Anthropic's rise came by doubling down on delivering generative artificial intelligence to enterprise clients rather than general users. Artificial intelligence company Anthropic said Thursday it had raised $65 billion in a new funding round that values the Claude maker at $965 billion, more than its archrival OpenAI, the maker of ChatGPT. The latest fundraising round confirms Anthropic's place as one of the most significant players in AI, with the startup led by Dario Amodei having drawn fans for its coding powers and state-of-the-art models. Anthropic's rise came by doubling down on delivering generative AI to enterprise clients rather than general users, the path initially chosen by OpenAI. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

We study contextual bandits in the stochastic i.i.d.\ setting, where a learner observes contexts drawn from an unknown distribution, selects actions from a finite set $A$, and aims to identify an approximately optimal policy from a given class based on bandit feedback. Motivated by bandit multiclass classification with zero-one rewards, we focus on the \emph{$s$-sparse} setting in which, for every context, the reward vector has $L_1$-norm at most $s \ll |A|$. Our main result is the design of algorithms that, with high probability, output an $ε$-optimal policy compared to policy class $Π$ using $\tilde{O} ((s/ε^2 + |A|/ε)\log |Π|/δ)$ samples. We extend this bound to general Natarajan classes and complement it with a matching lower bound (up to logarithmic factors), thereby closing a substantial gap left by prior work (Erez et al., 2024, 2025), which incurred an additional $Θ(|A|^9)$ dependence. We obtain these results via two complementary approaches. First, we analyze contextual bandits through the lens of contextual decision making with structured observations, designing an exploration-by-optimization algorithm whose sample complexity is governed by the \emph{decision-estimation coefficient} (DEC; Foster et al., 2021, 2022). We show that, with $s$-sparse rewards, the induced model class admits a sharp DEC bound that scales with $s$ and directly yields the optimal rate. Since this approach is largely information-theoretic and involves solving complex min-max optimization problems, we also develop a second, more specialized algorithmic method based on a low-variance exploration technique. This approach leads to concrete, tractable algorithms and naturally extends to contextual combinatorial semi-bandits, leading to improved sample complexity guarantees for bandit multiclass list classification.

Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic relevance determination (ARD), offers a practical Bayesian mechanism for feature sparsity via marginal likelihood optimization. Yet, its reliance on a homoscedastic noise model renders it sensitive to data contaminations such as outliers or misspecified noise, harming model fit and predictions. Instead, we propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification via a single Bayesian objective. This symmetric pruning of model and data offers a natural extension that preserves conjugacy, admits closed-form updates for standard optimization procedures, and aligns with perspectives from robust regression and influence functions. Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.

Cluster analysis is a widely applied machine learning technique to understand the existing patterns in the population of gamma-ray bursts (GRBs), in order to explore their physical sources. In the present scenario, the number of clusters corresponding to differentiable groups is still under conflict, in spite of numerous attempts with the state-of-the-art clustering procedures. This crucial unknown parameter needs to be evaluated, either directly or indirectly in terms of other tuning parameters, to produce the clusters in GRBs through implementation of an appropriate clustering algorithm. While most of the applied algorithms reached two physically explained groups of merger and collapsar predominated by the short and long bursts respectively, other statistical approaches violated this binary partition. However, physical establishment of any additional cluster(s) is not yet confirmed. Therefore, we propose a new algorithm, from a different stream of clustering referred to as `completely parameter-free', which carries out the classification of GRBs in a manner that has not been tried so far. It indicates two main groups, of short and long duration bursts from the BATSE sample, compatible with the merger-collapsar theory.

Finding approximations to an intractable probability distribution π of interest (usually known only up to a normalizing constant) is a key problem in scientific computing. Variational Inference stands out as a particularly attractive tool for this task, owing to its statistical and computational efficiency, and it has been the framework underlying many advances in computational statistics over the past half century (Parisi, 1980; Hinton and Van Camp, 1993; Jordan et al., 1999; Bishop and Nasrabadi, 2006). The central idea is to seek a tractable approximation to π within a chosen family of tractable distributions Q by minimizing a divergence to π over that'variational' family. Often, it is convenient or well-motivated to work with the family of product (or tensor, or factorized) distributions Q = P m, and define optimality through minimisation of the Kullback-Leibler (KL) divergence (also'relative entropy') min KL(ϱ||π): ϱ P m . A key practical aspect of working with this particular loss function is that in solving the associated optimisation problem, one is only required to compute expectations under the tractable variational distribution ϱ, rather than under the intractable target distribution π. In Bayesian statistics, π typically represents the joint posterior distribution of latent variables z Z and some parameters β B given observed data y Y. In these cases, we often choose m = 2 and seek the best variational approximation µ(dz) ν(dβ) to π to solve min KL(µ ν||π): µ P(Z), ν P(B) . The coordinate ascent variational inference algorithm (CAVI, Bishop and Nasrabadi, 2006; Blei et al., 2017) solves this problem by iteratively minimizing the Kullback-Leibler divergence with respect to one element at a time: given a starting point ν0, it iterates µk:= argmin

Dan Le Batard, who previously avoided Doug Emhoff abuse allegation, declares journalism'dead' USA Today calls Stephen Colbert, America's least funny comedian, a'gallant comic avenger' Critics reviews for'The Mandalorian and Grogu' are out, and it's yet another bad sign for Disney, Star Wars Can Victor Wembanyama be the true face of the NBA as a European? Audemars Piguet x Swatch'Royal Pop' release sparks mob scenes, pepper spray and arrests at malls Statisticians strangely don't count multiple clear-cut Caitlin Clark assists vs Mystics The best outdoor weekend in Northwest Georgia doesn't require'roughing it' or sleeping on the ground STRAIT OUTTA WAR?: Iran talks enter most critical phase yet as US military remains on standby Strait of Hormuz reopening among core conditions needed for Trump's approval Greg Gutfeld: A good sheep doesn't do that Brian Kilmeade: This should be in the'fiction section' of every library US, Israeli militaries must ensure Iranians'do not cheat,' Foundation for Defense of Democracies CEO says OutKick-Analysis'Supergirl' pre-release tracking looks disastrously bad for Hollywood after lead actress' bizarre comments Star Milly Alcock's divisive remarks and underwhelming trailers have tracking estimates far below studio hopes Greg Gutfeld: Will Hollywood take the hint? Fox News host Greg Gutfeld and the'Gutfeld!' panel discuss Hollywood's obsession with inserting politics into movies. Hollywood can't get out of its own way. For most of the last decade, the entertainment industry has worked extremely hard to alienate large numbers of potential customers.

Dan Le Batard, who previously avoided Doug Emhoff abuse allegation, declares journalism'dead' USA Today calls Stephen Colbert, America's least funny comedian, a'gallant comic avenger' Critics reviews for'The Mandalorian and Grogu' are out, and it's yet another bad sign for Disney, Star Wars Can Victor Wembanyama be the true face of the NBA as a European? Audemars Piguet x Swatch'Royal Pop' release sparks mob scenes, pepper spray and arrests at malls Statisticians strangely don't count multiple clear-cut Caitlin Clark assists vs Mystics The best outdoor weekend in Northwest Georgia doesn't require'roughing it' or sleeping on the ground NFL's grossly expanded national schedule is making RedZone and Sunday Ticket less essential Greg Gutfeld: A good sheep doesn't do that Brian Kilmeade: This should be in the'fiction section' of every library US, Israeli militaries must ensure Iranians'do not cheat,' Foundation for Defense of Democracies CEO says Scott Bessent reveals three conditions Iran deal must meet for Trump's final sign off Trump won't put'national security' at risk over 2026 midterms, former RNC chairman says President Trump: Democrats are'good salesmen,' but they have no policies While OutKick is trying to enjoy the NBA conference finals, though all the blowouts make that difficult, the fanboys keep demanding we comment on the ratings. Every other day, it seems, NBC or the NBA releases another celebratory graphic touting viewership. The Western Conference Finals are averaging 9.4 million viewers across NBC and Peacock, making it the most-watched Western Conference Finals on record through three games, NBC posted on X on Thursday. The network also said that Thunder-Spurs Game 4 on Sunday delivered a total audience of 10.3 million viewers, making it the most-watched Western Conference Finals Game 4 since 1999.

AI regulation is seen as urgent by nearly 8 in 10 voters in a new poll, with 80% saying protecting public interests should be prioritized over promoting innovation.

Paxton trounces Cornyn in the Texas Senate Republican primary runoff; Trump waffles between a losing "peace deal" and a return to war in Iran; and congressional candidate Alex Bores makes the case for AI regulation. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories--and eventually replace human labor altogether. If you could buy a humanoid robot for less than a smartphone, would you? Would you buy several robots to handle cooking, cleaning, babysitting, and even your job? This is the pitch being made by Zhou Yong, the 40-year-old founder and chief technology officer of LinkerBot, one of China's leading manufacturers of dexterous humanoid hands.