Computer manufacturers offer platforms for users to describe device faults using textual reports such as "My screen is flickering". Identifying the faulty component from the report is essential for automating tests and improving user experience. However, such reports are often ambiguous and lack detail, making this task challenging. Large Language Models (LLMs) have shown promise in addressing such issues. This study evaluates 27 open-source models (1B-72B parameters) and 2 proprietary LLMs using four prompting strategies: Zero-Shot, Few-Shot, Chain-of-Thought (CoT), and CoT+Few-Shot (CoT+FS). W e conducted 98,948 inferences, processing over 51 million input tokens and generating 13 million output tokens. W e achieve f1-score up to 0.76. Results show that three models offer the best balance between size and performance: mistral-small-24b-instruct and two smaller models, llama-3.2-1b-instruct

Computer manufacturers typically offer platforms for users to report faults. However, there remains a significant gap in these platforms' ability to effectively utilize textual reports, which impedes users from describing their issues in their own words. In this context, Natural Language Processing (NLP) offers a promising solution, by enabling the analysis of user-generated text. This paper presents an innovative approach that employs NLP models to classify user reports for detecting faulty computer components, such as CPU, memory, motherboard, video card, and more. In this work, we build a dataset of 341 user reports obtained from many sources. Additionally, through extensive experimental evaluation, our approach achieved an accuracy of 79% with our dataset.

Users experimenting with DeepSeek have seen the Chinese AI chatbot reply and then censor itself in real time, providing an arresting insight into its control of information and opinion. Users might expect censorship to happen behind closed doors, before any information is shared. But that does not seem to be the case in the tool that sent US technology stocks tumbling on Monday. DeepSeek, or the automated guardrails that appear to police its own freedom of "thought" and "speech", brazenly deletes uncomfortable points. Before the censor's cut comes, DeepSeek seems remarkably thoughtful. In Mexico, Guardian reader Salvador asked it on Tuesday if free speech was a legitimate right in China.

ChatGPT is down across the US as users report seeing a'bad gateway' error message when using the AI tool. Downdetector, a site that monitors online outages, shows issues hit the OpenAI-owned platform around 7am ET. The error message indicates that one server received an invalid response from another, creating a communication breakdown. Many users have shared their frustrations on X, saying they'feel lost without it.' 'Seriously, how am I supposed to brainstorm, write, and research without my AI assistant,' an X user posted.

Accurate weather forecasting plays a vital role in saving lives, aiding emergency management, and reducing the economic impact of severe weather events [Bauer et al., 2015]. The traditional paradigm of weather forecasting is numerical weather prediction (NWP), which focuses on nonlinear partial differential equations to simulate atmospheric dynamics and physical processes [Benjamin et al., 2019]. In recent years, with the advancement of artificial intelligence (AI) technology and the continuous accumulation of massive weather data, data-driven methods have been increasingly incorporated into various stages and different scales of weather forecasting [Ravuri et al., 2021, Schultz et al., 2021, Weyn et al., 2021]. Particularly in the past two years, numerous data-driven models addressing the short to mediumrange (0-10 day) forecasting problem have emerged [Bi et al., 2023, Lam et al., 2023, Chen et al., 2023, Lang et al., 2024]. These models have surpassed the operational Integrated Forecast System (IFS) from European Centre for Medium-Range Weather Forecasts (ECMWF) in metrics such as Root Mean Square Error (RMSE) and Anomaly Correlation Coefficient (ACC). These breakthroughs have instilled confidence that data-driven models can be significant tools for enhancing the accuracy and computational efficiency of weather forecasting.

The convenient robotic vacuums got a software update, and now users are saying the device is hitting furniture and struggling to find charging stations.

Facebook rushed to pull down footage of the New Zealand mass shooter's video from its platform, but it didn't start doing so until after the live broadcast was done. In a new post, Facebook VP of Integrity Guy Rosen discussed the company's successes and shortcomings in addressing the situation, as well as its plans to prevent videos like that from spreading on the social network in the future. He explained that while the platform's AI can quickly detect videos containing suicidal or harmful acts, the shooter's stream didn't trigger it. To be able to train the matching AI to detect that specific type of content, the platform needs big volumes of training data. As Facebook explains, something like that is difficult to obtain as "these events are thankfully rare."

This is software to save lives. Facebook's new "proactive detection" artificial intelligence technology will scan all posts for patterns of suicidal thoughts, and when necessary send mental health resources to the user at risk or their friends, or contact local first-responders. By using AI to flag worrisome posts to human moderators instead of waiting for user reports, Facebook can decrease how long it takes to send help. Facebook previously tested using AI to detect troubling posts and more prominently surface suicide reporting options to friends in the U.S. Now Facebook is will scour all types of content around the world with this AI, except in the European Union, where General Data Protection Regulation privacy laws on profiling users based on sensitive information complicate the use of this tech. Facebook also will use AI to prioritize particularly risky or urgent user reports so they're more quickly addressed by moderators, and tools to instantly surface local language resources and first-responder contact info.

The rise in consumer usage of ad blockers is leading to a few creative alternatives to try to achieve a'better relationship' between ad tech and web browsers. To wit Russia's Yandex, which has just announced it's adding a complaint button to its Android browser to lets users report ads they find annoying. Filing an ad complaint will send a report to Yandex which will initiate custom ad filtering for that user, using machine learning technology to hone the individual model over time. It will also be feeding intel back to advertisers so they can "create more targeted and effective campaigns that are relevant to users, reducing the need to install ad blocking software". So in theory users making use of the ad complaint button should see ads more pleasing/relevant to them over time, as well as eyeballing fewer ads they find annoying.