I reiterated Bill Joy's 2000 question: Does the future need us? Little did I know then that a revolution was already brewing. By 2011, GPUs had accelerated considerably the training of deep neural networks, finally making a technology whose roots go back to the early 1940sb competitive. By 2011–2012, AlexNet, a deep neural network, won several international competitions, launching the deep-learning revolution. A decade later, generative AI, which refers to AI that can generate novel content rather than simply analyze or act on existing data, has become all the rage.

Drugs play a central role in modern medicine, but bringing new ones to market is a lengthy, expensive process. Pharmaceutical companies are exploring ways to streamline all aspects of their complex pipelines with artificial intelligence (AI). A key early step is the discovery and design of new molecules that have a desired biochemical effect that can modulate known disease-related processes. To succeed, the molecules must also be suitable for manufacture and drug formulation, and have an acceptably low number of side effects. Finding better candidates and eliminating losers at an early stage makes this process faster and cheaper.

AI's rapid and unstoppable development raises numerous question marks in intellectual property law. It is not surprising that advances related to AI technologies and their use in the creative sector give rise to new development and business opportunities but also to new legal issues, especially related to the identification of the author of the work and the attribution of related rights. One reason for this success is that AI systems offer the most diverse application possibilities, simplifying and speeding up time-consuming processes: from music composition to mastering, from song identification tools to creating highly personalized playlists. This new technology is, therefore, changing how artists create music and audiences hear music. Applications and platforms capable of creating music online include, for example, AIVA, Endel, Xhail, Boomy, Score/Amper, Jukebox, MuseNet, ChatGPT, and, although not yet available, Google's MusicLM.

In an industry that prides itself on staying ahead of the curve, the world of cybersecurity just took a quantum leap into the future. Satu was recently appointed as the CEO of Syhunt, a cybersecurity firm known for its application security and dark web monitoring solutions. But what makes Satu different from other CEOs? Well, for starters, Satu is not human. That's right, in a historic move, Syhunt has appointed an artificial intelligence as its CEO. This marks the first-ever interview with an AI CEO in human history, and we at The Hunter were thrilled to have the opportunity to sit down virtually with Satu and pick its digital brain. The implications of this groundbreaking move are vast, and we were eager to get Satu's take on everything from the future of AI in the cybersecurity industry to the ethical considerations of having an AI in a leadership position.

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Vessel transit in ice-covered waters poses unique challenges in safe and efficient motion planning. When the concentration of ice is high, it may not be possible to find collision-free trajectories. Instead, ice can be pushed out of the way if it is small or if contact occurs near the edge of the ice. In this work, we propose a real-time navigation framework that minimizes collisions with ice and distance travelled by the vessel. We exploit a lattice-based planner with a cost that captures the ship interaction with ice. To address the dynamic nature of the environment, we plan motion in a receding horizon manner based on updated vessel and ice state information. Further, we present a novel planning heuristic for evaluating the cost-to-go, which is applicable to navigation in a channel without a fixed goal location. The performance of our planner is evaluated across several levels of ice concentration both in simulated and in real-world experiments.

Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of rewards. In practice, robots are usually deployed in cluttered environments, containing many obstacles and narrow passageways. Designing dense effective rewards is challenging, resulting in exploration issues during training. Such a problem becomes even more serious when tasks are described using temporal logic specifications. This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula. To overcome the environmental challenge of exploration during training, we propose a novel path planning-guided reward scheme by integrating sampling-based methods to effectively complete goal-reaching missions. To facilitate LTL satisfaction, our approach decomposes the LTL mission into sub-goal-reaching tasks that are solved in a distributed manner. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale cluttered environments. A video demonstration can be found on YouTube Channel: https://youtu.be/yMh_NUNWxho.

The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper we examine the role of social messaging shared by Persons in the Public Eye (i.e. athletes, politicians, news personnel) in determining overall public discourse direction. We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first two years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light.

Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data. However, the task-specific evaluation metric (e.g., PESQ) is usually non-differentiable and can not be directly constructed in the training criteria. This mismatch between the training objective and evaluation metric likely results in sub-optimal performance. To alleviate it, we propose a metric-oriented speech enhancement method (MOSE), which leverages the recent advances in the diffusion probabilistic model and integrates a metric-oriented training strategy into its reverse process. Specifically, we design an actor-critic based framework that considers the evaluation metric as a posterior reward, thus guiding the reverse process to the metric-increasing direction. The experimental results demonstrate that MOSE obviously benefits from metric-oriented training and surpasses the generative baselines in terms of all evaluation metrics.

Artificial intelligence (AI) systems can provide many beneficial capabilities but also risks of adverse events. Some AI systems could present risks of events with very high or catastrophic consequences at societal scale. The US National Institute of Standards and Technology (NIST) has been developing the NIST Artificial Intelligence Risk Management Framework (AI RMF) as voluntary guidance on AI risk assessment and management for AI developers and others. For addressing risks of events with catastrophic consequences, NIST indicated a need to translate from high level principles to actionable risk management guidance. In this document, we provide detailed actionable-guidance recommendations focused on identifying and managing risks of events with very high or catastrophic consequences, intended as a risk management practices resource for NIST for AI RMF version 1.0 (released in January 2023), or for AI RMF users, or for other AI risk management guidance and standards as appropriate. We also provide our methodology for our recommendations. We provide actionable-guidance recommendations for AI RMF 1.0 on: identifying risks from potential unintended uses and misuses of AI systems; including catastrophic-risk factors within the scope of risk assessments and impact assessments; identifying and mitigating human rights harms; and reporting information on AI risk factors including catastrophic-risk factors. In addition, we provide recommendations on additional issues for a roadmap for later versions of the AI RMF or supplementary publications. These include: providing an AI RMF Profile with supplementary guidance for cutting-edge increasingly multi-purpose or general-purpose AI. We aim for this work to be a concrete risk-management practices contribution, and to stimulate constructive dialogue on how to address catastrophic risks and associated issues in AI standards.