From TikTok to Huawei routers to DJI drones, rising tensions between China and the US have made Americans--and the US government--increasingly wary of Chinese-owned technologies. But thanks to the complexity of the hardware supply chain, encryption chips sold by the subsidiary of a company specifically flagged in warnings from the US Department of Commerce for its ties to the Chinese military have found their way into the storage hardware of military and intelligence networks across the West. In July of 2021, the Commerce Department's Bureau of Industry and Security added the Hangzhou, China-based encryption chip manufacturer Hualan Microelectronics, also known as Sage Microelectronics, to its so-called "Entity List," a vaguely named trade restrictions list that highlights companies "acting contrary to the foreign policy interests of the United States." Specifically, the bureau noted that Hualan had been added to the list for "acquiring and ... attempting to acquire US-origin items in support of military modernization for [China's] People's Liberation Army." Yet nearly two years later, Hualan--and in particular its subsidiary known as Initio, a company originally headquartered in Taiwan that it acquired in 2016--still supplies encryption microcontroller chips to Western manufacturers of encrypted hard drives, including several that list as customers on their websites Western governments' aerospace, military, and intelligence agencies: NASA, NATO, and the US and UK militaries.

The war in Ukraine has entered a new stage; this much we know. In the weeks leading up to the Ukrainian armed forces' long-anticipated counter-offensive, a series of attacks in Moscow and in the Belgorod Region, near the border with Ukraine, marked the most significant incursions into Russia since the full-scale war began. In the Belgorod Region, armed units overran villages and took hostages. In Moscow, two drones were shot down near the Kremlin on May 3rd. Four weeks later, eight drones crashed into residential buildings on the outskirts of the city.

Lawmakers from the European Union have taken a big step towards regulating the use of artificial intelligence systems in the region by passing a draft law known as the AI Act. As The New York Times notes, it's merely one part of a longer process, and the soonest we can see the final version of the law is most likely later this year. But if the law gets enforced in its current form, then the European Union will be banning the use of real-time and remote biometric systems, such as facial recognition scanning. It will also prohibit devices that exhibit cognitive behavioral manipulation, such as voice activated toys deemed to encourage dangerous behavior in children. Technologies that classify people based on their socio-economic status or personal characteristics will be banned, as well.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. Artificial Intelligence (AI) contributed 4.9% of layoffs in May, as noted in a recent job report analysis. This is particularly notable, as the report hasn't previously discussed AI as a reason for job cuts. Companies, like IBM, are adapting to this trend, halting hiring for positions they foresee being automated. Although the growth of AI might appear unsettling, considering that many rely on their jobs for economic stability, it does not necessarily spell disaster for the workforce.

New Natural Langauge Process (NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty and Xiezhi-Interdiscipline, both with 15k questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management.

Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dynamics require the planning module to integrate future environmental changes so that action decisions made earlier do not quickly become outdated. We propose a Monte Carlo tree search method which not only well balances the environment exploration and exploitation in space, but also catches up to the temporal environmental dynamics. This is achieved by incorporating multi-objective optimization and a look-ahead model-predictive rewarding mechanism. We show that by allowing the robot to leverage the simulated and predicted spatiotemporal environmental process, the proposed informative planning approach achieves a superior performance after comparing with other baseline methods in terms of the root mean square error of the environment model and the distance to the ground truth.

Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what might happen next and act accordingly with respect to an underlying objective. Such world models hold strong promises for planning in complex environments like in autonomous driving. A human driver, or a self-driving system, perceives their surroundings with their eyes or their cameras. They infer an internal representation of the world which should: (i) have spatial memory (e.g. occlusions), (ii) fill partially observable or noisy inputs (e.g. when blinded by sunlight), and (iii) be able to reason about unobservable events probabilistically (e.g. predict different possible futures). They are embodied intelligent agents that can predict, plan, and act in the physical world through their world model. In this thesis we present a general framework to train a world model and a policy, parameterised by deep neural networks, from camera observations and expert demonstrations. We leverage important computer vision concepts such as geometry, semantics, and motion to scale world models to complex urban driving scenes. First, we propose a model that predicts important quantities in computer vision: depth, semantic segmentation, and optical flow. We then use 3D geometry as an inductive bias to operate in the bird's-eye view space. We present for the first time a model that can predict probabilistic future trajectories of dynamic agents in bird's-eye view from 360{\deg} surround monocular cameras only. Finally, we demonstrate the benefits of learning a world model in closed-loop driving. Our model can jointly predict static scene, dynamic scene, and ego-behaviour in an urban driving environment.

As the reliance on wireless sensor networks (WSNs) rises in numerous sectors, cyberattack prevention and data transmission integrity become essential problems. This study provides a complete framework to handle these difficulties by integrating a cognitive intelligence (CI) framework, an information processing protocol, and sophisticated artificial intelligence (AI) and big data analytics approaches. The CI architecture is intended to improve WSN security by dynamically reacting to an evolving threat scenario. It employs artificial intelligence algorithms to continuously monitor and analyze network behavior, identifying and mitigating any intrusions in real time. Anomaly detection algorithms are also included in the framework to identify packet drop instances caused by attacks or network congestion. To support the CI architecture, an information processing protocol focusing on efficient and secure data transfer within the WSN is introduced. To protect data integrity and prevent unwanted access, this protocol includes encryption and authentication techniques. Furthermore, it enhances the routing process with the use of AI and big data approaches, providing reliable and timely packet delivery. Extensive simulations and tests are carried out to assess the efficiency of the suggested framework. The findings show that it is capable of detecting and preventing several forms of assaults, including as denial-of-service (DoS) attacks, node compromise, and data tampering. Furthermore, the framework is highly resilient to packet drop occurrences, which improves the WSN's overall reliability and performance

Multi-task missions for unmanned aerial vehicles (UAVs) involving inspection and landing tasks are challenging for novice pilots due to the difficulties associated with depth perception and the control interface. We propose a shared autonomy system, alongside supplementary information displays, to assist pilots to successfully complete multi-task missions without any pilot training. Our approach comprises of three modules: (1) a perception module that encodes visual information onto a latent representation, (2) a policy module that augments pilot's actions, and (3) an information augmentation module that provides additional information to the pilot. The policy module is trained in simulation with simulated users and transferred to the real world without modification in a user study (n=29), alongside supplementary information schemes including learnt red/green light feedback cues and an augmented reality display. The pilot's intent is unknown to the policy module and is inferred from the pilot's input and UAV's states. The assistant increased task success rate for the landing and inspection tasks from [16.67% & 54.29%] respectively to [95.59% & 96.22%]. With the assistant, inexperienced pilots achieved similar performance to experienced pilots. Red/green light feedback cues reduced the required time by 19.53% and trajectory length by 17.86% for the inspection task, where participants rated it as their preferred condition due to the intuitive interface and providing reassurance. This work demonstrates that simple user models can train shared autonomy systems in simulation, and transfer to physical tasks to estimate user intent and provide effective assistance and information to the pilot.

Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a limited number of examples are provided. Unlike humans who excel at such tasks, these models often fail to leverage known relationships between entities in order to draw conclusions about such objects. In this work, we show that incorporating a symbolic knowledge graph into a state-of-the-art recognition model enables a new approach for effective few-shot classification. In our proposed neuro-symbolic architecture and training methodology, the knowledge graph is augmented with additional relationships extracted from a small set of examples, improving its ability to recognize novel objects by considering the presence of interconnected entities. Unlike existing few-shot classifiers, we show that this enables our model to incorporate not only objects but also abstract concepts and affordances. The existence of the knowledge graph also makes this approach amenable to interpretability through analysis of the relationships contained within it. We empirically show that our approach outperforms current state-of-the-art few-shot multi-label classification methods on the COCO dataset and evaluate the addition of abstract concepts and affordances on the Visual Genome dataset.