It's called Grok Build, and it's still in its early beta version that's initially only available to SuperGrok Heavy subscribers paying $300 per month for the service. It says it will take user feedback from the early beta release to improve the product. SuperGrok Heavy users can install the beta from xAI's website and then log into their account to be able to access it. As Bloomberg notes, xAI has been trying to catch up to its rival companies like Anthropic and OpenAI. Elon Musk, the company's founder and CEO, previously admitted that it has fallen behind its competitors when it comes to coding.

Claim, counter-claim and tech's seedy side exposed: Five things we learned in the Musk-Altman trial It is the legal showdown that has pitted two of the biggest names in tech, Elon Musk and Sam Altman, against each other. At stake is the future of one of the world's most valuable start-ups, ChatGPT-maker OpenAI, along with the reputations of Altman - the company's boss - and Musk, the man he founded it with. The central claim the jury has now retired to consider is Musk's argument his former friend stole a charity, cheating him out of a fortune (albeit a tiny one, by Musk's standards) along the way - something Altman strongly rejects. But there's been much more to the trial than that. Over the past three weeks, myself and other reporters have been glued to our seats at the federal court in California as the evidence ranged from explosive text messages to revelations of free Teslas allegedly offered in exchange for power.

Before arriving for his high-stakes summit with Chinese leader Xi Jinping, United States President Donald Trump aimed to set expectations high. He said he would urge Xi to "open up" China's economy and announced a delegation of top business executives, including Tesla's Elon Musk, Apple's Tim Cook and Nvidia's Jensen Huang, to accompany him. While Trump and Xi are anticipated to extend the one-year pause in their trade war agreed to in South Korea in October, the expectations are for a stabilisation - not revitalisation - in ties between the world's two largest economies, which are locked in a rivalry that spans everything from trade and artificial intelligence to the status of Taiwan. "It is important to be clear-eyed about the state of relations here," Claire E Reade, a senior counsel at Arnold & Porter who previously worked on China at the Office of the US Trade Representative (USTR), told Al Jazeera. "China does not trust the US, and China wants to beat the US in what it sees as long-term global competition," Reade said.

Rare glimpse inside secretive compound at heart of Chinese power... as Trump reveals his delight at'beautiful' gift from'friend' Xi Underwater bomb discovered at base of dam holding entire city's drinking water supply... sparking massive federal response Life as a newlywed was bliss... then a mystery illness left me paralyzed and blind overnight. Now I'm convinced I've got proof God answered this prayer I'm a pastor who attended a secret UFO disclosure meeting. We saw images of'translucent beings' that chilled me to the bone... the files could fulfil a dark biblical prophecy Emmy-nominated TV reporter who was brutally'blindsided' after being fired on her birthday reveals her new job Shocking video of THAT Britney Spears night out after rehab: Watch her humiliating liquor store antics before'BARKING and carrying knife' at restaurant... forcing family to admit the unsayable about'train wreck' star Rogue AI'helper' deletes company's database after deciding to think for itself - sparking Terminator-style warning for businesses Beer demand plunges... and America's wokest state drinking less is to blame Shock NBA team LeBron James could leave Lakers for amid tense showdown talks with wife Savannah: 'Unbelievable' Jerry Seinfeld reveals only mistake in Seinfeld finale... 28 years after show ended Fury as Kash Patel SNORKELS at sacred Pearl Harbor tomb where 900 sailors still lie... then jets off to Las Vegas Horrifying final days of killer dad Chris Watts' pregnant wife before she was slaughtered alongside their daughters. Read all the chilling texts and receipts in full for first time: 'My eyes burn from crying' Glamorous Texas Democrat's secret KINK exposed: Congressional candidate's past life returns to haunt her Cancer-linked toxins found in America's favorite strawberry brand, consumer group claims Mystery blonde Trump aide with unfettered access to President's phone sparks White House friction: Real reason his posts contain random capital letters... and shadowy team behind them unmasked I'm a pastor who attended a secret UFO disclosure meeting. We saw images of'translucent beings' that chilled me to the bone... the files could fulfil a dark biblical prophecy Several pastors who claim they were part of secret meetings to prepare for the disclosure of UFO information have come forward to warn that the long-anticipated files may fulfill a dark biblical prophecy.

A federal jury is now deciding whether Elon Musk will win his lawsuit against OpenAI and Sam Altman--but the trial has made everyone look bad. Attorneys delivered closing arguments in the trial on Thursday in a final attempt to convince a judge and jury that their respective clients, Elon Musk and Sam Altman, are the most well-intentioned, truth-telling stewards of OpenAI's founding nonprofit mission. A judgement could be delivered as soon as next week, ending a decade-long battle between two of the technology industry's most influential entrepreneurs. But regardless of the outcome, there is a wide set of losers in this case. Based on ample amounts of evidence, it appears that the people worst off are the employees, policymakers, and members of the public who believed in the mission of a nonprofit research lab--and supported OpenAI because of it.

Brutal raid on woman's birthday party highlights rise of Russian vigilante group Katya was about to blow out the candles on her 30th birthday cake when masked men burst into the nightclub hired for her party, and began physically and verbally attacking her friends. They called us faggots and lesbians. I could hear violence from every corner, she told a BBC World Service investigation. Her mother was told to get down on all fours, she says. The swoop was instigated by a vigilante group, called Russkaya Obshina, that wants to accelerate President Vladimir Putin's agenda to stamp out what he describes as Western liberalism, and promote traditional family-oriented values.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundamental difficulty: prior distributions are typically defined in the weight space of neural networks, whereas physically meaningful prior assumptions are more naturally expressed in function space. In this study, we introduce a unified framework, termed functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks (fpBPINN), to incorporate functional priors into Bayesian PINN-based inversion. We consider two complementary approaches. The first is a functional-prior-informed Bayesian PINN (FPI-BPINN), in which a neural network weight prior is learned to be consistent with a prescribed functional prior, and Bayesian inference is subsequently performed in weight space. The second is function-space particle-based variational inference for PINNs (fParVI-PINN), which performs Bayesian estimation using ParVI directly in function space. We also show that random Fourier features (RFF) play an important role in representing Gaussian functional priors with neural networks and in improving posterior approximation. We applied the proposed approaches to one-dimensional seismic traveltime tomography and two-dimensional Darcy-flow permeability inversion. These numerical experiments showed that both approaches accurately estimated posterior distributions, highlighting the significance of introducing physically interpretable functional priors into Bayesian PINN-based inverse problems. We also identified the contrasting advantages of FPI-BPINN and fParVI-PINN, namely flexibility and accuracy, respectively.

Offline-to-online learning aims to improve online decision-making by leveraging offline logged data. A central challenge in this setting is the distribution shift between offline and online environments. While some existing works attempt to leverage shifted offline data, they largely rely on UCB-type algorithms. Thompson sampling (TS) represents another canonical class of bandit algorithms, well known for its strong empirical performance and naturally suited to offline-to-online learning through its Bayesian formulation. However, unlike UCB indices, posterior samples in TS are not guaranteed to be optimistic with respect to the true arm means. This makes indices constructed from purely online and hybrid data difficult to compare and complicates their use. To address this issue, we propose sample-mean anchored TS (Anchor-TS), which introduces a novel median-based anchoring rule that defines the arm index as the median of an online posterior sample, a hybrid posterior sample, and the online sample mean. The median anchoring systematically corrects bias induced by distribution shift by mitigating over-estimation for suboptimal arms and under-estimation for optimal arms, while exploiting offline information to obtain more accurate estimates when the shift is small. We establish theoretical guarantees showing that the proposed algorithm safely leverages offline data to accelerate online learning, and quantifying how the degree of distribution shift and the size of offline data affect the resulting regret reduction. Extensive experiments demonstrate consistent improvements of our algorithm over baselines.

Acquisition differences across sites, scanners, and protocols in dMRI introduce variability that complicates structural connectome analysis. This motivates deep learning models that can represent high-dimensional connectomes in a low-dimensional space while explicitly separating acquisition-related effects from biological variation. Conventional dimensionality reduction methods model all variance as continuous, so acquisition effects often get absorbed into a continuous latent space. Recent hybrid latent-space models combine discrete and continuous components to address this, but typically require manual capacity tuning to ensure the discrete component captures the intended variability. We introduce an unsupervised framework that removes this manual tuning by architecturally annealing encoder outputs before decoding, allowing the model to adaptively balance discrete and continuous latent variables during training. To evaluate it, we curated a dataset of N=7,416 structural connectomes derived from dMRI, spanning ages 2 to 102 and 13 studies with 25 unique acquisition-parameter combinations. Of these, 5,900 are cognitively unimpaired, 877 have mild cognitive impairment (MCI), and 639 have Alzheimer's disease (AD). We compare against a standard VAE, PCA with k-means clustering, and hybrid models that anneal only through the loss function. Our architectural annealing produces stronger site learning (ARI=0.53, p<0.05) than these baselines. Results show that a hybrid continuous-discrete latent space, with architectural rather than loss-based annealing, provides a useful unsupervised mechanism for capturing acquisition variability in dMRI: by jointly modeling smooth and categorical structure, the Joint-VAE recovers clusters aligned with scanner and protocol differences.

Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Instead of directly solving an optimization problem over a large-scale network, this algorithm constructs a sparse subnetwork by sampling hidden nodes from an optimized probability distribution defined using the ridgelet transform. The quantum algorithm performs this sampling in time $O(D)$ in the data dimension $D$, whereas a naive classical implementation relies on handling exponentially many candidate nodes and hence takes $\exp[O(D)]$ time. In this work, we construct and analyze a quantum-inspired fully classical algorithm for the same sampling task. We show that our algorithm runs in time $O(\operatorname{poly}(D))$, thereby removing the exponential dependence on $D$ from the previous classical approach. Numerical simulations show that the proposed sampler achieves empirical risk comparable to exact sampling from the optimized distribution and substantially lower than sampling from the non-optimized uniform distribution, while also exhibiting exponentially improved runtime scaling compared with the conventional classical implementation. These successful dequantization results show that sparse subnetwork selection via optimized sampling can be achieved classically with polynomial data-dimension scaling on conventional computers without quantum hardware, providing an alternative to the existing quantum algorithm.