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.

New Russini-Vrabel photos raise ESPN conflict questions but the network won't answer them ESPN's Mad Dog Russo melts down over'U-S-A' chants at the RBC Heritage A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan'First Take' host acts disgusted when she has to cover Vrabel-Russini drama Edward Cabrera's strikeout prop is the play as struggling Phillies face surging Cubs today Nuggets vs Timberwolves Game 3 pick hinges on Jaden McDaniels calling out Denver's entire defense Charles Barkley was disgusted by Magic's highly questionable pregame handshake ChatGPT predicted the first round of the NFL Draft and here's what it said Trump: Why would I use a nuclear weapon? California governor's race intensifies as six candidates face off Trump: US Navy to'shoot and kill' any boat placing mines in Hormuz Virginia court blocks Democrats' redistricting effort, Florida next Trump weighs in on Iran's internal power struggle and Strait of Hormuz control Hasan Piker justifies'social murder' of CEO Restaurant owner says hockey win was'beautiful sight,' defends patriotic response to media slam John Minadakis, the owner of Jimmy's Famous Seafood in Baltimore, tells Fox News Digital why he felt a need to defend USA pride after an Olympic article slam. As we have discussed several times recently, the Stanley Cup Playoffs are home to some of the most superstitious human beings on the planet. Several of the most famous traditions in the sport stem from fans and players alike doing something born out of superstition. Take the Detroit Red Wings and their octopus toss, whose eight legs symbolize the eight wins it took to win a Stanley Cup back when the league was much smaller.

The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification when the SCAR condition is unsatisfied. A secondary objective of this study is to determine the robustness of the LassoJoint method to perturbations of the SCAR condition. In the first step of our algorithm, we obtain cleaning labels from 2-means clustering. Subsequently, we perform logistic regression on the cleaned data, assigning positive labels from the cleaning algorithm with additional true positive observations. The remaining observations are assigned the negative label. The proposed algorithm is evaluated by comparing 11 real data sets from machine learning repositories and a synthetic set. The findings obtained from this study demonstrate the efficacy of the clustering algorithm in scenarios where the SCAR condition is violated and further underscore the moderate robustness of the LassoJoint algorithm in this context.

Bayesian neural network (BNN) posteriors are often considered impractical for inference, as symmetries fragment them, non-identifiabilities inflate dimensionality, and weight-space priors are seen as meaningless. In this work, we study how overparametrization and priors together reshape BNN posteriors and derive implications allowing us to better understand their interplay. We show that redundancy introduces three key phenomena that fundamentally reshape the posterior geometry: balancedness, weight reallocation on equal-probability manifolds, and prior conformity. We validate our findings through extensive experiments with posterior sampling budgets that far exceed those of earlier works, and demonstrate how overparametrization induces structured, prior-aligned weight posterior distributions.