I've recently introduced a few friends to the power of a great robot vacuum. One of my friends calls hers a marriage saver, while the other was both thrilled and horrified by how many stains the vacuum's AI found on her floors. Personally, my robot vacuums keep me from wanting to set the litter box on fire, since my cat is on a mission to create his own navigational trail of litter through my home. The best robot vacuums these days aren't just vacuuming your floors, nor are they blindly bumping around your house like they used to. These gadgets are mopping, scrubbing away stains, lifting themselves off of obstacles, and even reminding you to clean the dirtier areas in your home more frequently. A good robot vacuum can cost a pretty penny, but it doesn't have to, depending on what you're looking for. I've been testing every new robot vacuum I can in my three-story home filled with three adults, a preschooler, and a cat who's on a mission to get litter all over the house.

What Will It Take to Get A.I. Out of Schools? The tech world assumes that A.I.-aided education is necessary and inevitable. A growing number of parents, educators, and cognitive scientists say the opposite. I don't like A.I., and I am raising my children not to like it. I've been telling them for years now that chatbots are manipulative and dangerous, that A.I.-image generators are loosening our collective grip on reality, that large language models are built atop industrial-scale intellectual-property theft. At times, I find myself speaking with my kids about A.I. in the same terms that we might discuss a creepy neighbor who lives down the block: avoid eye contact, cross the street when you walk past his house, and, when in doubt, call on a trusted adult. Yes, I, too, have suspected that the creepy neighbor walks on cloven hooves inside his Yeezy Boosts, but he probably isn't going anywhere--in fact, he keeps buying up properties around town--so just try your best not to engage. Somehow, I was not prepared for the creepy neighbor to start hanging around my kids' schools; somehow, I thought we had until high school.

Will fusion power get cheap? New research suggests that cost declines could be slow for the technology. Fusion power could provide a steady, zero-emissions source of electricity in the future--if companies can get plants built and running. But a new study suggests that even if that future arrives, it might not come cheap. Technologies tend to get less expensive over time. Lithium-ion batteries are now about 90% cheaper than they were in 2013.

We show that several striking differences in memory performance between recognition and recall tasks are explained by an ecological bias endemic in classic memory experiments - that such experiments universally involve more stimuli than retrieval cues. We show that while it is sensible to think of recall as simply retrieving items when probed with a cue - typically the item list itself - it is better to think of recognition as retrieving cues when probed with items. To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds. We build a simple computational model around this theory, using sampling to approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues. This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory.

A visitor looks at the copy of Michelangelo's Last Judgment by Robert le Voyer at the Louvre in Paris, 14 April 2026. A visitor looks at the copy of Michelangelo's Last Judgment by Robert le Voyer at the Louvre in Paris, 14 April 2026. To be human is to live with friction. That's something AI boosters will never understand We're being sold a world where there's no room for reflection or spontaneity. H ow fast do you have to strike a match to get it to light?

In the AI era, Apple's strengths may become its constraints Apple has expressed some willingness to use AI technology developed by rivals when needed. San Francisco - Apple built its empire on control. For decades, the company's tightly managed ecosystem, spanning custom chips, proprietary operating systems and curated apps, delivered devices that were secure and easy to use. That approach helped turn the iPhone into the most successful consumer product in history, generating nearly $210 billion in revenue last year. It also made Apple the world's top-valued company for much of the past decade, a position only overtaken by artificial intelligence chipmaker Nvidia in 2024.

We introduce and study the problem of calibrating conditional risk, which involves estimating the expected loss of a prediction model conditional on input features. We analyze this problem in both classification and regression settings and show that it is fundamentally equivalent to a standard regression task. For classification settings, we further establish a connection between conditional risk calibration and individual/conditional probability calibration, and develop theoretical insights for the performance metric. This reveals that while conditional risk calibration is related to existing uncertainty quantification problems, it remains a distinct and standalone machine learning problem. Empirically, we validate our theoretical findings and demonstrate the practical implications of conditional risk calibration in the learning to defer (L2D) framework. Our systematic experiments provide both qualitative and quantitative assessments, offering guidance for future research in uncertainty-aware decision-making.

Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This task is especially difficult in the pre-launch and early post-launch phases, when product-specific outcome history is limited or unavailable, creating a cold-start problem. In these phases, firms must make decisions before demand patterns become reliably observable, while early signals are often sparse, noisy, and unstable We propose the Conditional Diffusion Life-cycle Forecaster (CDLF), a conditional generative framework for forecasting new-product life-cycle trajectories under cold start. CDLF combines three sources of information: static descriptors, reference trajectories from similar products, and newly arriving observations when available. Here, static descriptors refer to structured pre-launch characteristics of the product, such as category, price tier, brand or organization identity, scale, and access conditions. This structure allows the model to condition forecasts on relevant product context and to update them adaptively over time without retraining, yielding flexible multi-modal predictive distributions under extreme data scarcity. The method satisfies consistency with a horizon-uniform distributional error bound for recursive generation. Across studies on Intel microprocessor stock keeping unit (SKU) life cycles and the platform-mediated adoption of open large language model repositories, CDLF delivers more accurate point forecasts and higher-quality probabilistic forecasts than classical diffusion models, Bayesian updating approaches, and other state-of-the-art machine-learning baselines.

Gear 2026 Mother's Day gift guide: An updating list of great presents to give your mom No matter what your mom is into, we have perfect gift suggestions from budget-friendly options to full-on splurges. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Treat your mom to the treats she deserves. We may earn revenue from the products available on this page and participate in affiliate programs. Type "gifts for mom" into a search bar and the algorithm pulls you toward the same black hole of spa baskets, quippy wine glasses, and bouquets that spew petals all over the floor.

Booking.com confirmed hackers may have accessed customer names, emails, phone numbers and booking details, though financial data was not compromised in the breach.