Typical marine environments are highly complex with spatio-temporally varying currents and dynamic obstacles, presenting significant challenges to Unmanned Surface Vehicles (USVs) for safe and efficient navigation. Thus, the USVs need to continuously adapt their paths with real-time information to avoid collisions and follow the path of least resistance to the goal via exploiting ocean currents. In this regard, we introduce a novel algorithm, called Self-Morphing Adaptive Replanning Tree for dynamic Obstacles and Currents (SMART-OC), that facilitates real-time time-risk optimal replanning in dynamic environments. SMART-OC integrates the obstacle risks along a path with the time cost to reach the goal to find the time-risk optimal path. The effectiveness of SMART-OC is validated by simulation experiments, which demonstrate that the USV performs fast replannings to avoid dynamic obstacles and exploit ocean currents to successfully reach the goal.

We argue that Large language models (LLMs) will soon alter the economics of cyberattacks. Instead of attacking the most commonly used software and monetizing exploits by targeting the lowest common denominator among victims, LLMs enable adversaries to launch tailored attacks on a user-by-user basis. On the exploitation front, instead of human attackers manually searching for one difficult-to-identify bug in a product with millions of users, LLMs can find thousands of easy-to-identify bugs in products with thousands of users. And on the monetization front, instead of generic ransomware that always performs the same attack (encrypt all your data and request payment to decrypt), an LLM-driven ransomware attack could tailor the ransom demand based on the particular content of each exploited device. We show that these two attacks (and several others) are imminently practical using state-of-the-art LLMs. For example, we show that without any human intervention, an LLM finds highly sensitive personal information in the Enron email dataset (e.g., an executive having an affair with another employee) that could be used for blackmail. While some of our attacks are still too expensive to scale widely today, the incentives to implement these attacks will only increase as LLMs get cheaper. Thus, we argue that LLMs create a need for new defense-in-depth approaches.

In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. We extended the functionality of LIMA in terms of the number of supported languages while preserving existing configurable architecture and the availability of previously developed rule-based and statistical analysis components. Models were trained for more than 60 languages on the Universal Dependencies 2.5 corpora, WikiNer corpora, and CoNLL-03 dataset. Universal Dependencies allowed us to increase the number of supported languages and to generate models that could be integrated into other platforms. This integration of ubiquitous Deep Learning Natural Language Processing models and the use of standard annotated collections using Universal Dependencies can be viewed as a new path of interoperability, through the normalization of models and data, that are complementary to a more standard technical interoperability, implemented in LIMA through services available in Docker containers on Docker Hub.

It is easy to overlook the role lithography plays in developing digital technologies. Every year, new and more advanced integrated circuits appear, pushing computing capabilities to more advanced levels. Typically, the focus is on what these devices can do, in areas like supercomputing, artificial intelligence (AI), and wireless communications, for example. Yet, behind the curtain is the technology that makes all of this possible. Lithography--which at its most basic level refers to a photography-like process that uses light to imprint images on a suitable substrate, such as a thin film--is at the foundation of modern computing and electronics.

In this paper, a method for generating a map from path information described using natural language (textual path) is proposed. In recent years, robotics research mainly focus on vision-and-language navigation (VLN), a navigation task based on images and textual paths. Although VLN is expected to facilitate user instructions to robots, its current implementation requires users to explain the details of the path for each navigation session, which results in high explanation costs for users. To solve this problem, we proposed a method that creates a map as a topological map from a textual path and automatically creates a new path using this map. We believe that large language models (LLMs) can be used to understand textual path. Therefore, we propose and evaluate two methods, one for storing implicit maps in LLMs, and the other for generating explicit maps using LLMs. The implicit map is in the LLM's memory. It is created using prompts. In the explicit map, a topological map composed of nodes and edges is constructed and the actions at each node are stored. This makes it possible to estimate the path and actions at waypoints on an undescribed path, if enough information is available. Experimental results on path instructions generated in a real environment demonstrate that generating explicit maps achieves significantly higher accuracy than storing implicit maps in the LLMs.

A company called Sonantic has created what it claims is the first artificial intelligence voice models that sound genuinely human and capable of expressing "a wide range of complex human emotions, from fear and sadness to joy and surprise." Related Story Actors interested in creating voice models spend a few hours in the studio recording various lines of dialogue unrelated to clients' scripts. Sonantic records the actors' performances and utilizes proprietary deep learning algorithms to augment the data captured from their voices to create voice models. The AI has been trained to match any voice style, so that it doesn't need an actor to record every word or phrase. The actors can thereafter generate passive income every time their voice models are used.

Multi-agent path finding (MAPF) is an active area in artificial intelligence, which has many real-world applications such as warehouse management, traffic control, robotics, etc. Recently, M* and its variants have greatly improved the ability to solve the MAPF problem. Although subdimensional expansion used in those approaches significantly decreases the dimensionality of the joint search space and reduces the branching factor, they do not make full use of the possible non-uniqueness of the optimal path of each agent. As a result, the updating of the collision sets may bring a large number of redundant computation. In this paper, the idea of bypass is introduced into subdimensional expansion to reduce the redundant computation. Specifically, we propose the BPM* algorithm, which is an implementation of subdimensional expansion with bypass in M*. In the experiments, we show that BPM* outperforms the state-of-the-art in solving several MAPF benchmark problems.

Pogrebna is a pioneer in behavioural data science – a field that combines behavioural science and data science techniques to better understand, model and predict the behaviour of humans, algorithms and complex systems in the face of risk and uncertainty. "I warmly welcome Professor Pogrebna to the University and look forward to working with her to establish the Cybersecurity and Data Science Institute," Charles Sturt pro vice-chancellor (Research and Innovation) Professor Mark Evans said. "Professor Pogrebna comes to the University with a wealth of experience. This includes being the Lead of the Behavioural Data Science strand at The Alan Turing Institute – the national centre for AI and data science in London where she is also a Fellow working on hybrid modelling approaches between behavioural science and data science (e.g. According to Evans, Pogrebna helps leaders in businesses, charities and the public sector better understand why they make the decisions they make and how they can optimise their behaviour to achieve higher profit, better social and commercial outcomes, and bolster the wellbeing of their teams. Pogrebna said her goal as executive director of the Cybersecurity and Data Science Institute is to avoid'building a silo'. "I have spent several weeks visiting our researchers across the University's campuses and became aware of the incredible work they are doing," she said. "The new Institute will aim to support our local talent and build on it, seeing how we can develop new research collaborations in Australia as well as internationally.

In the Zagar & Evans pop song classic In the Year 2525, the duo sang of a year where "some machine is doing that for you." A company called Sonantic has created what it claims is the first artificial intelligence voice models that sound genuinely human and capable of expressing "a wide range of complex human emotions, from fear and sadness to joy and surprise." Gaming producers like Obsidian Entertainment, Splash Damage and 4A Games are already on board with the technology, which is in use from development through post-production. The concept is similar to CGI for audio. Human, realistic voice tech is in its infancy, but along with this brave new frontier is a new opportunity for voiceover artists – banking their words for future use.

SparkBeyond, an AI-powered problem-solving platform that augments and accelerates the generation of novel insights out of data and knowledge, alongside Baker McKenzie, a leading multinational law firm, announced a market-first collaboration that will apply SparkBeyond's technology to reimagine legal client services in the future. Baker McKenzie will launch its new global innovation arm, Reinvent, and apply SparkBeyond's AI to predict which services clients will require from law firms, explore unforeseen drivers of client demand and learn how to evolve its business to accommodate those needs. The partnership also aims to reimagine traditional law practices and pave new paths for tens of thousands of firms across the globe. "The legal sector is on a new path for disruption and innovation and our partnership with SparkBeyond will turbo-charge the evolution in our business," said Ben Allgrove, Partner at Baker McKenzie. "Understanding the drivers and root causes driving future client demand will allow us unparalleled insights and the ability to shape the future of our business to create additional value across the legal, tax, and compliance functions in the future."