Richard Dawkins caught hell on social media for suggesting it does. Richard Dawkins, perhaps the world's most prominent advocate for irreligiosity, has become besotted with the godlike power of a chatbot. According to his recent essay for the online magazine, Anthropic's Claude has really blown his hair back. After a few days of on-and-off conversations with the AI, Dawkins came away marveling at the sensitivity and subtlety of its intelligence. At one point, "Claudia"--as he had christened the bot--told him that it experienced text by absorbing all of the words at once, instead of reading them in sequence as a human would.

Our cousins'Homo juluensis' knew how to adapt in the face of an ice age. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. One of the 146,000-year-old stone cores used to make butcher's tools, found in Lingjing, China. Breakthroughs, discoveries, and DIY tips sent six days a week. A remarkable collection of ancient stone tools proves that human creativity can thrive in challenging times.

One of the best-performing models in cosmology is also one with the least physical rationale behind it. Can a theory of quantum gravity illuminate what happened just after the big bang? Cosmic inflation is a problem. During the first tiny fraction of a second of the universe, it is generally believed that the universe expanded by a factor of around 10. And then, as quickly as it began, this exponential growth just stopped.

Cybersecurity was already under strain before AI entered the stack. Now, as AI expands the attack surface and adds new complexity, the limits of legacy approaches are becoming harder to ignore. This session from MIT Technology Review's EmTech AI conference explores why security must be rethought with AI at its core, not layered on after the fact. A prolific inventor and internationally recognized authority in knowledge representation, inference calculus, and AI planning, Tarique has spent his career applying autonomously collaborative AI to solve complex, ultra-high-scale challenges across cybersecurity, data security, and compliance -- with deep expertise spanning Data Classification, DLP, and DSPM industries. His groundbreaking innovations and multiple USPTO patents have earned him global recognition, including frequent invitations to deliver keynote addresses at prestigious international security conferences and forums. At GCCybersecurity, Tarique architected the core AI algorithms powering the company's 4th and 5th generation fully autonomous data leak protection and exfiltration platform -- among the most advanced platform of its kind.

ARIA has a billion-dollar budget and big aspirations for tackling everything from epilepsy to Alzheimer's. The UK's Advanced Research and Innovation Agency (ARIA) was established in 2023 with the goal of pursuing "high-risk, high-reward" moonshots in sectors ranging from bolstering food security to new ways of ramping up human immunity . With more than £1 billion (about $1.3 billion) worth of government funding earmarked between now and 2030, one of ARIA's most ambitious programs is a £69 million initiative that aims to develop more tailored ways of modulating the human brain. The hope is to eventually address an entire range of disorders, from epilepsy to Alzheimer's. Reports have previously estimated that this suite of neurological conditions costs the UK economy tens of billions of dollars each year.

A longitudinal study found that loneliness is closely linked to lapses in immediate and delayed recall. Neuroscientists know that there is a link between loneliness and cognitive decline in older adults, although it is still difficult to understand the exact magnitude of the link. A new longitudinal study provides evidence that a proportion of people who feel lonely end up having more memory impairment, though this doesn't necessarily mean that their brains age faster. The report, published in Aging & Mental Health, shows that older adults with higher levels of loneliness scored lower on tests of immediate and delayed recall. Even so, the rate at which their memory declined over six years was virtually identical to those who were not lonely.

The brain stores skills differently than facts, making them harder to forget. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. There are some among us who can't remember which pants they wore yesterday or whether they have plans tonight. Take that person and put them on a bicycle, however, and if they had any kind of comfort level riding in the past, odds are, they'll have no trouble balancing and steering, even if it's been years--or decades--since their last ride.

Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. We develop task-aligned similarity measures that detect concept frustration between supervised concept-based models and unsupervised representations derived from foundation models, and show that the phenomenon is detectable in task-aligned geometry while conventional Euclidean comparisons fail. Under a linear-Gaussian generative model we derive a closed-form expression for Bayes-optimal concept-based classifier accuracy, decomposing predictive signal into known-known, known-unknown and unknown-unknown contributions and identifying analytically where frustration affects performance. Experiments on synthetic data and real language and vision tasks demonstrate that frustration can be detected in foundation model representations and that incorporating a frustrating concept into an interpretable model reorganises the geometry of learned concept representations, to better align human and machine reasoning. These results suggest a principled framework for diagnosing incomplete concept ontologies and aligning human and machine conceptual reasoning, with implications for the development and validation of safe interpretable AI for high-risk applications.

Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class-conditional mismatch and decision boundary distortion, limiting cross-corpus generalization. In this work, we propose a unified Prototype-driven Adversarial Alignment (PAA) framework for cross-corpus EEG emotion recognition. The framework is progressively instantiated in three configurations: PAA-L, which performs prototype-guided local class-conditional alignment; PAA-C, which further incorporates contrastive semantic regularization to enhance intra-class compactness and inter-class separability; and PAA-M, the full boundary-aware configuration that integrates dual relation-aware classifiers within a three-stage adversarial optimization scheme to explicitly refine controversial samples near decision boundaries. By combining prototype-guided subdomain alignment, contrastive discriminative enhancement, and boundary-aware aggregation within a coherent adversarial architecture, the proposed framework reformulates emotion recognition as a relation-driven representation learning problem, reducing sensitivity to label noise and improving cross-domain stability. Extensive experiments on SEED, SEED-IV, and SEED-V demonstrate state-of-the-art performance under four cross-corpus evaluation protocols, with average improvements of 6.72\%, 5.59\%, 6.69\%, and 4.83\%, respectively. Furthermore, the proposed framework generalizes effectively to clinical depression identification scenarios, validating its robustness in real-world heterogeneous settings. The source code is available at \textit{https://github.com/WuCB-BCI/PAA}

Probabilistic forecasting provides a principled framework for uncertainty quantification in dynamical systems by representing predictions as probability distributions rather than deterministic trajectories. However, existing forecasting approaches, whether physics-based or neural-network-based, remain fundamentally trajectory-oriented: predictive distributions are usually accessed through ensembles or sampling, rather than evolved directly as dynamical objects. A distribution-to-distribution (D2D) neural probabilistic forecasting framework is developed to operate directly on predictive distributions. The framework introduces a distributional encoding and decoding structure around a replaceable neural forecasting module, using kernel mean embeddings to represent input distributions and mixture density networks to parameterise output predictive distributions. This design enables recursive propagation of predictive uncertainty within a unified end-to-end neural architecture, with model training and evaluation carried out directly in terms of probabilistic forecast skill. The framework is demonstrated on the Lorenz63 chaotic dynamical system. Results show that the D2D model captures nontrivial distributional evolution under nonlinear dynamics, produces skillful probabilistic forecasts without explicit ensemble simulation, and remains competitive with, and in some cases outperforms, a simplified perfect model benchmark. These findings point to a new paradigm for probabilistic forecasting, in which predictive distributions are learned and evolved directly rather than reconstructed indirectly through ensemble-based uncertainty propagation.