A robot that uses the same technology as self-driving vehicles is transporting medicines around hospitals as part of a new trial. The'helper bot' is being used to carry and deliver prescriptions and other items around Milton Keynes University Hospital, helping to relieve pressure on human staff. It is the creation of British firm Academy of Robotics, which has already worked on autonomous technology for its'Kar-Go' self-driving vehicle. Just like Kar-Go, the bot uses sonar and LiDAR technology to navigate around obstacles such as people, wheelchairs and beds inside the hospital. The robot uses a combination of three types of sensors to see both into the distance and to understand how close objects are and how they are moving in relation to its own path.

The entrepreneurial disruption phase of "move fast and break things" is being replaced with a mantra of "move fast and keep up" when it comes to applying ethical frameworks and leading practices to emerging technologies, according to a new study by Deloitte. The firm's first-ever State of Ethics and Trust in Technology annual report defines emerging technologies, identifies trustworthy and ethical standards, explains different approaches to operationalizing standards, and encourages actions that can be taken in the short term. Many companies want to be on the cutting edge of emerging technologies to stay competitive and gain benefits such as improved customer experience, operational efficiencies and newly-enabled use cases, according to Deloitte. "But these technologies are often being developed at such breakneck speeds that few companies are pausing to consider the ethical implications,'' the report noted. "With great power comes great responsibility.

Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs. All of the presented theoretical results are extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data.

Most real-world networks suffer from incompleteness or incorrectness, which is an inherent attribute to real-world datasets. As a consequence, those downstream machine learning tasks in complex network like community detection methods may yield less satisfactory results, i.e., a proper preprocessing measure is required here. To address this issue, in this paper, we design a new community attribute based link prediction strategy HAP and propose a two-step community enhancement algorithm with automatic evolution process based on HAP. This paper aims at providing a community enhancement measure through adding links to clarify ambiguous community structures. The HAP method takes the neighbourhood uncertainty and Shannon entropy to identify boundary nodes, and establishes links by considering the nodes' community attributes and community size at the same time. The experimental results on twelve real-world datasets with ground truth community indicate that the proposed link prediction method outperforms other baseline methods and the enhancement of community follows the expected evolution process.

Network Intrusion and Detection Systems (NIDS) are essential for malicious traffic and cyberattack detection in modern networks. Artificial intelligence-based NIDS are powerful tools that can learn complex data correlations for accurate attack prediction. Graph Neural Networks (GNNs) provide an opportunity to analyze network topology along with flow features which makes them particularly suitable for NIDS applications. However, successful application of such tool requires large amounts of carefully collected and labeled data for training and testing. In this paper we inspect different versions of ToN-IoT dataset and point out inconsistencies in some versions. We filter the full version of ToN-IoT and present a new version labeled ToN-IoT-R. To ensure generalization we propose a new standardized and compact set of flow features which are derived solely from NetFlowv5-compatible data. We separate numeric data and flags into different categories and propose a new dataset-agnostic normalization approach for numeric features. This allows us to preserve meaning of flow flags and we propose to conduct targeted analysis based on, for instance, network protocols. For flow classification we use E-GraphSage algorithm with modified node initialization technique that allows us to add node degree to node features. We achieve high classification accuracy on ToN-IoT-R and compare it with previously published results for ToN-IoT, NF-ToN-IoT, and NF-ToN-IoT-v2. We highlight the importance of careful data collection and labeling and appropriate data preprocessing choice and conclude that the proposed set of features is more applicable for real NIDS due to being less demanding to traffic monitoring equipment while preserving high flow classification accuracy.

According to the few recorded accounts Next we turn our attention to the research domain and of security failures "in the wild," ML systems can be broken take a snapshot of the current landscape of adversarial ML by naïve attackers that are not systematically exploiting the as portrayed in scientific papers ( IV). After surveying the vulnerabilities of ML, but rather are developing attacks by proceedings of the "Top-4" security conferences from 2019 guessing--either indiscriminately or by some coarse heuristic to 2021, we systematically analyze all 88 papers that consider [6], [7]. Red-team exercises on ML systems often take attacks against ML or corresponding defenses. Of these papers, advantage of security gaps that are agnostic to the existence 89% only evaluate algorithms based on neural networks, 63% of an ML model, and subsequent defensive recommendations focus on computer vision, and 80% perform their experiments are likewise more broad than, e.g., adversarial training [8], on "benchmarks". We discover several inconsistencies in the [9]. Additionally, the ML models deployed in productiongrade terminology adopted in reputable prior work. We also identify ML systems are often not directly observable (and are several positive trends, such as an increasing amount of papers sometimes even unreachable) by most attackers [10].

After a year's hiatus, the AI Debate hosted by Gary Marcus and Vincent Boucher returned with a gaggle of AI thinkers, this time including policy types and scholars outside of the discipline of AI such as Noam Chomsky. After a one-year hiatus, the annual artificial intelligence debate organized by Montreal.ai Learn about the leading tech trends the world will lean into over the next 12 months and how they will affect your life and your job. The debate this year, AI Debate 3: The AGI Debate, as it's called, focused on the concept of artificial general intelligence, the notion of a machine capable of integrating a myriad of reasoning abilities approaching human levels. While the previous debate featured a number of AI scholars, Friday's meet-up drew participation by 16 participants from a much wider gamut of professional backgrounds. In addition to numerous computer scientists and AI luminaries, the program included legendary linguist and activist Noam Chomsky, computational neuroscientist Konrad Kording, and Canadian parliament member Michelle Rempel Garner. Also: AI's true goal may no longer be intelligence Marcus was once again joined by his co-host, Vincent Boucher of Montreal.ai. The debate ran longer than planned. The full 3.5 hours can be viewed on the YouTube page for the debate. The debate Web site is agidebate dot com. In addition, you may want to follow the hashtag #agidebate. NYU professor emeritus and AI gadfly Gary Marcus resumed his duties hosting the multi-scholar face-off. Marcus started things off with a slide show of a "very brief history of AI," tongue firmly in cheek. Marcus said that contrary to enthusiasm in the decade following the landmark ImageNet success, the "promise" of machines doing various things had not paid off. He featured reference to his own New Yorker article throwing cold water on the matter.

The European Union is set to create the world's first broad standards for regulating artificial intelligence. As well as determining how the technology affects the lives of almost 450 million citizens in the 27 countries of the EU, the rules are likely to influence how AI is used elsewhere in the world. "The idea is that you have a harmonised system, which is really good," says Sandra Wachter at the University of Oxford.

Ukraine has bought some 1,400 drones, mostly for reconnaissance, and plans to develop combat models that can attack the exploding drones Russia has used during its invasion of the country, according to the Ukrainian government minister in charge of technology. In a recent interview with The Associated Press, Minister of Digital Transformation Mykhailo Fedorov described Russia's war in Ukraine as the first major war of the internet age. He credited drones and satellite internet systems like Elon Musk's Starlink with having transformed the conflict. Ukraine has purchased drones like the Fly Eye, a small unmanned aerial vehicle used for intelligence, battlefield surveillance and reconnaissance. "And the next stage, now that we are more or less equipped with reconnaissance drones, is strike drones," Fedorov said.

Winter on Mars transforms the Red Planet into something spectacular, but it's not quite like like a Hallmark greeting card's holiday scene. Temperatures at the planet's poles plummet to bone-chilling lows of minus 190 degrees Fahrenheit. Although humans are years from colonizing Mars, NASA's robotic rovers on the planet reveal a few discoveries about the colder season. The HiRISE camera aboard NASA's Mars Reconnaissance Orbiter captured these images of sand dunes covered by frost just after winter solstice The Mars Reconnaissance Orbiter has been in orbit for more than 16 years and has returned over 436 terabits of data back to NASA. Mars is the fourth planet from the sun, with a'near-dead' dusty, cold, desert world with a very thin atmosphere.