A two-legged robot that can chase you down in the street may sound like something dreamed up for the latest episode of Black Mirror. But it may not be far from reality, after a two-legged robot known as'Cassie' set a new Guinness World Record for the fastest 100 metres by a bipedal robot. Cassie clocked the historic time of 24.73 seconds, starting from a standing position and returning to that position after the sprint, with no falls. While that's more than 15 seconds slower than the world record for a 100-metre sprint by a human, held by Usain Bolt at 9.58 seconds, it is faster than most humans would take to jog the same distance. Oregon State robotics professor Jonathan Hurst, who led the development of the robot, described it as'a big watershed moment'.

Individuals and companies that suffer harm from drones, robots and other products or services equipped with artificial intelligence software will find it easier to sue for compensation under EU draft rules seen by Reuters. The AI Liability Directive, which the European Commission will announce on Wednesday, aims to address the increasing proliferation of AI-enabled products and services and the patchwork of national rules across the 27-country European Union. Victims can sue for compensation for harm to their life, property, health and privacy due to the fault or omission of a provider, developer or user of AI technology or was discriminated in a recruitment process using AI, the draft rules said. The rules seek to lighten the burden of proof on victims by introducing a "presumption of causality," which means victims only need to show that a manufacturer or user's failure to comply with certain requirements caused the harm and then link this to the AI technology in their lawsuit. Under a "right of access to evidence," victims can ask a court to order companies and suppliers to provide information about high-risk AI systems so that they can identify the liable person and find out what went wrong.

In Ukraine's port city of Odessa, residents have recently found themselves hiding not from the thunder of rocket attacks but from the whir of buzzing Iranian drones in the sky. The machines have been playing an important role since Russia invaded seven months ago -- forming part of reconnaissance operations, missile firings or bomb drops. Awakened with a start on Saturday morning by a roar from the sky, Maryna Kondratieva ran to hide in the cellar with her two young children, fearing the worst. "I understand now that everything can change in five minutes," Kondratieva, who lives in a well-to-do part of the city and whose terrace overlooks the Black Sea, told AFP. Odessa -- the'capital' of the southwest and Ukraine's main port -- had seemed largely safe from Moscow, whose troops failed to take it at the beginning of the war.

The Association for Uncrewed Vehicle Systems International (AUVSI), the world's leading trade association for uncrewed vehicle systems, has announced a collaboration with the Defense Innovation Unit (DIU) to further commercial cyber methodologies to build a shared standard--like the one used to develop the DIU's Blue UAS "Cleared List." AUVSI's effort is designed to expand the number of vetted Uncrewed Aircraft Systems, or UAS, that meet Congressional and federal agency drone security requirements. "The goal of this new pilot initiative is to extend relevant cyber credentialing across the U.S. industrial base, proactively, streamlining and accelerating capabilities available to the Department of Defense (DoD) and the rest of the U.S. government," said Brian Wynne, AUVSI President and CEO. "We are grateful for DIU's partnership and look forward to working with them to make the US drone industry more resilient and secure." AUVSI efforts will streamline the vetting process and further expand the potential small UAS entrants to the government through its Trusted Cyber Program.

The head of the UK's largest and most advanced robotics centre has said that society needs to prepare for the increased integration of robots but shouldn't fear the rise of artificial intelligence (AI). Stewart Miller, the chief executive of the National Robotarium, which opens today in Edinburgh, told Sky News that "Inevitably there will be more robots in everybody's life. They'll be helping you at home, when you go out shopping, when you go to a hotel, they'll be involved in hospitality, when you go to a theatre, everything. Internationally, some scientists have expressed concerns over rapid progress in the field of artificial intelligence. A new survey of researchers from the New York University Centre for Data Science found that more than a third (36%) of respondents that had published recent papers in the field thought that AI could produce catastrophic outcomes in this century, "on the level of all-out nuclear war". Mr Miller said that "the thing to remembers is that we, the humans, are in control.

In this paper, we examine the development and usage of six low-resource machine translation systems translating between the Ukrainian language and each of the official languages of the Baltic states. We developed these systems in reaction to the escalating Ukrainian refugee crisis caused by the Russian military aggression in Ukraine in the hope that they might be helpful for refugees and public administrations. Now, two months after MT systems were made public, we analyze their usage patterns and statistics. Our findings show that the Latvian-Ukrainian and Lithuanian-Ukrainian systems are integrated into the public services of Baltic states, leading to more than 127 million translated sentences for the Lithuanian-Ukrainian system. Motivated by these findings, we further enhance our MT systems by better Ukrainian toponym translation and publish an improved version of the Lithuanian-Ukrainian system.

We show that quantum states with "low stabilizer complexity" can be efficiently distinguished from Haar-random. Specifically, given an $n$-qubit pure state $|\psi\rangle$, we give an efficient algorithm that distinguishes whether $|\psi\rangle$ is (i) Haar-random or (ii) a state with stabilizer fidelity at least $\frac{1}{k}$ (i.e., has fidelity at least $\frac{1}{k}$ with some stabilizer state), promised that one of these is the case. With black-box access to $|\psi\rangle$, our algorithm uses $O\!\left( k^{12} \log(1/\delta)\right)$ copies of $|\psi\rangle$ and $O\!\left(n k^{12} \log(1/\delta)\right)$ time to succeed with probability at least $1-\delta$, and, with access to a state preparation unitary for $|\psi\rangle$ (and its inverse), $O\!\left( k^{3} \log(1/\delta)\right)$ queries and $O\!\left(n k^{3} \log(1/\delta)\right)$ time suffice. As a corollary, we prove that $\omega(\log(n))$ $T$-gates are necessary for any Clifford+$T$ circuit to prepare computationally pseudorandom quantum states, a first-of-its-kind lower bound.

Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the \underline{com}plete and \underline{e}fficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (\url{https://github.com/divelab/DIG}).

Whilst assistive robots [7] have been embedded into social and health care environments [1, 2, 10], they have largely been limited to simple applications, such as support for social and physical activities and hall monitoring, but often without considering potential interactions with humans. To expand the range of these applications, the human user and the robot need to interact in order to perform tasks together [4]. As such, this interaction, which is still underexplored in the social care domain, should be prioritised, with an emphasis on the safety of the human [3, 9]. To enable the development of applications that support such interaction and to ensure its safety, we propose an adaptive mission and path finding framework for an autonomous robot operating in a homecare environment. The framework models the environment as a graph, with nodes representing key locations within the environment where the robot can perform local tasks. Missions are modelled as a repertoire of locations within the environment where a task requires completion. The main contributions of our'research preview' paper are: (i) a generalised approach for modelling environments as graphs with edges represented as levels of risk, (ii) a modified Dijkstra's algorithm for performing path finding in uncertain environments with a cost function to reduce risk, (iii) simple human predictive behaviour model that forecasts human intention allowing for adaptive path finding using heat maps to artificially increase the risk associated with specific edges in the graph, (iv) a framework that combines modelling methods, adaptive path finding techniques and run-time probabilistic model generation for safety verification into an end-to-end solution for autonomous robotic mission planning, (v) finally, a simulation-based case study that shows the effectiveness of the framework.

The exceptional progress in the field of machine learning (ML) in recent years has attracted a lot of interest in using this technology in aviation. Possible airborne applications of ML include safety-critical functions, which must be developed in compliance with rigorous certification standards of the aviation industry. Current certification standards for the aviation industry were developed prior to the ML renaissance without taking specifics of ML technology into account. There are some fundamental incompatibilities between traditional design assurance approaches and certain aspects of ML-based systems. In this paper, we analyze the current airborne certification standards and show that all objectives of the standards can be achieved for a low-criticality ML-based system if certain assumptions about ML development workflow are applied.