As 5G coverage around the world slowly increases, Singapore has become the first country in the world to be fully covered by standalone 5G. The island nation now has achieved over 95% of 5G nationwide coverage by Singtel. The initial target set by Singapore was for the nation to only reach the target by the end of 2025. However, Singtel has managed to achieve these three years ahead of schedule. While some would argue that the size of the country made deployment faster, the reality is Singapore has had a solid plan for its 5G coverage since the network was first deployed in the country.

The power of AI and ML play a substantial role in defining cybersecurity processes.   Every day, the attack surface keeps on getting bigger and bigger. Threats have not only become complex but are also more difficult to detect. On a daily basis, it is common for cybersecurity teams to grapple with a huge number […]

The next generation of artificial intelligence will not only engage in database comparisons but learn in real-time and assist in taking on …

Rapid new drug discovery had never been more critical in a world with an aging population and increased instances of infectious diseases. While traditional lab methods have proven reliable, the recent covid-19 pandemic has shown the need for further innovation. The global drug discovery market size was valued at US$ 74.96 billion in 2021.[1] Despite such massive investments, the number of new drugs approved by the FDA remains low. Current drug discovery methods are slow, expensive, dominated by big pharma, and require cruel animal testing procedures.

Like the steam engine, electricity, or the internet, Artificial Intelligence is a general purpose technology – with the potential to revolutionise every aspect of our lives, help realise our ambitions to be a science superpower, and to foster economic growth across the UK. The UK excels at AI – from scientific research, where we rank third in the world for number of academic journal citations; to investment – receiving more investment in AI companies than France and Germany combined in 2021. As a Government, we are committed to unlocking the enormous benefits of AI across our economy and society. That is why we have invested over £2.3 billion in AI since 2014, which has been bolstered year-on-year by ambitious announcements such as the creation of the NHS AI Lab to drive use of AI in improving healthcare, to the creation of Turing AI World-leading Researcher Fellowships, to ensure the UK attracts and retains the best and brightest AI talent. Last September, we also published the National AI Strategy – a 10-year vision to ensure the UK is the best place to start and grow an AI business and to strengthen our position as a global AI leader.

Maverick's next wingman could be a drone. In the movies, fighter pilots are depicted as highly trained military aviators with the skills and experience to defeat adversaries in thrilling aerial dogfights. New technologies, though, are set to redefine what it means to be a "Top Gun," as algorithms, data and machines take on a bigger role in the cockpit -- changes hinted at in "Top Gun: Maverick." "A lot of people talk about, you know, the way of the future, possibly taking the pilot out of the aircraft," said 1st Lt. Walker Gall, an F-35 pilot with the U.S. 48th Fighter Wing based at RAF Lakenheath in England. "That's definitely not something that any of us look forward to." "I'd like to keep my job as long as possible, but I mean, it's hard to argue with newer and newer technology," he said.

Artificial intelligence plays an important role in the digitalization of many banks, but it could turn into a regulatory minefield in the coming years. Preparations are underway in the European Union for regulations on the use of artificial intelligence (AI). While the process is still in limbo, the thrust of the planned rules provides clues as to what companies need to prepare for. Swiss AI and the analytics consultancy «Unit8» published a white paper outlining the areas of concern arising from such regulation. Companies would do well to prepare for the new rules and already implement them as a preventive measure, it recommends, even though Switzerland is not part of the EU.

The Ukrainian army continues to defend its land against the invading Russians and has recently destroyed a Russian tank and killed more than a dozen soldiers as the war enters its sixth month, a new recording revealed. The Security Service of Ukraine (SSU) on Sunday released new audio of an intercepted phone call where a Russian soldier, whose identity was not revealed, recounted the attack to his mother. "Mom, we had such a'circus' today! At two o'clock in the morning – fifteen '200' [killed]. Because they all were watching a movie in the tank. They threw it [a projectile] into the turret from a drone! They just approached us, dropped the ammo, and it blew up," the soldier said.

Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion self-supervision by building on the cognitive science concept of a Spelke Object: a set of physical stuff that moves together. We introduce the Excitatory-Inhibitory Segment Extraction Network (EISEN), which learns to extract pairwise affinity graphs for static scenes from motion-based training signals. EISEN then produces segments from affinities using a novel graph propagation and competition network. During training, objects that undergo correlated motion (such as robot arms and the objects they move) are decoupled by a bootstrapping process: EISEN explains away the motion of objects it has already learned to segment. We show that EISEN achieves a substantial improvement in the state of the art for self-supervised image segmentation on challenging synthetic and real-world robotics datasets.

Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches proposed so far in the literature have severe limitations: they either require prior domain knowledge used to design the anomaly discovery algorithms, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose an unsupervised method suitable for domain agnostic subsequence anomaly detection. Our method, Series2Graph, is based on a graph representation of a novel low-dimensionality embedding of subsequences. Series2Graph needs neither labeled instances (like supervised techniques) nor anomaly-free data (like zero-positive learning techniques), and identifies anomalies of varying lengths. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies without any prior knowledge of their characteristics, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster. This paper has appeared in VLDB 2020.