Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the goal to provide a synthesis route. Its two primary research directions, single-step retrosynthesis prediction, which models the chemical reaction logic, and multi-step synthesis planning, which tries to find the correct sequence of reactions, are inherently intertwined. Still, this connection is not reflected in contemporary research. In this work, we combine these two major research directions by applying multiple single-step retrosynthesis models within multi-step synthesis planning and analyzing their impact using public and proprietary reaction data. We find a disconnection between high single-step performance and potential route-finding success, suggesting that single-step models must be evaluated within synthesis planning in the future. Furthermore, we show that the commonly used single-step retrosynthesis benchmark dataset USPTO-50k is insufficient as this evaluation task does not represent model performance and scalability on larger and more diverse datasets. For multi-step synthesis planning, we show that the choice of the single-step model can improve the overall success rate of synthesis planning by up to +28% compared to the commonly used baseline model. Finally, we show that each single-step model finds unique synthesis routes, and differs in aspects such as route-finding success, the number of found synthesis routes, and chemical validity, making the combination of single-step retrosynthesis prediction and multi-step synthesis planning a crucial aspect when developing future methods.

Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and understanding. Like for other textual domains, TLMs have indeed pushed the state-of-the-art of AI approaches for many tasks of interest in the legal domain. Despite the first Transformer model being proposed about six years ago, there has been a rapid progress of this technology at an unprecedented rate, whereby BERT and related models represent a major reference, also in the legal domain. This article provides the first systematic overview of TLM-based methods for AI-driven problems and tasks in the legal sphere. A major goal is to highlight research advances in this field so as to understand, on the one hand, how the Transformers have contributed to the success of AI in supporting legal processes, and on the other hand, what are the current limitations and opportunities for further research development.

Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms of building user trust, complying with regulations, using data ethically.Explainable AI (XAI) aims to enable humans understand AI and trust its results. This paper presents a literature review on the recent developments of XAI solutions for medical decision support, based on a representative sample of 198 articles published in recent years. The systematic synthesis of the relevant articles resulted in several findings. (1) model-agnostic XAI techniques were mostly employed in these solutions, (2) deep learning models are utilized more than other types of machine learning models, (3) explainability was applied to promote trust, but very few works reported the physicians participation in the loop, (4) visual and interactive user interface is more useful in understanding the explanation and the recommendation of the system. More research is needed in collaboration between medical and AI experts, that could guide the development of suitable frameworks for the design, implementation, and evaluation of XAI solutions in medicine.

Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.

Ocean modeling is a powerful tool for simulating the physical, chemical, and biological processes of the ocean, which is the foundation for marine science research and operational oceanography. Modern numerical ocean modeling mainly consists of governing equations and numerical algorithms. Nonlinear instability, computational expense, low reusability efficiency and high coupling costs have gradually become the main bottlenecks for the further development of numerical ocean modeling. Recently, artificial intelligence-based modeling in scientific computing has shown revolutionary potential for digital twins and scientific simulations, but the bottlenecks of numerical ocean modeling have not been further solved. Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction. AI-GOMS consists of a backbone model with the Fourier-based Masked Autoencoder structure for basic ocean variable prediction and lightweight fine-tuning models incorporating regional downscaling, wave decoding, and biochemistry coupling modules. AI-GOMS has achieved the best performance in 30 days of prediction for the global ocean basic variables with 15 depth layers at 1/4{\deg} spatial resolution. Beyond the good performance in statistical metrics, AI-GOMS realizes the simulation of mesoscale eddies in the Kuroshio region at 1/12{\deg} spatial resolution and ocean stratification in the tropical Pacific Ocean. AI-GOMS provides a new backbone-downstream paradigm for Earth system modeling, which makes the system transferable, scalable and reusable.

Motivated by the expressiveness of hyperproperties, Hyperproperties extend trace properties to express properties of several hyper-temporal logics, such as HyperLTL [20], sets of traces, and they are increasingly popular in specifying various HyperSTL [38], and HyperMTL [11], were recently proposed by extending security and performance-related properties in domains such the conventional temporal logics such as Linear Temporal as cyber-physical systems, smart grids, and automotive. This paper Logic (LTL) [41], Signal Temporal Logic (STL) [37], and Metric Temporal introduces HyperTWTL, which extends Time Window Temporal Logic (MTL) [33], respectively. Consequently, various model Logic (TWTL)-a domain-specific formal specification language for checking techniques have been proposed for verifying HyperLTL robotics, by allowing explicit and simultaneous quantification over [20, 22, 25, 27], HyperMTL [11, 30], HyperMITL [31], HyperSTL multiple execution traces. We propose two different semantics for [38] specifications employing alternating automata, model checking, HyperTWTL, synchronous and asynchronous, based on the alignment strategy synthesis, and several other methods [32]. of the timestamps in the traces. Consequently, we demonstrate Time bounded specifications are common in many applications, the application of HyperTWTL in formalizing important such as robotics.

Customising AI technologies to each user's preferences is fundamental to them functioning well. Unfortunately, current methods require too much user involvement and fail to capture their true preferences. In fact, to avoid the nuisance of manually setting preferences, users usually accept the default settings even if these do not conform to their true preferences. Norms can be useful to regulate behaviour and ensure it adheres to user preferences but, while the literature has thoroughly studied norms, most proposals take a formal perspective. Indeed, while there has been some research on constructing norms to capture a user's privacy preferences, these methods rely on domain knowledge which, in the case of AI technologies, is difficult to obtain and maintain. We argue that a new perspective is required when constructing norms, which is to exploit the large amount of preference information readily available from whole systems of users. Inspired by recommender systems, we believe that collaborative filtering can offer a suitable approach to identifying a user's norm preferences without excessive user involvement.

President Joe Biden on Wednesday signed an executive order that will narrowly prohibit certain United States investments in sensitive technology in China and require government notification of funding in other tech sectors. Biden said in a letter to Congress he was declaring a national emergency to deal with the threat of advancement by countries like China "in sensitive technologies and products critical to the military, intelligence, surveillance, or cyber-enabled capabilities". The long-awaited order authorises the US treasury secretary to prohibit or restrict certain US investments in Chinese entities in three sectors: semiconductors and microelectronics, quantum information technologies, and certain artificial intelligence systems. Senior administration officials said that the effort stemmed from national security goals, rather than economic interests and that the categories it covered were narrow in scope. The order seeks to blunt China's ability to use US investments in its technology companies to upgrade its military while also preserving broader levels of trade that are vital for both nations' economies.

President Biden escalated his confrontation with China on Wednesday by signing an executive order banning American investments in key technology industries that could be used to enhance Beijing's military capabilities, the latest in a series of moves putting further distance between the world's two largest economies. The order will prohibit venture capital and private equity firms from pumping money into Chinese efforts to develop semiconductors and other microelectronics, quantum computers and certain artificial intelligence applications. Administration officials stressed that the move was tailored to guard national security, but China is likely to see it as part of a wider campaign to contain its rise. "The Biden administration is committed to keeping America safe and defending America's national security through appropriately protecting technologies that are critical to the next generation of military innovation," the Treasury Department said in a statement. The statement emphasized that the executive order was a "narrowly targeted action" complementing existing export controls and that the administration maintained its "longstanding commitment to open investment."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russia claims it shot down two drones attempting to attack targets in Moscow on Wednesday, the third such incident in recent months. Russia's Defense Ministry released a statement blaming the attack on Ukraine and framing the incident as an attempted "terrorist attack." Russian officials said the drones were destroyed without causing any damage or casualties.