AI-generated music is becoming increasingly common and increasingly difficult to recognise. Here are the tell-tale signs that reveal whether a song is AI-generated – and what this development means for the music industry.

What Do We Need From Our Homes Right Now? The global editorial directors of WIRED and Architectural Digest on teaming up to help you understand how we live today, and what comes next. There's no place like home--even if it keeps changing. After all, the places where we reside in 2026 look remarkably different than they did even a few decades ago: the style and decor, the technology and appliances, and even the way houses are insured and protected from natural disasters. The external forces shaping our day-to-day lives today, in turn, will inform what makes a home desirable--and safe--decades from now.

Devices that monitor seniors for safety are appealing to worried loved ones and underresourced home care agencies. It was January of 2026 in North Seattle, and my 86-year-old father was struggling to move around his house. "I'm stumbling around here," my 86-year-old father told a guest in his home this past January. "Oooh, ooh, careful," the guest replied. "Yeah, I almost fell down."

An icon of Silicon Valley's counterculture, Stewart Brand is confronting his final years in a home that embodies the self-sufficient, DIY ethos of his famous Whole Earth Catalog. The three-building cluster in Petaluma where Stewart Brand and Ryan Phelan live. The new studio is in the center. This past January, Stewart Brand published a book, "Maintenance is what keeps everything going," he begins. "It's what keeps life going." Brand's life has been going for 87 years, but lately the going has been tough. The man known for creating the Whole Earth Catalog --the 1960s countercultural guide to self-sufficiency that Steve Jobs was fond of --has an incurable disease and is down to 130 pounds, an alarming weight for a nearly 6-footer. Brand's mind is sharp as ever; you can't talk to the man for five minutes without learning something. But his once-nimble movements are now cautious, and he's never far from an oxygen tank. Stewart Brand's body, in other words, requires constant maintenance.

Traditional Home Insurance Is Collapsing. A new, AI-assisted model of insurance is quietly exploding in disaster-prone areas--and may be coming for FEMA too. Is it the answer to climate change, or a trap? In 2019, when the worst flooding in recorded history spread across the entire Mississippi River basin, Colin Wellenkamp's phone rang for weeks. Wellenkamp runs a nonprofit called the Mississippi River Cities & Towns Initiative, which coordinates between mayors' offices in more than 100 river communities from northern Minnesota to southern Louisiana. As he describes it, his headquarters served as "one big virtual situation room" for relief agencies and municipalities up and down the central US.

Accelerating model convergence in resource-constrained environments is essential for fast and efficient neural network training. This work presents learn2mix, a new training strategy that adaptively adjusts class proportions within batches, focusing on classes with higher error rates.

Dynamic pricing algorithms typically assume continuous price variables, which may not reflect real-world scenarios where prices are often discrete. This paper demonstrates that leveraging discrete price information within a semi-parametric model can substantially improve performance, depending on the size of the support set of the price variable relative to the time horizon. Specifically, we propose a novel semi-parametric contextual dynamic pricing algorithm, namely BayesCoxCP, based on a Bayesian approach to the Cox proportional hazards model. Our theoretical analysis establishes high-probability regret bounds that adapt to the sparsity level γ, proving that our algorithm achieves a regret upper bound of eO(T(1+γ)/2 + dT) for γ < 1/3 and eO(T2/3 + dT) for γ 1/3, where γ represents the sparsity of the price grid relative to the time horizon T. Through numerical experiments, we demonstrate that our proposed algorithm significantly outperforms an existing method, particularly in scenarios with sparse discrete price points.

AF3 introduces: CMM (i) AF-Whisper, a unified audio encoder trainedPrevious SOTA (Closed Source) using a novel strategy for joint representation learning across all 3 modalities of speech, sound, and music; (ii) flexible, on-demand thinking, allowing the model to do chain-of-thought-type reasoning before answering; (iii) multi-turn, multiaudio chat; (iv) long audio understanding and reasoning (including speech) up MMSU to 10 minutes; and (v) voice-to-voice interaction. To enable these capabilities, (avg.)

Online tensor decompositions are powerful and proven techniques that address the challenges in processing high-velocity streaming tensor data, such as traffic flow and weather system. The main aim of this work is to propose a novel online functional tensor decomposition (OFTD) framework, which represents a spatialtemporal continuous function using the CP tensor decomposition parameterized by coordinate-based implicit neural representations (INRs). The INRs allow for natural characterization of continually expanded streaming data by simply adding new coordinates into the network. Particularly, our method transforms the classical online tensor decomposition algorithm into a more dynamic continual learning paradigm of updating the INR weights to fit the new data without forgetting the previous tensor knowledge. To this end, we introduce a long-tail memory replay method that adapts to the local continuity property of INR. Extensive experiments for streaming tensor completion using traffic, weather, user-item, and video data verify the effectiveness of the OFTD approach for streaming data analysis. This endeavor serves as a pivotal inspiration for future research to connect classical online tensor tools with continual learning paradigms to better explore knowledge underlying streaming tensor data.

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