Time series data measure how environments change over time and drive decision-making in critical domains like finance and healthcare. When analyzing time series, we often seek to understand the underlying events occurring in the measured environment. For example, one might ask: What caused a sharp drop in the stock price? Events are often described with natural language, so we conduct the first study of whether Large Language Models (LLMs) can infer natural language events from time series. We curate a new benchmark featuring win probabilities collected from 4,200 basketball and American football games, featuring 1.7M timesteps with real value data and corresponding natural language events. Building on the recent wave of using LLMs on time series, we evaluate 16 LLMs and find that they demonstrate promising abilities to infer events from time series data. The open-weights DeepSeek-R1 32B model outperforms proprietary models like GPT-4o. Despite this impressive initial performance, we also find clear avenues to improve recent models, as we identify failures when altering the provided context, event sequence lengths, and evaluation strategy. (All resources needed to reproduce our work are available: https://github.com/BennyTMT/GAMETime)

Deploying large scale language models on edge devices faces inherent challenges such as high computational demands, energy consumption, and potential data privacy risks. This paper introduces the Shakti Small Language Models (SLMs) Shakti-100M, Shakti-250M, and Shakti-500M which target these constraints headon. By combining efficient architectures, quantization techniques, and responsible AI principles, the Shakti series enables on-device intelligence for smartphones, smart appliances, IoT systems, and beyond. We provide comprehensive insights into their design philosophy, training pipelines, and benchmark performance on both general tasks (e.g., MMLU, Hellaswag) and specialized domains (healthcare, finance, and legal). Our findings illustrate that compact models, when carefully engineered and fine-tuned, can meet and often exceed expectations in real-world edge-AI scenarios.

With the development of artificial intelligence, neural networks play a key role in network intrusion detection systems (NIDS). Despite the tremendous advantages, neural networks are susceptible to adversarial attacks. To improve the reliability of NIDS, many research has been conducted and plenty of solutions have been proposed. However, the existing solutions rarely consider the adversarial attacks against recurrent neural networks (RNN) with time steps, which would greatly affect the application of NIDS in real world. Therefore, we first propose a novel RNN adversarial attack model based on feature reconstruction called \textbf{T}emporal adversarial \textbf{E}xamples \textbf{A}ttack \textbf{M}odel \textbf{(TEAM)}, which applied to time series data and reveals the potential connection between adversarial and time steps in RNN. That is, the past adversarial examples within the same time steps can trigger further attacks on current or future original examples. Moreover, TEAM leverages Time Dilation (TD) to effectively mitigates the effect of temporal among adversarial examples within the same time steps. Experimental results show that in most attack categories, TEAM improves the misjudgment rate of NIDS on both black and white boxes, making the misjudgment rate reach more than 96.68%. Meanwhile, the maximum increase in the misjudgment rate of the NIDS for subsequent original samples exceeds 95.57%.

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.

There, top hackers from around the globe will rack up points for inducing AI models to err in various ways, with categories of challenges that include political misinformation, defamatory claims, and "algorithmic discrimination," or systemic bias. Leading AI firms such as Google, OpenAI, Anthropic and Stability have volunteered their latest chatbots and image generators to be put to the test. The competition's results will be sealed for several months afterward, organizers said, to give the companies time to address the flaws exposed in the contest before they are revealed to the world.

The AI-powered Microsoft 365 Copilot could allow you to skip or even double-book meetings without missing out on what was discussed, "hopscotch" through priority email, and more. Microsoft 365 Copilot, announced in March, unfortunately remains in preview. Microsoft is confident enough of what it can do, though, that it said today that it's charging 600 worldwide customers to try it out as part of a Microsoft 365 Copilot Early Access Pass. In March, corporate vice president Jared Spataro said that Copilot would come to basically all Microsoft 365 apps: Word, PowerPoint, Excel, Teams and more. The company released a number of video demonstrations of how Microsoft 365 Copilot will work in its various apps.

We are a growing, dynamic computer software company that helps businesses achieve greater levels of financial intelligence across their organization with our world-class financial reporting solutions. At insightsoftware, you will learn and grow in a fast-paced, supportive environment that will take your career to the next level. We are looking for future insighters who can demonstrate teamwork, results orientation, a growth mindset, disciplined execution, and a winning attitude to join our growing team! Insightsoftware celebrates diversity and is proud to have an open and inclusive environment where our rapidly expanding family of 2400 associates feel they belong, and all voices are heard. Account Executive to focus on new business for a fast-growth global software provider ($1bn PE funding & 20 companies acquired since 2018).

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Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. At Weights & Biases, our mission is to build the best developer tools for machine learning. Weights & Biases is a series C company with $200 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more. Reporting to the Head of Data Science, the Machine Learning Engineer (MLE) will own the interface between our Data Science Team and our Data Platform Team, while making the results of Data Science into ML Applications for the business.

At Microsoft, we believe that energized, empowered employees are the key to a durable, competitive advantage for every organization. The Microsoft Work Trend Index shows that leaders today need to end productivity paranoia, embrace the fact that people come into the office for each other, and re-recruit everyone.1 Empowering today's digitally connected, distributed workforce requires the right culture and the right technology. At Microsoft Ignite, we're sharing new innovations across Microsoft 365, Microsoft Teams, and Microsoft Viva to help everyone thrive. Global experiences, localized content, in-person opportunities, and more--let's get ready for a new kind of Microsoft Ignite. Microsoft 365 is the cloud-first platform for all the ways that people work today--wherever, whenever, however.