The ANTHBOT N8 mows, mulches, collects grass clippings and removes leaves. Up to 1,500 m, no boundary wire required, 23-liter grass catcher. One robot handles mowing, clippings and fallen leaves. Robotic lawn mowers take the hard work out of mowing, but they rarely handle the cleanup. Most models rely exclusively on mulching, cutting the grass and leaving the clippings on the lawn.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . What Meta's new teen restrictions mean for young people What happens if the Waymo computer fails? Dana Perino: Policymakers need to find a way to stop'bad actors' from abusing this Theresa Payton reacts to Vance's AI data center push Meta social media settlement is a'reckoning': Dr Drew Pinsky Meta's social media settlement could reshape rules for kids online OlloNi SS1 learns your household over time. The'Cyber Guy' Kurt Knutsson cautions parents about AI-powered chatbot teddy bears, highlighting privacy risks as toys might collect children's personal details.

Fix It Future Blink Self Made Small Talk AI at Heart Watch of the Week Amplify The Best of 2024 AI at Play Watch History Office Ladies Podcast Don't Freak Out All Series Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland. Her work has earned numerous state awards, including the Virginia Press Association's top honor, Best in Show, and national recognition for narrative storytelling. For each year she has covered space, Sauers has won National Headliner Awards, including first place for her Sex in Space series.

When you purchase through links in our articles, we may earn a small commission. Not every old PC part deserves a spot in your new build. Here's how to tell what's safe to reuse and what to replace. When building a new desktop computer, it makes sense to reuse as many components as possible from your old machine. That very flexibility is one of the greatest advantages of a self-built system!

We've seen a number of classic game consoles get the Lego treatment over the years, including the NES and Atari 2600. You'll soon be able to build a version of the original Xbox as well -- perhaps even while sitting on an Xbox cushion from IKEA. However, this is a model from Mattel rather than Lego. This is a 1:1-scale model of the Xbox. It's a 3,509-piece interactive set with three small modules that reference Halo: Combat Evolved, The Elder Scrolls III: Morrowind and Psychonauts (which is slightly awkward, given Psychonauts developer Double Fine only just separated from Xbox).

Biological circuits have evolved to incorporate multiple modules that perform similar functions. In the fly olfactory circuit, both lateral inhibition (LI) and neuronal spike frequency adaptation (SFA) are thought to enhance pattern separation for odor learning. However, it remains unclear whether these mechanisms play redundant or distinct roles in this process. In this study, we present a computational model of the fly olfactory circuit to investigate odor discrimination under varying noise conditions that simulate complex environments. Our results show that LI primarily enhances odor discrimination in low and medium noise scenarios, but this benefit diminishes and may reverse under higher noise conditions. In contrast, SFA consistently improves discrimination across all noise levels. LI is preferentially engaged in low and medium noise environments, whereas SFA dominates in high noise settings. When combined, these two sparsification mechanisms enable optimal discrimination performance. This work demonstrates that seemingly redundant modules in biological circuits can, in fact, be essential for achieving optimal learning in complex contexts.

Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort. In today's data-driven world, such tasks are well suited for automation. We present a new R package that uses a computer vision model to automate the evaluation of residual plots. An accompanying Shiny application is provided for ease of use. Given a sample of residuals, the model predicts a visual signal strength (VSS) and offers supporting information to help analysts assess model fit.

The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks typically demand full-fledged code repositories rather than simple scripts. Building such repositories from scratch remains a major challenge. Fortunately, GitHub hosts a vast, evolving collection of open-source repositories, which developers frequently reuse as modular components for complex tasks. Yet, existing frameworks like OpenHands and SWE-Agent still struggle to effectively leverage these valuable resources.

We introduce SLED, an alternative approach to speech language modeling by encoding speech waveforms into sequences of continuous latent representations and modeling them autoregressively using an energy distance objective. The energy distance offers an analytical measure of the distributional gap by contrasting simulated and target samples, enabling efficient training to capture the underlying continuous autoregressive distribution. By bypassing reliance on residual vector quantization, SLED avoids discretization errors and eliminates the need for the complicated hierarchical architectures common in existing speech language models.

Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require features that are jointly informative across clients while disentangling clientspecific factors without supervision. We thus introduce FORLA, a novel framework for federated object-centric representation learning and feature adaptation using unsupervised slot attention. At the core of our method is a shared feature adapter, trained collaboratively across clients to adapt features from foundation models, and a shared slot attention module that learns to reconstruct the adapted features.