Scientific English has undergone rapid and unprecedented changes in recent years, with words such as "delve," "intricate," and "crucial" showing significant spikes in frequency since around 2022. These changes are widely attributed to the growing influence of Large Language Models like ChatGPT in the discourse surrounding bias and misalignment. However, apart from changes in frequency, the exact structure of these linguistic shifts has remained unclear. The present study addresses this and investigates whether these changes involve the replacement of synonyms by suddenly 'spiking words,' for example, "crucial" replacing "essential" and "key," or whether they reflect broader semantic and pragmatic qualifications. To further investigate structural changes, we include part of speech tagging in our analysis to quantify linguistic shifts over grammatical categories and differentiate between word forms, like "potential" as a noun vs. as an adjective. We systematically analyze synonym groups for widely discussed 'spiking words' based on frequency trends in scientific abstracts from PubMed. We find that entire semantic clusters often shift together, with most or all words in a group increasing in usage. This pattern suggests that changes induced by Large Language Models are primarily semantic and pragmatic rather than purely lexical. Notably, the adjective "important" shows a significant decline, which prompted us to systematically analyze decreasing lexical items. Our analysis of "collapsing" words reveals a more complex picture, which is consistent with organic language change and contrasts with the patterns of the abrupt spikes. These insights into the structure of language change contribute to our understanding of how language technology continues to shape human language.

Elon Musk's so-called Department of Government Efficiency (DOGE) used artificial intelligence from Meta's Llama model to comb through and analyze emails from federal workers. Materials viewed by WIRED show that DOGE affiliates within the Office of Personnel Management (OPM) tested and used Meta's Llama 2 model to review and classify responses from federal workers to the infamous "Fork in the Road" email that was sent across the government in late January. The email offered deferred resignation to anyone opposed to changes the Trump administration was making to its federal workforce, including an enforced return to office policy, downsizing, and a requirement to be "loyal." To leave their position, recipients merely needed to reply with the word "resign." This email closely mirrored one that Musk sent to Twitter employees shortly after he took over the company in 2022.

Engineers for Elon Musk's so-called Department of Government Efficiency, or DOGE, are working on new software that could assist mass firings of federal workers across government, sources tell WIRED. The software, called AutoRIF, which stands for Automated Reduction in Force, was first developed by the Department of Defense more than two decades ago. Since then, it's been updated several times and used by a variety of agencies to expedite reductions in workforce. Screenshots of internal databases reviewed by WIRED show that DOGE operatives have accessed AutoRIF and appear to be editing its code. There is a repository in the Office of Personnel Management's (OPM) enterprise GitHub system titled "autorif" in a space created specifically for the director's office--where Musk associates have taken charge--soon after Trump took office.

Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data-driven healthcare. We show however, that using prediction models for decision making can lead to harmful decisions, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients but the worse outcome of these patients does not invalidate the predictive power of the model. Our main result is a formal characterization of a set of such prediction models. Next we show that models that are well calibrated before and after deployment are useless for decision making as they made no change in the data distribution. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models that are used in medical decisions.

The evolution of cloud computing and artificial intelligence are growing in parallel, and the complexity of the cloud is driving the need for AI. In addition, the complexity of AI is also creating the need for it to work better in the cloud environment with efficiency, transparency and control. Organizations are taking a more data-driven approach, where artificial intelligence can be used to detect and proactively provide alerts on weaknesses and vulnerabilities both that are being exploited right now, or that might be exploited in the future. This is being done by analyzing data coming in and out of protected endpoints, both detecting threats based on known behavior, and spotting yet known threats based on predictive analytics. Join this interactive presentation and be sure to stick around for Q&A with Melvin!

Data is becoming an important central point for making design decisions for most software. Game development is not an exception. As data-driven methods and systems start to populate these environments, a good question is: can we make models developed from this data transparent to users? In this paper, we synthesize existing work from the Intelligent User Interface and Learning Science research communities, where they started to investigate the potential of making such data and models available to users. We then present a new area exploring this question, which we call Open Player Modeling, as an emerging research area. We define the design space of Open Player Models and present exciting open problems that the games research community can explore. We conclude the paper with a case study and discuss the potential value of this approach.

Many important optimization problems, such as the minimum spanning tree and minimum-cost flow, can be solved optimally by a greedy method. In this work, we study a learning variant of these problems, where the model of the problem is unknown and has to be learned by interacting repeatedly with the environment in the bandit setting. We formalize our learning problem quite generally, as learning how to maximize an unknown modular function on a known polymatroid. We propose a computationally efficient algorithm for solving our problem and bound its expected cumulative regret. Our gap-dependent upper bound is tight up to a constant and our gap-free upper bound is tight up to polylogarithmic factors. Finally, we evaluate our method on three problems and demonstrate that it is practical.

In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

We also require a generic model of provenance The Web has evolved from a collection of hyperlinked documents in order to support the diverse ecosystem of sensor to a complex ecosystem of interconnected documents, platforms and data. We have investigated a number of existing services and devices. Due to the inherent open nature of the models for representing provenance information but Web, data can be published by anyone or any'thing'. As a found many of these to be tailored to specific domains result of this, there is enormous variation in the quality of (e.g.