Higher-resolution video, improved overall performance, and an exciting new AI-powered voice response make Swann's second video doorbell a winner. Swann, a longtime player in the security camera world, has been spreading its wings to expand into related smart home gear, including video doorbells. Its first SwannBuddy Video Doorbell, a lackluster release, hit in 2022. The all-new SwannBuddy 4K Video Doorbell expands that device's resolution and image quality considerably, resolving one of the original product's biggest shortcomings. The SwannBuddy 4K offers a familiar design to both the original SwannBuddy and most video doorbells, with a large doorbell button in the center of the device, ringed with light (briefly blue, turning red when recording), a camera lens up top, and a motion sensor at the bottom.

This enterprise proved so amusing that the pair, in collaboration with composer Dave Soldier, repeated the experiment with popular music, releasing the "most wanted" and "least wanted" songs together on a CD with a cover photo of all three men wearing white lab coats and pointing at a calculator. Sadly, the pair stopped short of what I view as the greatest challenge: producing novels that reflect what Americans like and dislike in fiction. Now, at last, with People's Choice Literature, by the writer/artist/composer Tom Comitta, a new "scientist" has taken up the task. People's Choice Literature offers its readers two novels for the price of one. The first is a thriller whose heroine tries to prevent her boss, a new age–y tech mogul, from launching a quantum computing network that will bring about a total surveillance state.

Who says the contact center space is moving slowly? In early April, I attended 8X8's Analyst Summit 2025, where company leaders outlined their vision for transforming customer experience (CX) through an integrated, AI-enabled ecosystem. There was so much content about recent and upcoming trends that I needed to take some time to analyze the discussions before sharing this now long-overdue recap of the event. Typically known for its unified communications as a service (UCaaS) and contact center as a service (CCaS) solutions, 8x8 is increasingly focusing on its role as a multi-product communications intelligence platform -- one designed to deliver meaningful business outcomes, not just digital transactions. That evolution reflects a broader market trend: the traditional boundaries between UCaaS and CCaaS are fading.

Senior medical analyst Dr. Marc Siegel discusses advancements in artificial intelligence aimed at predicting an individuals future risk of breast cancer and the increased health risks from cannabis as users age. SMARTER SCREENINGS: The U.S. Food and Drug Administration (FDA) has approved the first artificial intelligence (AI) tool to predict breast cancer risk. NOVA IN ACTION: Flock Safety has released another piece of revolutionary technology aimed at keeping everyday civilians safe from crime. The company's new product, Flock Nova, helps law enforcement with a common but often overlooked problem – a lack of data sharing and access. ROBOT NURSES RISING: The global healthcare system is expected to face a shortage of 4.5 million nurses by 2030, with burnout identified as a leading cause for this deficit.

President Donald Trump's recent visit to the UAE marked a pivotal moment for UAE-U.S. bilateral relations, shining a spotlight on a shared vision for the future. As the UAE and the "New Gulf" pivot from oil to cutting-edge technologies, our partnership with the U.S., rooted in decades of trust, has become a beacon of what's possible when nations collaborate. This trust has paved the way for a bold new chapter: a strategic economic alliance poised to create tens of thousands of high-tech, energy and manufacturing jobs, driving prosperity in both of our countries. At the heart of this collaboration lies the new U.S.-UAE AI Acceleration Partnership. This initiative will advance cooperation in artificial intelligence and other transformative technologies while spurring investment flows between our nations.

Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation, i.e., regions belonging to the same class may exhibit a very different appearance even in the same picture. This diversity will make the label propagation hard from pixels to pixels. To address this problem, we propose a novel approach to regularize the distribution of within-class features to ease label propagation difficulty. Specifically, our approach encourages the consistency between the prediction from a linear predictor and the output from a prototype-based predictor, which implicitly encourages features from the same pseudo-class to be close to at least one within-class prototype while staying far from the other between-class prototypes.

Large language models produce human-like text that drives a growing number of applications. However, recent literature and, increasingly, real world observations, have demonstrated that these models can generate language that is toxic, biased, untruthful or otherwise harmful. Though work to evaluate language model harms is under way, translating foresight about which harms may arise into rigorous benchmarks is not straightforward. To facilitate this translation, we outline six ways of characterizing harmful text which merit explicit consideration when designing new benchmarks. We then use these characteristics as a lens to identify trends and gaps in existing benchmarks. Finally, we apply them in a case study of the Perspective API, a toxicity classifier that is widely used in harm benchmarks. Our characteristics provide one piece of the bridge that translates between foresight and effective evaluation.

Causal modeling is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate using empirical data rather than synthetic data. We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice. We show that such techniques are feasible and that data sets are available to conduct such evaluations. We also show that these techniques produce substantially different results than using structural measures and synthetic data.

Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited. We investigate MCTS and Depth-First Proof-Number (DFPN) Search, a PNS variant, in the domain of Retrosynthetic Analysis (RA). We find that DFPN's strengths, that justify its success in games, have limited value in RA, and that an enhanced MCTS variant by Segler et al. significantly outperforms DFPN. We address this disadvantage of DFPN in RA with a novel approach to combine DFPN with Heuristic Edge Initialization. Our new search algorithm DFPN-E outperforms the enhanced MCTS in search time by a factor of 3 on average, with comparable success rates.