effort to develop high-quality machine translation capabilities for most of the world's low-resource languages, this single AI model was designed with a focus on African languages. They are challenging from a machine translation perspective. AI models require lots and lots of data to help them learn, and there's not a lot of human-translated training data for these languages.

The graph represents a network of 758 Twitter users whose tweets in the requested range contained "neo4j", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 12 July 2022 at 05:30 UTC. The requested start date was Tuesday, 12 July 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 22-hour, 7-minute period from Tuesday, 28 June 2022 at 00:51 UTC to Monday, 11 July 2022 at 22:59 UTC.

Digital technology will play a significant role in achieving sustainable human development worldwide. In 2015, United Nations Member States set 17 goals, The Sustainable Development Goals (SDGs), to provide a road map for the achievement of Earth’s peace and human prosperity by 2030. SDG 3 as one of the goals which is aimed at ensuring healthy lives and promoting well-being for all at all ages, will greatly benefit from the implementation of digital technology. With over a billion people, Africa can be better positioned to surmount its health challenges -especially regarding maternal and child health, infectious and non-communicable disease- using digital technology including artificial intelligence.Artificial intelligence (AI) is defined as the automation of activities associated with human thinking such as decision-making, problem-solving and learning.1AI was first used in medicine in the 1970s when medical expert systems – based on Bayesian statistics and decision theory – diagnosed and recommended treatments for glaucoma and infectious disease.2 Progress in Bayesian networks, artificial neural networks, and hybrid intelligent systems in the late 1990s has scaled up bioinformatics research; thereby expanding uptake of Medical Artificial Intelligence (MAI).7 Global investment in MAI is projected to hit about $6.6 Billion by 2021 as it is anticipated that AI implementations in healthcare can help save $150 Billion in costs by 2026.8At present, a more meaningful applicat...

TOKYO, July 12, 2022 (GLOBE NEWSWIRE) -- The Global Artificial Intelligence Robots Market size was valued at USD 6,214 Million in 2021 and is expected to reach USD 66,662 Million by 2030 growing at a CAGR of 30.5% during the forecast period from 2022 to 2030. Human-robot interaction is becoming more common as robots make everyone's lives easier and more comfortable, and as a result, the market for AI robots is expanding. AI, or machine intelligence in robotic systems, is the implementation of AI technology into robots to allow them to perform repetitive tasks more efficiently without human intervention. AI also enables robots to communicate with other autonomous systems. For entrepreneurs, robotic systems will prove to be more effective and cost-effective labor.

"What do you think the rate of Covid-19 is for us?" This is the question that many Black people living in Berlin asked me at the beginning of March 2020. The answer: We don't know. Unlike other countries, notably the United States and the United Kingdom, the German government does not record racial identity information in official documents and statistics. Due to the country's history with the Holocaust, calling Rasse (race) by its name has long been contested.

The drone strike was the latest in a series of American military operations against ISIS and Al Qaeda in Syria, which have been relatively rare since the fall of the Islamic State's so-called caliphate in 2019. On June 16, Army Delta Force commandos seized Hani Ahmed al-Kurdi, a top Islamic State bomb maker and operations facilitator also known as Salim, in a ground raid in Aleppo, Syria. Nine days later, the United States carried out an airstrike in Idlib Province that the military said killed Abu Hamzah al Yemeni, a senior leader of Hurras al-Din, Al Qaeda's branch in Syria. The U.S. attack on Tuesday came as Mr. Biden prepared to depart for Israel and Saudi Arabia, his first visit to the Middle East as president. The trip will largely focus on Iran's nuclear program and malign activities in the region.