In the Swiss Alps, a plan to tidy up Romansh--spoken by less than one per cent of the country--set off a decades-long quarrel over identity, belonging, and the sound of authenticity. After reformers launched Rumantsch Grischun, a standardized version of Romansh's various dialects, traditionalists denounced it as a "bastard," a "castrated" tongue, an act of "linguistic murder." Ask him how it all began, and he remembers the ice. It was a bitter morning in January, 1982, when Bernard Cathomas, aged thirty-six, carefully picked his way up a slippery, sloping Zurich street. His destination was No. 33, an ochre house with green shutters--the home of Heinrich Schmid, a linguist at the University of Zurich. Inside, the décor suggested that "professor" was an encompassing identity: old wooden floors, a faded carpet, a living room seemingly untouched since the nineteen-thirties, when Schmid had grown up in the house. Schmid's wife served, a Swiss carrot cake that manages bourgeois indulgence with a vegetable alibi. Cathomas had already written from Chur, in the canton of the Grisons, having recently become the general secretary of the Lia Rumantscha, a small association charged with protecting Switzerland's least known national language, Romansh. Spoken by less than one per cent of the Swiss population, the language was itself splintered into five major "idioms," not always readily intelligible to one another, each with its own spelling conventions. Earlier attempts at unification had collapsed in rivalries. In his letter, Cathomas said that Schmid's authority would be valuable in standardizing the language. Cathomas wrote in German but started and ended in his native Sursilvan, the biggest of the Romansh idioms: " ." Translation: "I thank you very much for your interest and attention to this problem." Schmid, the man he was counting on, hadn't grown up speaking Romansh; he first learned it in high school, and later worked on the "Dicziunari Rumantsch Grischun," a Romansh dictionary begun in 1904 and still lumbering toward completion.

In this study, we first tested the performance of the TreeTagger English model developed by Helmut Schmid with test files at our disposal, using this model to analyze relative clauses and noun complement clauses in English. We distinguished between the two uses of "that," both as a relative pronoun and as a complementizer. To achieve this, we employed an algorithm to reannotate a corpus that had originally been parsed using the Universal Dependency framework with the EWT Treebank. In the next phase, we proposed an improved model by retraining TreeTagger and compared the newly trained model with Schmid's baseline model. This process allowed us to fine-tune the model's performance to more accurately capture the subtle distinctions in the use of "that" as a complementizer and as a nominal. We also examined the impact of varying the training dataset size on TreeTagger's accuracy and assessed the representativeness of the EWT Treebank files for the structures under investigation. Additionally, we analyzed some of the linguistic and structural factors influencing the ability to effectively learn this distinction.

Random Forests have become a widely used tool in machine learning since their introduction in 2001, known for their strong performance in classification and regression tasks. One key feature of Random Forests is the Random Forest Permutation Importance Measure (RFPIM), an internal, non-parametric measure of variable importance. While widely used, theoretical work on RFPIM is sparse, and most research has focused on empirical findings. However, recent progress has been made, such as establishing consistency of the RFPIM, although a mathematical analysis of its asymptotic distribution is still missing. In this paper, we provide a formal proof of a Central Limit Theorem for RFPIM using U-Statistics theory. Our approach deviates from the conventional Random Forest model by assuming a random number of trees and imposing conditions on the regression functions and error terms, which must be bounded and additive, respectively. Our result aims at improving the theoretical understanding of RFPIM rather than conducting comprehensive hypothesis testing. However, our contributions provide a solid foundation and demonstrate the potential for future work to extend to practical applications which we also highlight with a small simulation study.

The small, unadorned church has long ranked as the oldest in the Swiss city of Lucerne. But Peter's chapel has become synonymous with all that is new after it installed an artificial intelligence-powered Jesus capable of dialoguing in 100 different languages. "It was really an experiment," said Marco Schmid, a theologian with the church. "We wanted to see and understand how people react to an AI Jesus. What would they talk with him about? Would there be interest in talking to him? The installation, known as Deus in Machina, was launched in August as the latest initiative in a years-long collaboration with a local university research lab on immersive reality. After projects that had experimented with virtual and augmented reality, the church decided that the next step was to install an avatar. Schmid said: "We had a discussion about what kind of avatar it would be – a theologian, a person or a saint?

Some modern technologies may seem miraculous, but never has that been quite so literal. Thanks to technological advances, worshipers at a church in Switzerland can now speak directly to Jesus - or at least an AI version of him. As part of an art project called'Deus in Machina' (God in a Machine) St Peter's Church in Lucerne has installed an AI-powered Jesus hologram to take confessions. Worshipers simply voice their concerns and questions to get a response from the digitally-rendered face of Jesus Christ. At least two-thirds of people who spoke to AI Jesus came out of the confessional reporting having had a'spiritual' experience.

Fueled by advances in artificial intelligence, robotic process automation is impacting virtually every sector of the economy (McKinsey Global Institute 2017). The logistics sector lies at the core of this transformation: autonomous mobile robots are being deployed in tens of thousands of manufacturing and distribution facilities with a near-term $10-50 billion market potential (Grand View Research 2021, ABI Research 2021). A predominant operating model, shown in Figure 1, involves part-to-picker warehousing operations, which relies on robotic agents transporting shelves of inventory from a storage location to a workstation for a human operator to fulfill orders and back to a storage location. Robotic operations can improve throughput and working conditions by letting human workers focus on the more productive tasks, while improving system reliability. Yet, to truly take advantage of automation opportunities, modern warehousing systems require dedicated decision support tools to manage large robotic fleets and human-robot interactions in high-density operations. At the core of robotic process automation lies the computer vision, sensing, mapping and robotic technologies to empower autonomous agents--in our case, robots capable to move shelves of inventory. A subsequent problem involves control mechanisms to coordinate multiagent systems--in our case, to avoid conflicts and collisions between robots.

Epistemic analysis of distributed systems is one of the biggest successes among applications of logic in computer science. The reason for that is that agents' actions are necessarily guided by their knowledge. Thus, epistemic modal logic, with its knowledge and belief modalities (and group versions thereof), has played a vital role in establishing both impossibility results and necessary conditions for solvable distributed tasks. In distributed systems, knowledge is largely attained via communication. It has been standard in both distributed systems and dynamic epistemic logic to treat incoming messages as trustworthy, thus, creating difficulties in the epistemic analysis of byzantine distributed systems where faulty agents may lie. In this paper, we argue that handling such communication scenarios calls for additional modalities representing the informational content of messages that should not be taken at face value. We present two such modalities: hope for the case of fully byzantine agents and creed for non-uniform communication protocols in general.

Shall we play a game? A single artificial intelligence can beat human players in chess, Go, poker and other games that require a variety of strategies to win. The AI, called Student of Games, was created by Google DeepMind, which says it is a step towards an artificial general intelligence capable of carrying out any task with superhuman performance. Martin Schmid, who worked at DeepMind on the AI but who is now at a start-up called EquiLibre Technologies, says that the Student of Games (SoG) model can trace its lineage back to two projects. One was DeepStack, the AI created by a team including Schmid at the University of Alberta in Canada and which was the first to beat human professional players at poker.

Multi-armed bandits are extensively used to model sequential decision-making, making them ubiquitous in many real-life applications such as online recommender systems and wireless networking. We consider a multi-agent setting where each agent solves their own bandit instance endowed with a different set of arms. Their goal is to minimize their group regret while collaborating via some communication protocol over a given network. Previous literature on this problem only considered arm heterogeneity and networked agents separately. In this work, we introduce a setting that encompasses both features. For this novel setting, we first provide a rigorous regret analysis for a standard flooding protocol combined with the classic UCB policy. Then, to mitigate the issue of high communication costs incurred by flooding in complex networks, we propose a new protocol called Flooding with Absorption (FwA). We provide a theoretical analysis of the resulting regret bound and discuss the advantages of using FwA over flooding. Lastly, we experimentally verify on various scenarios, including dynamic networks, that FwA leads to significantly lower communication costs despite minimal regret performance loss compared to other network protocols.

Artificial intelligence (AI) could soon offer more help to those with speech disabilities. Big tech companies are partnering with the University of Illinois to form the Speech Accessibility Project to upgrade AI's understanding of people with disabilities or unusual speech patterns. The project will gather a set of high-quality, diverse speech samples that will help improve speech technologies. "Being able to devise new interventions and screening tools will help us be more proactive in early detection of conditions in children and help us customize more specific therapies for a patient's condition," Karen Panetta, a professor of electrical and computer engineering at Tufts University and an IEEE Fellow, who is not involved in the project, told Lifewire in an email interview. Speech recognition, found in many software programs and voice assistants, has become a part of many people's everyday lives.