Six "unarmed" Palestinians were "deliberately" killed in an Israeli drone strike in the occupied West Bank.

Microsoft, a notoriously conservative and slow-moving giant, is bringing artificial intelligence right into the heart of Windows. But. after investing a total of $13 billion in ChatGPT-maker OpenAI (and acquiring a 49 percent stake in the process), will AI actually make its products better? Bing Chat officially kicked off its year of AI, while Copilot, assisting with its AI smarts, subsequently launched on Edge, Microsoft 365 products like Word and Powerpoint and eventually Windows 11. While the AI interactions aren't perfect, the one constant around AI is that everything is changing incredibly quickly. Microsoft has already announced Copilot will be upgraded with the more powerful GPT-4 Turbo and Dall-E 3 models.

Autonomous vehicles could be on UK roads as soon as 2026, the transport secretary has said, as ministers seeks to capture as much as £42bn of the international self-driving market within the coming decade. "This technology exists, it works, and what we're doing is putting in place the proper legislation so that people can have full confidence in the safety of this technology," Mark Harper told BBC Radio 4's Today programme on Wednesday. Asked if people would be able to travel in self-driving vehicles "with your hands off the wheel, doing your emails" in 2026, Harper replied: "Yes, and I think that's when companies are expecting – in 2026, during that year – that we'll start seeing this technology rolled out." Responding to a question by the former Top Gear presenter James May – who was Today's guest editor – about why the government was supporting the development of autonomous driving, Harper claimed there were "a few" reasons. He said: "I think it will actually improve road safety. We already have a very good road safety record in Britain but there are still several thousand people a year killed on our roads. "It's a big economic opportunity for Britain to get what will be a big global share of market.

As wildfire activity reaches record levels, the tech integration company SAIC is developing artificial intelligence technology that can help predict when they'll happen, how to stop them, and how to keep folks safe. A long list of new products and developments made 2023 possibly the biggest year yet for artificial intelligence, with major tech companies breaking into action and everyday consumers becoming increasingly aware of the rapidly developing technology. "2023 was a banner year for AI in that we saw both investment and public interest explode," Samuel Mangold-Lenett, a staff editor at The Federalist, told Fox News Digital. "We also saw how AI can revolutionize every aspect of every major industry. From defense to finance to dating apps, AI proved it's here to stay."

There is a real potential for this sort of technology to actually improve safety on the roads, not just for drivers, not just for passengers, but for other vulnerable road users - pedestrians, cyclists - to really improve road safety, which is a real win for everybody.

Reza Pahlavi, exiled crown prince of Iran, weighs in on Iranian threats to shut the Mediterranean Sea amid war with Israel and the threat America faces from the country and its proxies. The U.S. Navy fired anti-ship ballistic missiles on Tuesday against incoming Iran-backed Houthi missiles in the Red Sea, signaling a significant escalation in the region, a senior defense official told Fox News. The Navy engaged three ballistic missiles provided to Yemen's Houthis by Iran. It was the first time the Navy shot down an incoming ballistic missile using an anti-ship ballistic missile. The USS Laboon and assets from the Eisenhower Carrier Strike Group shot down 12 one-way attack drones, three anti-ship ballistic missiles and two land attack missiles fired by the Houthis over a 12-hour period, U.S. Central Command said.

Vanderbilt University, Nashville, Tennessee, USA taylor.johnson@vanderbilt.edu Abstract This report summarizes the 4th International Verification of Neural Networks Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 35th International Conference on Computer-Aided Verification (CAV). The VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2023 iteration, 7 teams participated on a diverse set of 10 scored and 4 unscored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.

We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an information-theoretic measure rooted in a generalized version of Granger-causality, which is applicable to both linear and nonlinear dynamics. Our framework offers advancements in measuring systemic risk and establishes meaningful connections with established econometric models, including vector autoregression and switching models. We evaluate the efficacy of our proposed model through simulation experiments and empirical analysis, reporting promising results in recovering simulated time-varying networks with nonlinear and multivariate structures. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network. We focus on cryptocurrencies' potential systemic risks to financial stability, including spillover effects on other sectors during crises like the COVID-19 pandemic and the Federal Reserve's 2020 emergency response. Our findings reveals significant, previously underrecognized pre-2020 influences of cryptocurrencies on certain financial sectors, highlighting their potential systemic risks and offering a systematic approach in tracking evolving cross-sector interactions within financial networks.

As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest. News intent refers to the purpose or intention behind the creation of a news article. While the significance of research on news intent has been widely acknowledged, the absence of a systematic news intent understanding framework hinders further exploration of news intent and its downstream applications. To bridge this gap, we propose News INTent (NINT) frame, the first component-aware formalism for understanding the news creation intent based on research in philosophy, psychology, and cognitive science. Within this frame, we define the news intent identification task and provide a benchmark dataset with fine-grained labels along with an efficient benchmark method. Experiments demonstrate that NINT is beneficial in both the intent identification task and downstream tasks that demand a profound understanding of news. This work marks a foundational step towards a more systematic exploration of news creation intents.

Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms' activities often rely on vision-based or light detection and ranging (LiDAR) sensors, which require line-of-sight (LoS) access and may raise privacy concerns, for example, in nursing facilities. This research pioneers an innovative approach harnessing channel state information (CSI) measured from WiFi signals, subtly influenced by the activity of robotic arms. We developed an attention-based network to classify eight distinct activities performed by a Franka Emika robotic arm in different situations. Our proposed bidirectional vision transformer-concatenated (BiVTC) methodology aspires to predict robotic arm activities accurately, even when trained on activities with different velocities, all without dependency on external or internal sensors or visual aids. Considering the high dependency of CSI data to the environment, motivated us to study the problem of sniffer location selection, by systematically changing the sniffer's location and collecting different sets of data. Finally, this paper also marks the first publication of the CSI data of eight distinct robotic arm activities, collectively referred to as RoboFiSense. This initiative aims to provide a benchmark dataset and baselines to the research community, fostering advancements in the field of robotics sensing.