It came earlier than expected. More than a year before his recent standoff with the Pentagon, Dario Amodei, the chief executive of Anthropic, published a 15,000-word manifesto describing a glorious AI future. Its title, "Machines of Loving Grace," is borrowed from a Richard Brautigan poem, but as Amodei acknowledged, with some embarrassment, its utopian vision bears some resemblance to science fiction. According to Amodei, we will soon create the first polymath AIs with abilities that surpass those of Nobel Prize winners in "most relevant fields," and we'll have millions of them, a "country of geniuses," all packed into the glowing server racks of a data center, working together. With access to tools that operate directly on our physical world, these AIs would be able to get up to a great deal of dangerous mischief, but according to Amodei, if they're developed--or "grown," as staffers at Anthropic are fond of saying--in the correct way, they will decide to greatly improve our lives. Amodei does not explain precisely how the AIs will accomplish this.

Minamitorishima is nearly 1,250 miles east of Tokyo. The island is surrounded by a coral atoll and is only 0.6 miles wide. Breakthroughs, discoveries, and DIY tips sent six days a week. Nuclear power is on the rise around the world, but with it comes an extremely pressing question: where will all of the radioactive waste be stored? For Japan, one answer may lie in literally the most remote location at their disposal.

Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Shocking declassified files have revealed how the US government intentionally injected Americans with radioactive substances without their knowledge or consent. This happened to 18 hospital patients between 1945 and 1947, where doctors secretly administered plutonium to study how it moved through and affected the human body as part of early US nuclear experiments during World War II and the Cold War. The chilling details originally came to light in 1995, when the Clinton White House had the Department of Energy disclose the secret experiments aimed at understanding radiation risks to workers building atomic bombs.

Stochastic network models play a central role across a wide range of scientific disciplines, and questions of statistical inference arise naturally in this context. In this paper we investigate goodness-of-fit and two-sample testing procedures for statistical networks based on the principle of maximum entropy (MaxEnt). Our approach formulates a constrained entropy-maximization problem on the space of networks, subject to prescribed structural constraints. The resulting test statistics are defined through the Lagrange multipliers associated with the constrained optimization problem, which, to our knowledge, is novel in the statistical networks literature. We establish consistency in the classical regime where the number of vertices is fixed. We then consider asymptotic regimes in which the graph size grows with the sample size, developing tests for both dense and sparse settings. In the dense case, we analyze exponential random graph models (ERGM) (including the Erdös-Rènyi models), while in the sparse regime our theory applies to Erd{ö}s-R{è}nyi graphs. Our analysis leverages recent advances in nonlinear large deviation theory for random graphs. We further show that the proposed Lagrange-multiplier framework connects naturally to classical score tests for constrained maximum likelihood estimation. The results provide a unified entropy-based framework for network model assessment across diverse growth regimes.

We use the discrete variablezd,t to represent the topic that thetth word is generated from.

Forexample, areal-time communications service maybeinterested in tuning the parameters of a control policy to adapt video quality in real time in order to maximize video quality and minimize latency [10, 17].

Inthis paper,we provide ageneralization error bound applicable for graph embedding both in linear and hyperbolic spaces under various negative sampling settings that appear in graph embedding.

Gittins index, which can be reinterpreted as an acquisition function.

Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SL T datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SL T.