Googles Pixel 4a has long been considered a great smartphone for those on a budget, but it just received a software update that calls that into question. The update lowers the reported battery life. This isnt a side-effect of some new software. This is the actual intent of the refresh.Wait, what? Google says the automatic software update to Android 13 will reduce your battery's runtime and charging performance but that its necessary to improve the stability of each device. Thats the only explanation the company offered. We reached out to Google to ask for a specific reason as to why this was necessary.Any other Google Pixel 4a users get an email about a battery update with Android 13 that may affect your phone's battery? I'm reading this and like...what Alexis Collins (@Lexiscee) January 7, 2025 Theres a silver lining here for current Pixel 4a owners. While every Pixel 4a will receive the automatic update, only certain devices will see a reduction in battery life and charging performance. Theres no information as to what designates which handsets will suffer as a result of the update, but owners of so-called impacted devices will have a few appeasement options to choose from.Folks can send the phone in for a free battery replacement, but that will require the owner to go without a handset while Google performs the replacement. If thats not viable, the company will send impacted owners $50 or give them a $100 credit toward a new Pixel phone from the Google Store. Pixel 4a owners have one year to choose one of these options.Its likely theres nothing nefarious going on and that Pixel 4a batteries are simply getting old, being as how the phone was released in 2020. This means they may not provide all of the power demanded by the current OS. The software update could be intended to prevent unexpected behavior or shutdowns and the reduction in battery life is an unfortunate side effect.This whole thing sounds suspiciously similar to when Apple started slowing down older iPhones in 2017. However, Apple wasnt forthright with consumers during that whole fiasco, leading to court cases and the like. At least Google seems to be getting ahead of things here, even if it could stand to be a bit more transparent.This article originally appeared on Engadget at https://www.engadget.com/big-tech/google-pixel-4as-update-kills-its-battery-life-on-purpose-164532917.html?src=rss

The increasing cybersecurity and governance risks facing organizations as a result of Shadow AI.

Government agency addS three new flaws to its KEV catalog, signaling in-the-wild abuse.

The Nintendo Switch 2 logo may have leaked, and I think it's pretty bland.

When you do your own taxes with the help of H&R Block software (or a human, if you need it), you can get 15% off.

MacworldBetween Dropbox, iCloud, and Google Drive, cloud storage often feels like just another bill to pay every month. But it doesnt have to be. Internxt offers a secure cloud storage solution with a lifetime plan, no recurring payments, and no strings attached. Right now, you can get 10TB of storage for a one-time payment of $279.99 (regularly $2,999).Dropbox is doneInternxt puts your privacy front and center with end-to-end encryption. Nobody can access your files without your permission, but if you do want to share, its really easy.You can access your files on virtually any modern device, whether youre on your Macbook, a PC, iOS, Android, or Linux. Prefer an app? Theyve got you covered. Like using a browser? That works too.For those in regions with strict data protection laws, Internxts GDPR compliance adds an extra layer of reassurance.Internxt is the perfect example of the buy once, cry once principle. Yeah, its more expensive than a month of DropBox, but its a whole lot cheaper in the long run.Use code HOLIDAY20 by January 12 to get a 10TB Internxt lifetime subscription on sale for $279.99.Internxt Cloud Storage Lifetime Subscription: 10TB Plan $279.99See DealStackSocial prices subject to change.

MacworldWhen Apple officially announced Mac OS X, it changed the course of history for the company. On this episode of the Macworld Podcast, well look at the history of Mac OS X, the effect it has had on the company, and what we expect in the future.This is episode 916 with Jason Cross, Michael Simon, and Roman Loyola.Watch episode 916 on YouTubeListen to episode 916 on Apple PodcastsListen to episode 916 on SpotifyGet infoFor more information about the topics discussed on the show, click on the links below.Looking back 25 years later, even Steve was wrong about Mac OS XFrom Aqua to Catalina: The evolution of macOS XSubscribe to the Macworld PodcastYou can subscribe to the Macworld Podcastor leave us a review!right here in the Podcasts app. The Macworld Podcast is also available on Spotify and on the Macworld Podcast YouTube channel. Or you can point your favorite podcast-savvy RSS reader at: https://feeds.megaphone.fm/macworldTo find previous episodes, visit Macworlds podcast page or our home on MegaPhone.Apple

Scientific research is often constrained by resource limitations and time-intensive processes. Tasks such as hypothesis testing, data analysis, and report writing demand significant effort, leaving little room for exploring multiple ideas simultaneously. The increasing complexity of research topics further compounds these issues, requiring a blend of domain expertise and technical skills that may not always be readily available. While AI technologies have shown promise in alleviating some of these burdens, they often lack integration and fail to address the entire research lifecycle in a cohesive manner.In response to these challenges, researchers from AMD and John Hopkins have developed Agent Laboratory, an autonomous framework designed to assist scientists in navigating the research process from start to finish. This innovative system employs large language models (LLMs) to streamline key stages of research, including literature review, experimentation, and report writing.Agent Laboratory comprises a pipeline of specialized agents tailored to specific research tasks. PhD agents handle literature reviews, ML Engineer agents focus on experimentation, and Professor agents compile findings into academic reports. Importantly, the framework allows for varying levels of human involvement, enabling users to guide the process and ensure outcomes align with their objectives. By leveraging advanced LLMs like o1-preview, Agent Laboratory offers a practical tool for researchers seeking to optimize both efficiency and cost.Technical Approach and Key BenefitsAgent Laboratorys workflow is structured around three primary components:Literature Review: The system retrieves and curates relevant research papers using resources like arXiv. Through iterative refinement, it builds a high-quality reference base to support subsequent stages.Experimentation: The mle-solver module autonomously generates, tests, and refines machine learning code. Its workflow includes command execution, error handling, and iterative improvements to ensure reliable outcomes.Report Writing: The paper-solver module generates academic reports in LaTeX format, adhering to established structures. This phase includes iterative editing and feedback integration to enhance clarity and coherence.The framework offers several benefits:Efficiency: By automating repetitive tasks, Agent Laboratory reduces research costs by up to 84% and shortens project timelines.Flexibility: Researchers can choose their level of involvement, maintaining control over critical decisions.Scalability: Automation frees up time for high-level planning and ideation, enabling researchers to manage larger workloads.Reliability: Performance benchmarks like MLE-Bench highlight the systems ability to deliver dependable results across diverse tasks.Evaluation and FindingsThe utility of Agent Laboratory has been validated through extensive testing. Papers generated using the o1-preview backend consistently scored high in usefulness and report quality, while o1-mini demonstrated strong experimental reliability. The frameworks co-pilot mode, which integrates user feedback, was especially effective in producing impactful research outputs.Runtime and cost analyses revealed that the GPT-4o backend was the most cost-efficient, completing projects for as little as $2.33. However, the o1-preview achieved a higher success rate of 95.7% across all tasks. On MLE-Bench, Agent Laboratorys mle-solver outperformed competitors, earning multiple medals and surpassing human baselines on several challenges.ConclusionAgent Laboratory offers a thoughtful approach to addressing the bottlenecks in modern research workflows. By automating routine tasks and enhancing human-AI collaboration, it allows researchers to focus on innovation and critical thinking. While the system has limitationsincluding occasional inaccuracies and challenges with automated evaluationit provides a solid foundation for future advancements.Looking ahead, further refinements to Agent Laboratory could expand its capabilities, making it an even more valuable tool for researchers across disciplines. As adoption grows, it has the potential to democratize access to advanced research tools, fostering a more inclusive and efficient scientific community.Check out the Paper, Code, and Project Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. Asif RazzaqAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences. [Recommended Read] Nebius AI Studio expands with vision models, new language models, embeddings and LoRA (Promoted)