Apple's Siri AI will be powered by Gemini Fender's guitar lessons are coming to Samsung TVs later this year The Fender Play app is coming to TVs for the first time. We've all heard of couch surfing, but Fender and Samsung have made it their 2026 mission to make couch a thing. Samsung TV users will soon be able to take guitar lessons from the comfort of their living rooms, with the first TV edition of the Fender Play app set to arrive in the first half of this year. Debuted at CES, players can choose from video-based lessons for both electric and acoustic guitar, as well as bass and -- for all the wannabe Jake Shimabukuros or George Formbys (one for the Brits) among you -- the ukulele. There are on-demand courses for different levels of skill, with each lesson built around a wide spectrum of well-known songs, everything from The Beatles' to Olivia Rodrigo's .

Sam Fender has won the 2025 Mercury Prize for his third album, People Watching, a steely-eyed dissection of working-class life in the north of England. The singer looked stunned when his name was announced. I didn't think that was going to happen at all, he told the BBC as he came off stage. I've spent the last 10 minutes crying. Fender beat the likes of Pulp and Wolf Alice - both former winners of the £25,000 prize for the best British or Irish album of the year - at a star-studded ceremony in Newcastle's Utilita Arena.

An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot manipulator to learn and generalise motions across complex surfaces by leveraging differential mathematical operators on discrete manifolds to embed information on the geometry of the workpiece extracted from triangular meshes, and extend the Dynamic Movement Primitives (DMPs) framework to generate motions on the mesh surfaces. We also propose an effective strategy to adapt the motion to different surfaces, by introducing an isometric transformation of the learned forcing term. The resulting approach, namely MeshDMP, is evaluated both in simulation and real experiments, showing promising results in typical industrial automation tasks like car surface polishing.

Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user's interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in the PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users' browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users' historical purchases and potential interests.

Bill Holmes, facilities manager at the Corona, Calif., plant that produces the iconic Fender Stratocaster and Telecaster guitars, remembers all too well walking the factory floor with a crude handheld vibration analyzer and then plugging the device into a computer to get readings on the condition of his equipment. While all of the woodworking was done by hand when Leo Fender founded Fender Musical Instruments Corp. 75 years ago, today the guitar necks and bodies are produced with computer-controller woodworking routers, then handed off to the craftsmen who build the final product. Holmes says he is always looking for the latest technological advances to solve problems (he uses robotics to help paint the guitars), and there's no problem more vexing than equipment breakdowns. Preventive maintenance, where machines get attention on a predetermined schedule, is insufficient, he says. "Ninety percent of breakdowns are instant failures that shut down processes. If you can spot a failure before it happens, you're not shutting down production and the maintenance team isn't running around putting out fires."

The best products in the world are made by vertically integrated businesses: Apple's hardware to software; Amazon's warehouses to websites; and Carnegie's mines to mills [1]. Zetta is completely focused on investing in data and machine learning startups. We see lots of horizontal platforms and APIs that anyone can use to add some machine learning models to their application. However, machine learning has advanced to the point where customers expect better than commodity performance. We like to see startups vertically integrating their technical skills with the skills of domain experts and unique data acquisition to build applications with the level of accuracy required in commercial and industrial settings.

I'm known to be a bit negative about email. When you receive a few hundred of them a day, it tends to make you a little skittish, even a little depressed. Yet, it's still my primary form of communication, especially when I'm trying to find answers to problems. In our highly digitized world, it's amazing we still use asynchronous communication so often. When you send a message, there's no way of knowing if the recipient is doing anything about it.

I'm known to be a bit negative about email. When you receive a few hundred of them a day, it tends to make you a little skittish, even a little depressed. Yet, it's still my primary form of communication, especially when I'm trying to find answers to problems. In our highly digitized world, it's amazing we still use asynchronous communication so often. When you send a message, there's no way of knowing if the recipient is doing anything about it.

Conditional preference networks (CP-nets) exploit the power of conditional ceteris paribus rules to enable a compact representation of human preferences. CP-nets have much appeal. However, the study of CP-nets has not advanced sufficiently for their widespread use in complex, real-world applications. Most studies limit their attention to strict, complete, consistent preferences over binary domains. In my research, I attempt to address these limitations to make CP-nets more useful. I discuss recent research in which we presented a novel algorithm for learning CP-nets from user queries, as well as work showing how to adapt existing algorithms to learn and reason with multivalued CP-nets that can model indifference as well as strict preference. I outline anticipated research to extend our elicitation algorithm to a richer class of CP-nets, develop a formal model of expected flipping sequence length, and learn CP-nets in which a subject prefers to coordinate certain features, leading to a cycle in the dependency graph.