Rogue Republican calls Trump presidency the'Epstein administration' amid criticism of Pam Bondi Mystery buyer of Epstein's Zorro Ranch is revealed to be Texas politician running for election Scandal engulfs America's'most hated podcast': Insiders torch Nick Viall... but save the ugliest whispers for his much-younger wife The tragic truth about what happened to the quintuplets who won America's hearts America's top 10 most generous billionaires are revealed Ritzy LA neighborhood where yoga moms are scandalized by den of iniquity... then go home to letters about what their brazen husbands are up to Ex-UFC star Cain Velasquez reunites with family as he's released after being jailed over attempted murder of man accused of sexually assaulting his son Meet'Donor Dan' who promises dads an international life of luxury... but only if they meet his incredibly high standards Disney's staggering nine-figure loss laid bare after its woke Snow White movie with Rachel Zegler flopped spectacularly Terrifying moment'drunk driver' is brought to his knees in high-speed chase with cops Meghan Markle cozies up to Prince Harry as they enjoy courtside date night at NBA All-Star Game over Valentine's Day weekend Harrison Ford, 83, and Calista Flockhart, 61, kiss on the tarmac as they arrive back in LA on Valentine's Day How the daughter of a Real Housewife laid bare an ugly truth at the heart of Hollywood... and exposed the depth of the damage it's done I'm a celebrity security guard here's where I'd never let my teenagers go for spring break David Harbour skips Stranger Things costar Maya Hawke's wedding as he goes on Valentine's date with mystery woman My wife thinks her surprise to me is every man's dream... but I'm disgusted by what she's offering to do: DEAR JANE I exposed the only US city erased from Google Maps... here's what this ultra-wealthy community doesn't want you to see Only 20% of people can solve this three-question IQ test backed by MIT... are you one of them? The world's shortest IQ test is just three questions long and can tell if you're smarter than 80 percent of the population. Called the Cognitive Reflection Test ( CRT), it has been around since 2005 but recently gained popularity on social media, with one TikTok user's breakdown of the three questions getting 14million views. The test was created by psychologist Shane Frederick, now at the Yale School of Management, to help predict whether people are likely to make common mistakes in thinking and decision-making. Since its creation, multiple studies over the last two decades have tested thousands of college students, finding that less than 20 percent can get all three right.

Comedian Bill Maher and actor Tim Allen criticized DEI practices during the "Club Random" podcast, arguing diversity initiatives shouldn't impede creative processes.

Large language models exhibit intelligence without genuine epistemic understanding, exposing a key gap: the absence of epistemic architecture. This paper introduces the Structured Cognitive Loop (SCL) as an executable epistemological framework for emergent intelligence. Unlike traditional AI research asking "what is intelligence?" (ontological), SCL asks "under what conditions does cognition emerge?" (epistemological). Grounded in philosophy of mind and cognitive phenomenology, SCL bridges conceptual philosophy and implementable cognition. Drawing on process philosophy, enactive cognition, and extended mind theory, we define intelligence not as a property but as a performed process -- a continuous loop of judgment, memory, control, action, and regulation. SCL makes three contributions. First, it operationalizes philosophical insights into computationally interpretable structures, enabling "executable epistemology" -- philosophy as structural experiment. Second, it shows that functional separation within cognitive architecture yields more coherent and interpretable behavior than monolithic prompt based systems, supported by agent evaluations. Third, it redefines intelligence: not representational accuracy but the capacity to reconstruct its own epistemic state through intentional understanding. This framework impacts philosophy of mind, epistemology, and AI. For philosophy, it allows theories of cognition to be enacted and tested. For AI, it grounds behavior in epistemic structure rather than statistical regularity. For epistemology, it frames knowledge not as truth possession but as continuous reconstruction within a phenomenologically coherent loop. We situate SCL within debates on cognitive phenomenology, emergence, normativity, and intentionality, arguing that real progress requires not larger models but architectures that realize cognitive principles structurally.

In recent years we observed rapid and significant advancements in artificial intelligence (A.I.). So much so that many wonder how close humanity is to developing an A.I. model that can achieve human level of intelligence, also known as artificial general intelligence (A.G.I.). In this work we look at this question and we attempt to define the upper bounds, not just of A.I., but rather of any machine-computable process (a.k.a. an algorithm). To answer this question however, one must first precisely define A.G.I. We borrow prior work's definition of A.G.I. [1] that best describes the sentiment of the term, as used by the leading developers of A.I. That is, the ability to be creative and innovate in some field of study in a way that unlocks new and previously unknown functional capabilities in that field. Based on this definition we draw new bounds on the limits of computation. We formally prove that no algorithm can demonstrate new functional capabilities that were not already present in the initial algorithm itself. Therefore, no algorithm (and thus no A.I. model) can be truly creative in any field of study, whether that is science, engineering, art, sports, etc. In contrast, A.I. models can demonstrate existing functional capabilities, as well as combinations and permutations of existing functional capabilities. We conclude this work by discussing the implications of this proof both as it regards to the future of A.I. development, as well as to what it means for the origins of human intelligence.

We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM). Unlike existing foundation models (FMs), which specialize in pattern learning within specific domains such as language, vision, or time series, IFM aims to acquire the underlying mechanisms of intelligence by learning directly from diverse intelligent behaviors. Vision, language, and other cognitive abilities are manifestations of intelligent behavior; learning from this broad range of behaviors enables the system to internalize the general principles of intelligence. Based on the fact that intelligent behaviors emerge from the collective dynamics of biological neural systems, IFM consists of two core components: a novel network architecture, termed the state neural network, which captures neuron-like dynamic processes, and a new learning objective, neuron output prediction, which trains the system to predict neuronal outputs from collective dynamics. The state neural network emulates the temporal dynamics of biological neurons, allowing the system to store, integrate, and process information over time, while the neuron output prediction objective provides a unified computational principle for learning these structural dynamics from intelligent behaviors. Together, these innovations establish a biologically grounded and computationally scalable foundation for building systems capable of generalization, reasoning, and adaptive learning across domains, representing a step toward truly AGI.

Human achievement, whether in culture, science, or technology, is unparalleled in the known existence. This achievement is tied to the enormous communities of knowledge, made possible by language: leaving theological content aside, it is very much true that "in the beginning was the word", and that in Western societies, this became particularly identified with the written word. There lies the challenge regarding modern age chatbots: they can 'do' language apparently as well as ourselves and there is a natural question of whether they can be considered intelligent, in the same way as we are or otherwise. Are humans uniquely intelligent? We consider this question in terms of the psychological literature on intelligence, evidence for intelligence in non-human animals, the role of written language in science and technology, progress with artificial intelligence, the history of intelligence testing (for both humans and machines), and the role of embodiment in intelligence. We think that it is increasingly difficult to consider humans uniquely intelligent. There are current limitations in chatbots, e.g., concerning perceptual and social awareness, but much attention is currently devoted to overcoming such limitations.

Benchmarks such as ARC, Raven-inspired tests, and the Blackbird Task are widely used to evaluate the intelligence of large language models (LLMs). Yet, the concept of intelligence remains elusive- lacking a stable definition and failing to predict performance on practical tasks such as question answering, summarization, or coding. Optimizing for such benchmarks risks misaligning evaluation with real-world utility. Our perspective is that evaluation should be grounded in generality rather than abstract notions of intelligence. We identify three assumptions that often underpin intelligence-focused evaluation: generality, stability, and realism. Through conceptual and formal analysis, we show that only generality withstands conceptual and empirical scrutiny. Intelligence is not what enables generality; generality is best understood as a multitask learning problem that directly links evaluation to measurable performance breadth and reliability. This perspective reframes how progress in AI should be assessed and proposes generality as a more stable foundation for evaluating capability across diverse and evolving tasks.

Epstein's ultimate betrayal of Trump as emails reveal billionaire's twisted plot against president: 'I am the one able to take him down' Father of cheerleader who mysteriously died on Carnival cruise speaks out on investigation... and reveals the horrific theories he's heard I tried the'magic' pill that claims to cure migraines, back pain, anxiety and insomnia. The relief was instant... and it costs just $25 a month The REAL reason why Prince Harry and Meghan Markle photos were removed from Kris Jenner's birthday posts Kim Kardashian's daughter North West, 12, shocks fans with'high-risk piercing' not suitable for kids Alex Murdaugh's housekeeper says she KNEW the lawyer killed his wife and son in bombshell new book Civil rights leader Rev. Jesse Jackson hospitalized in Chicago Donald Trump leaves Ozzy Osbourne's widow Sharon in tears after paying tribute to the late rocker Kelly Clarkson's staff'feel like s***': TV insiders reveal star's huge backstage transformation after death of ex-husband He killed his daughter, 2, in a hot car then committed suicide on day he was due to be jailed. Then she tried to have her rich husband assassinated. Epstein's mysterious falling out with Clinton is revealed in emails to Obama lawyer inviting her to his infamous NYC townhouse John Travolta's son Benjamin, 14, has grown into his spitting image as Grease star proudly shares new clip Sober Dolphins coach Mike McDaniel'indebted' to Commanders' Dan Quinn for helping him beat drinking problem Diddy has prison release date pushed BACK amid allegations of'drinking moonshine' Three winters into Putin's savage war, his battered army is devouring itself. Trump makes sordid joke about Muslim president's WIFE at the White House The Navy commander who stared down Al Qaeda on the USS Cole has a new enemy... and a chilling warning for America Scientists have cracked the code behind a mysterious language discovered among a non-human species living in Earth's oceans that mirrors human speech.

Improvisation--the art of spontaneous creation that unfolds moment-to-moment without a scripted outcome--requires practitioners to continuously sense, adapt, and create anew. It is a fundamental mode of human creativity spanning music, dance, and everyday life. The open-ended nature of improvisation produces a stream of novel, unrepeatable moments--an aspect highly valued in artistic creativity. In parallel, open-endedness (OE)--a system's capacity for unbounded novelty and endless "interestingness"--is exemplified in natural or cultural evolution and has been considered "the last grand challenge" in artificial life (ALife). The rise of generative AI now raises the question in computational creativity (CC) research: What makes a "good" improvisation for AI? Can AI learn to improvise in a genuinely open-ended way? In this work-in-progress paper, we report insights from in-depth interviews with 6 experts in improvisation across dance, music, and contact improvisation. We draw systemic connections between human improvisa-tional arts and the design of future experiential AI agents that could improvise alone or alongside humans--or even with other AI agents--embodying qualities of improvisation drawn from practice: active listening (umwelt and awareness), being in the time (mindfulness and ephemerality), embracing the unknown (source of randomness and serendipity), non-judgmental flow (acceptance and dynamical stability, balancing structure and surprise (unpredictable criticality at edge of chaos), imaginative metaphor (synaesthesia and planning), empathy, trust, boundary, and care (mutual theory of mind), and playfulness and intrinsic motivation (maintaining interestingness).

A.I. tools are getting better at producing convincing images, text, and videos. Does that mean they can make art? Generative A.I., once an uncanny novelty, is now being used to create not only images and videos but entire "artists." Its boosters claim that the technology is merely a tool to facilitate human creativity; the major use cases we've seen thus far--and the money being poured into these projects--tell a different story. On this episode of Critics at Large, Vinson Cunningham, Naomi Fry, and Alexandra Schwartz discuss the output of Timbaland's A.I. rapper TaTa Taktumi and the synthetic actress Tilly Norwood.