Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Marvel unveils first'VisionQuest' trailer, reveals Ultron's return to the MCU Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and . Marvel drops a special look'Avengers: Doomsday' trailer at D23 'Shrek 5' teaser trailer: Shrek, Fiona, and Donkey are back to make you feel like an'All Star' Marvel Studios debuted the first trailer for its Disney+ series at D23 in Anaheim, California, offering the clearest look yet at Paul Bettany's return as the sentient AI hero Vision. The trailer finds Vision living in hiding and grappling with an existential crisis after essentially being resurrected in .

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series The popular Square Enix video game starring Mickey Mouse and friends is getting a TV show. The hit video game series Kingdom Hearts is getting its own TV show on Disney+. The next time is on your television screen, you might not be playing it as a video game but watching it as an anime TV series. A slew of Disney-related movies and TV shows have been announced over the past 24 hours as part of D23, the company's annual fan event, which is currently taking place in Anaheim, California. Disney announced that an anime TV series based on the video game franchise will debut soon on Disney+'s streaming platform and the Disney Channel.

AIhub Coffee Corner: does AI change the way we think? This month we ask whether AI tools are changing the way we think. Joining the conversation this time are: Joydeep Biswas (The University of Texas at Austin), Sanmay Das (Virginia Tech), Rina Dechter (University of California, Irvine), Sabine Hauert (University of Bristol), Michael Littman (Brown University), and Marija Slavkovik (University of Bergen). There's just so much to discuss there. I don't know how to start. First of all, it is definitely changing how we write code, develop code, think about correctness, and work on projects. Once upon a time, implementation used to be bottlenecked by writing the code. We are no longer bottlenecked by writing the code, we are bottlenecked in testing and understanding for correctness. As a researcher, there's a lot of what I would call throwaway tools.

Tesla vehicles and super chargers are shown at a Tesla dealership in Buena Park, California, on 28 January 2026. Tesla vehicles and super chargers are shown at a Tesla dealership in Buena Park, California, on 28 January 2026. Strong figures suggest Tesla's auto business is regaining momentum after two straight annual sales declines Tesla blew past Wall Street estimates for second-quarter deliveries on Thursday, posting a record for the period as recovering demand in Europe outweighed persistent weakness in North America. The strong figures suggest Tesla's mainstay auto business is regaining momentum after two straight annual sales declines, providing the spending cushion needed to power its ambitions in autonomous driving and artificial intelligence - the main drivers of the company's roughly $1.6tn valuation. Tesla expects to spend more than $25bn on capital expenditure in 2026, nearly triple the $8.5bn last year, to expand AI infrastructure, battery production, Cybercab manufacturing and Optimus robots.

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Things to Do in L.A. Tap to enable a layout that focuses on the article. This startup was supposed to revolutionize California's wine industry: 'It totally failed' Nilay Patel, left, interviews Monarch Tractor Chief Executive Praveen Penmetsa during Vox Media's 2023 Code Conference in Dana Point, Calif., in 2023. That year, Monarch was on a Forbes list of startups most likely to reach a $1-billion valuation. This is read by an automated voice. Please report any issues or inconsistencies here .

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This week I've been at SXSW London . There's been music, film, and a lot--and I mean --of talk about AI. I also had the opportunity to sit down with Gloria Mark, a psychologist at the University of California, Irvine, who has spent the last 30 years studying how people interact with digital technologies. Early in her career, the biggest concerns were the potential impacts of internet and email use on our brains. We may laugh those concerns off today, but it's true that as the technologies became more ubiquitous and ingrained in our daily lives, our attention spans began to shrink.

Simone Bombari asked us whether the 1-bit quantized random vector Y = sgn(Wx) has subgaussian norm bounded by a universal constant. Here W is an n n random Gaussian matrix, and x is an independent standard normal random vector in Rn. The question is nontrivial since the coordinates of Y are not independent. We give a strong positive answer to this question - for any bounded map instead of sgn() - using AI: AIDiscovery and Generalization (Theorem 1): To handle coordinate dependence, Gemini 3.5 Flash1 proposed decomposing the Gaussian vector into independent parts, using one part to "smooth" the sign function, and then applying Gaussian concentration for Lipschitz functions.

Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $Δ$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.