The Ecobee Smart Doorbell (wired) is a reliable, easy-to-use, high-end video doorbell. It depends on hardwired power, but it can trigger a homeowner's existing chimes. As with many of its competitors, you'll need to pay for a subscription to unlock all its features, but it can be incorporated into a robust home security system with professional monitoring at a very reasonable price. If you're already using one of Ecobee's smart home thermostats or security systems--or you're thinking about installing one--you'll want to consider the company's first video doorbell. The Ecobee Smart Doorbell Camera (wired) doesn't just compete with category leaders Ring, Nest, and Arlo, it also brings a few smart ideas of its own--and it plays especially well within Ecobee's larger smart home/home security ecosystem.

We study whether and how the choice of optimization algorithm can impact group fairness in deep neural networks. Through stochastic differential equation analysis of optimization dynamics in an analytically tractable setup, we demonstrate that the choice of optimization algorithm indeed influences fairness outcomes, particularly under severe imbalance. Furthermore, we show that when comparing two categories of optimizers, adaptive methods and stochastic methods, RMSProp (from the adaptive category) has a higher likelihood of converging to fairer minima than SGD (from the stochastic category). Building on this insight, we derive two new theoretical guarantees showing that, under appropriate conditions, RMSProp exhibits fairer parameter updates and improved fairness in a single optimization step compared to SGD. We then validate these findings through extensive experiments on three publicly available datasets, namely CelebA, FairFace, and MS-COCO, across different tasks as facial expression recognition, gender classification, and multi-label classification, using various backbones. Considering multiple fairness definitions including equalized odds, equal opportunity, and demographic parity, adaptive optimizers like RMSProp and Adam consistently outperform SGD in terms of group fairness, while maintaining comparable predictive accuracy. Our results highlight the role of adaptive updates as a crucial yet overlooked mechanism for promoting fair outcomes.

The United States under President Donald Trump is ramping up use of surveillance systems and artificial intelligence (AI) to track and arrest immigrants, raising fears that risks to accuracy and privacy could put almost anyone in danger of getting caught up in the crackdown. The Department of Homeland Security (DHS) and other immigration control agencies are using a suite of AI tools -- such as facial recognition scanners in public areas and robotic dogs patrolling the southern border for human movement -- as part of the crackdown on alleged illegal immigration. Many of the AI tools that immigration agents are using have been in place for years and are a legacy of previous administrations, according to Saira Hussain, an attorney at the Electronic Frontier Foundation, a digital rights advocacy group.

While new Apple iPhone "leaks" arrive on a near-weekly basis, evidence is growing that a folding iPhone is coming soon, now bolstered by new reports about its camera and use of Touch ID. Via 9To5Mac, Korean blog yeux1122, which covers chatter from Chinese social media site Weibo, reported that Apple's foldable iPhone will have a punch-hole camera design on its external display. The blog also shared that the internal display will be 7.76 inches with a 2713 x 1920p resolution, and the external display will be 5.49 inches with a 2088 x 1422p resolution. This is similar to the design of Samsung's flagship foldable, the Samsung Galaxy Z Fold 6, and the Google Pixel 9 Pro Fold. The punch-hole camera on the external display strengthens last month's report from Apple analyst Ming-Chi Kuo, who said the foldable iPhone will use a Touch ID side button like the iPad Air instead of Face ID, which is what every iPhone since the iPhone X has used.

For much of last year, US Marines conducting training exercises in the waters off South Korea, the Philippines, India, and Indonesia were also running an experiment. The service members in the unit responsible for sorting through foreign intelligence and making their superiors aware of possible local threats were for the first time using generative AI to do it, testing a leading AI tool the Pentagon has been funding. Two officers tell us that they used the new system to help scour thousands of pieces of open-source intelligence--nonclassified articles, reports, images, videos--collected in the various countries where they operated, and that it did so far faster than was possible with the old method of analyzing them manually. Though the US military has been developing computer vision models and similar AI tools since 2017, the use of generative AI--tools that can engage in human-like conversation--represent a newer frontier. The International Energy Agency states in a new report that AI could eventually reduce greenhouse-gas emissions, possibly by much more than the boom in energy-guzzling data center development pushes them up.

The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this relaxation forms a manifold, which we call the Relaxed Indicator Matrix Manifold (RIM manifold). Based on Riemannian geometry, we develop a Riemannian toolbox for optimization on the RIM manifold. Specifically, we provide several methods of Retraction, including a fast Retraction method to obtain geodesics. We point out that the RIM manifold is a generalization of the double stochastic manifold, and it is much faster than existing methods on the double stochastic manifold, which has a complexity of \( \mathcal{O}(n^3) \), while RIM manifold optimization is \( \mathcal{O}(n) \) and often yields better results. We conducted extensive experiments, including image denoising, with millions of variables to support our conclusion, and applied the RIM manifold to Ratio Cut, we provide a rigorous convergence proof and achieve clustering results that outperform the state-of-the-art methods. Our Code in \href{https://github.com/Yuan-Jinghui/Riemannian-Optimization-on-Relaxed-Indicator-Matrix-Manifold}{here}.

Lockin has stuffed pleny of clever ideas into this smart lock, but it feels like it's a few firmware updates away from something I'd trust to guard any of my entry doors. Lockin claims a history dating back to 2014--as well as the involvement of Hartmut Esslinger, best known as a key Apple Computer designer in the 1980s--but it wasn't until CES 2025 that the company really touched down with a major presence in the smart lock space. Though Esslinger has a reputation for minimalism, the new Lockin Veno 7 Pro really does come loaded with everything. It's a hub-free Wi-Fi lock with ANSI grade 2 and IP65 certifications that allows for access via a numeric touchpad, fingerprint reader, or palm vein scan--in addition to support for its mobile app and a physical key. A very wide-angle camera mounted on the front of the device also lets the unit work as a video doorbell, complete with a ring button that illuminates when someone comes near.

Amazon is running a sale on its Blink home security devices. Among the items that have seen a price drop is the Blink Video Doorbell, which is available for a record low of 30. That's half what you might otherwise pay for it. The doorbell allows you to answer your door using your phone. You can see who rang your doorbell via a 1080p video stream (there's an infrared night vision mode) and chat to them using the two-way audio feature.

Buying something in the shops used to be as simple as choosing the item and handing over the money. But in recent years, the great British shopping experience has dramatically changed. In 2025, artificial intelligence (AI) is patrolling Britain's retail stores to keep an eye on customers as they stock up on essentials. Now, people are subjected to a slew of AI-powered tech, including intelligent surveillance cameras, robots, facial recognition systems and online age checks. Home Bargains is the latest to follow the trend, with a new AI-enabled security system that watches you while you scan your own items.

This paper addresses the task of learning convex regularizers to guide the reconstruction of images from limited data. By imposing that the reconstruction be amplitude-equivariant, we narrow down the class of admissible functionals to those that can be expressed as a power of a seminorm. We then show that such functionals can be approximated to arbitrary precision with the help of polyhedral norms. In particular, we identify two dual parameterizations of such systems: (i) a synthesis form with an $\ell_1$-penalty that involves some learnable dictionary; and (ii) an analysis form with an $\ell_\infty$-penalty that involves a trainable regularization operator. After having provided geometric insights and proved that the two forms are universal, we propose an implementation that relies on a specific architecture (tight frame with a weighted $\ell_1$ penalty) that is easy to train. We illustrate its use for denoising and the reconstruction of biomedical images. We find that the proposed framework outperforms the sparsity-based methods of compressed sensing, while it offers essentially the same convergence and robustness guarantees.