As with many of us, after three years of staying home, I realized a few months ago that I'd had it with the pandemic. Having traveled up to 70% for years before Covid-19, my initial reaction to being in the same time zone and same building and bed was pure gratitude, even bliss. I wanted to go out and experience the world again. During our last Omicron-initiated staycation over the Christmas holidays, my 14-year-old son stated in his very polished, diplomatic and convincing style that he was bored. As soon as we learned Omicron was manageable, and there would be a break from lockdowns and fewer travel restrictions, we decided to get on with it and book some memorable holidays. So came the trips to Costa Rica and the Dominican Republic.

Russia's war in Ukraine is becoming a testing ground for loitering ammunition. How is artificial intelligence changing the future of military deterrence? The Russian attack on Ukraine shows that wars of conquest are not an artifact of the past. This reversion to an outdated notion of territorial integrity of states, visible since 2014 at the latest, puts the concept of deterrence back on the political agenda of many democracies. The new German government now wants to make the contribution to NATO that the then U.S. President Donald Trump, for example, demanded with media attention a few years ago.

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences). We construct a novel dataset for focus, coverage, and inter-sentential coherence, and develop automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods, including question answering (QA) approaches, semantic textual similarity (STS), next-sentence prediction (NSP), and scores derived from 19 pre-trained language models. We then apply the developed metrics in evaluating a broad range of summarization models across two datasets, with some surprising findings.

Phishing attacks are cyber-attacks through which criminals trick users into sending them money and sensitive information, or into installing malware on their computer, by sending them deceptive emails or messages. As these attacks have become increasingly widespread, developers have been trying to develop more advanced tools to detect them and protect potential victims. Researchers at Monash University and CSIRO's Data61 in Australia have recently developed a machine learning-based approach that could help users to identify phishing emails, so that they don't inadvertently install malware or send sensitive data to cyber-criminals. This model was introduced in a paper pre-published on arXiv and set to be presented at AsiaCCS 2022, a cyber-security conference. "We have identified a gap in current phishing research, namely realizing that existing literature focuses on rigorous'black and white' methods to classify whether something is a phishing email or not," Tingmin (Tina) Wu, one of the researchers who carried out the study, told TechXplore.

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.

The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often require data to be stored on a single machine with compute performed by a single node, making them unsuitable for decentralised training on multiple devices. This deficiency has motivated the development of federated learning algorithms, which allow multiple data owners to train collaboratively and use a shared model whilst keeping local data private. However, many of these algorithms focus on obtaining point estimates of model parameters, rather than probabilistic estimates capable of capturing model uncertainty, which is essential in many applications. Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. In this paper we introduce partitioned variational inference (PVI), a general framework for performing VI in the federated setting. We develop new supporting theory for PVI, demonstrating a number of properties that make it an attractive choice for practitioners; use PVI to unify a wealth of fragmented, yet related literature; and provide empirical results that showcase the effectiveness of PVI in a variety of federated settings.

The vast list of artificial intelligence applications continually increases as researchers, technologists, and scientists try to leverage computing power to gain competitive edges over the more slowly adopting set. Today I want to traipse across the American business and tech landscape and present a few of the new and hopefully intriguing upgrades of these mostly familiar devices and services being brought into the 21st century via AI. First a quick overview of the concept of AI and where it's come from over the past years and decades. Earlier writings comingled two phrases to identify the technology: artificial intelligence, which has become the well-known marketable way to talk about the tech, and "computational intelligence," which might be useful amongst a group of AI--err, CI?--subject matter experts, but doesn't carry the cachet of its more widely accepted phrase. For anyone who uses either phrase, it's generally understood to refer to some sort of machine-based intelligence.

The Advancing Data Justice project is a Global Partnership on AI (GPAI) initiative, led by The Alan Turing Institute. Researchers from the Institute have been collaborating with twelve Policy Pilot Partner organisations from Asia, Oceania, Africa, and South America, and each of these have been working to understand what data justice might look like in their distinct contexts. The aims of the project are 1) to gain a better understanding of the current state of research in the field to better inform future research directions, and 2) to create a guide for policymakers, developers, and communities affected by AI, comprising advice on what they should consider in their practice, use and experience of AI systems. As part of the project, the team have recently launched the first instalment of a documentary series which tracks the work of the project partners. They discuss how data-driven technologies can be deployed in a way which is compatible with values of social justice.

RAMSTEIN AIR BASE, Germany – The United States marshaled 40 allies on Tuesday to furnish Ukraine with long-term military aid in what could become a protracted battle against the Russian invasion, and Germany said it would send dozens of armored anti-aircraft vehicles. It was a major policy shift for a country that had wavered over fear of provoking Russia. The announcement by Germany, Europe's biggest economy and one of Russia's most important Western trading partners, was among many signals on Tuesday pointing to further escalation in the war and disappointment for diplomacy. Germany's shift on weapons also was seen as a strong affirmation of a toughened message by the administration of U.S. President Joe Biden, which has said it wants to see Russia not only defeated in Ukraine but seriously weakened from the conflict that Russian President Vladimir Putin began two months ago. The increasing flow of Western weapons into Ukraine -- including howitzers, armed drones, tanks and ammunition -- also amounted to another sign that a war Putin had expected would divide his Western adversaries had instead drawn them much closer together.

Jerry Levine is Chief Evangelist & General Counsel at ContractPodAi. He helps guide global client success and shape overall product vision. It was 25 years ago when IBM's artificial intelligence system, Deep Blue, defeated Garry Kasparov in a six-game rematch of chess. But this competition did not reveal AI to be smarter than its human opponent, who was at the time the reigning world champion; Deep Blue's success demonstrated that we, humans, could program AI to perform functions we cannot do quickly on our own--analyzing vast amounts of data and processing any number of natural languages, just to name a couple of functions. Today, AI continues to attract more attention and interest than most other innovations, including when it comes to nonfungible tokens (NFTS).