Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be interpreted as the agent's estimate of the probability of its death. We formally define death for generally intelligent agents like AIXI, and prove a number of related theorems about their behaviour. Notable discoveries include that agent behaviour can change radically under positive linear transformations of the reward signal (from suicidal to dogmatically self-preserving), and that the agent's posterior belief that it will survive increases over time.

As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-term dangers of job loss or threats of machines causing harm to humans. All of this concern, however, detracts from the more pertinent and already existing threats posed by AI today: its ability to amplify bias found in training datasets, and swiftly impact marginalized populations at scale. Government and public sector institutions have a responsibility to citizens to establish a dialogue with technology developers and release thoughtful policy around data standards to ensure diverse representation in datasets to prevent bias amplification and ensure that AI systems are built with inclusion in mind.

CRISPR-based genome editing has revolutionized biotechnology, yet optimizing guide RNA (gRNA) design for efficiency and safety remains a critical challenge. Recent advances (2020--2025, updated to reflect current year if needed) demonstrate that artificial intelligence (AI), especially deep learning, can markedly improve the prediction of gRNA on-target activity and identify off-target risks. In parallel, emerging explainable AI (XAI) techniques are beginning to illuminate the black-box nature of these models, offering insights into sequence features and genomic contexts that drive Cas enzyme performance. Here we review how state-of-the-art machine learning models are enhancing gRNA design for CRISPR systems, highlight strategies for interpreting model predictions, and discuss new developments in off-target prediction and safety assessment. We emphasize breakthroughs from top-tier journals that underscore an interdisciplinary convergence of AI and genome editing to enable more efficient, specific, and clinically viable CRISPR applications.

This paper leverages various philosophical and ontological frameworks to explore the concept of embodied artificial general intelligence (AGI), its relationship to human consciousness, and the key role of the metaverse in facilitating this relationship. Several theoretical frameworks underpin this exploration, such as embodied cognition, Michael Levin's computational boundary of a "Self," and Donald D. Hoffman's Interface Theory of Perception, which lead to considering human perceived outer reality as a symbolic representation of alternate inner states of being, and where AGI could embody a different form of consciousness with a larger computational boundary. The paper further discusses the necessary architecture for the emergence of an embodied AGI, how to calibrate an AGI's symbolic interface, and the key role played by the Metaverse, decentralized systems and open-source blockchain technology. The paper concludes by emphasizing the importance of achieving a certain degree of harmony in human relations and recognizing the interconnectedness of humanity at a global level, as key prerequisites for the emergence of a stable embodied AGI.

Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and bioengineering for precise navigation and targeted payload delivery, particularly for conditions like brain tumors, Alzheimer's disease, and Parkinson's disease. Recent progress in artificial intelligence (AI) and machine learning (ML) has improved the navigation and effectiveness of nanorobots, allowing them to detect and interact with cancer cells through biomarker analysis. This study presents a new reinforcement learning (RL) framework for optimizing nanorobot navigation in complex biological environments, focusing on cancer cell detection by analyzing the concentration gradients of surrounding biomarkers. We utilize a computer simulation model to explore the behavior of nanorobots in a three-dimensional space with cancer cells and biological barriers. The proposed method uses Q-learning to refine movement strategies based on real-time biomarker concentration data, enabling nanorobots to autonomously navigate to cancerous tissues for targeted drug delivery. This research lays the groundwork for future laboratory experiments and clinical applications, with implications for personalized medicine and less invasive cancer treatments. The integration of intelligent nanorobots could revolutionize therapeutic strategies, reducing side effects and enhancing treatment effectiveness for cancer patients. Further research will investigate the practical deployment of these technologies in medical settings, aiming to unlock the full potential of nanorobotics in healthcare.

The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s-50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin's The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.

General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs)1,2 and chain-of-thought (CoT) prompting3, have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent on extensive human-annotated demonstrations and the capabilities of models are still insufficient for more complex problems. Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-reflection, verification and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifiable tasks such as mathematics, coding competitions and STEM fields, surpassing its counterparts trained through conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically used to guide and enhance the reasoning capabilities of smaller models. A new artificial intelligence model, DeepSeek-R1, is introduced, demonstrating that the reasoning abilities of large language models can be incentivized through pure reinforcement learning, removing the need for human-annotated demonstrations.

Checco, A., L. Bracciale, P. Loreti, S. Pinfield, and G. Bianchi. 2021. AI-assisted Peer review. Humanities & Social Sciences Communications 8. doi:10.1057/s41599020-00703-8. Flaherty, C. 2022. The peer-review crisis. Inside Higher Ed [Online]. Available: https://www.insidehighered.com/ news/2022/06/13/peer-review-crisis-creates-problems-jou rnals-and-scholars [Accessed 06/08/2023]. Mclean, S., G. J. M. Read, J. Thompson, C. Baber, N. A. Stanton, and P. M. Salmon. 2023. The risks associated with artificial general intelligence: A systematic review. Journal of Experimental & Theoretical Artificial Intelligence 35 (5):649–63. doi:10.1080/0952813X.2021. 1964003. Nam, J., S. Mo, J. Lee, and J. Shin. 2023. Breaking the spurious causality of conditional generation via fairness intervention with corrective sampling. arXiv:2212.02090 [cs.CV]. doi:10.48550/arXiv.2212.02090. Porsdam Mann, S., B. D. Earp, N. Møller, S. Vynn, and J. Savulescu. 2023. AUTOGEN: A personalized large language model for academic enhancement—ethics and proof of principle. The American Journal of Bioethics 23 (10):28–41. doi:10.1080/15265161.2023.2233356. Weber-Wulff, D., A. Anohina-Naumeca, S. Bjelobaba, T. Folty nek, J. Guerrero-Dib, O. Popoola, P. Sigut, &, and L. Wadding. 2023. Testing of detection tools for AI-generated text. arXiv:2306.15666 [cs.CL]. doi:10.48550/arXiv. 2306.15666. Zupanc, G. K. H. 2023. It is becoming increasingly difficult to find reviewers”—Myths and facts about Peer review. Journal of Comparative Physiology A. doi:10.1007/s00359023-01642-w.

By introducing elements of information mining to tax analysis, by means of data mining software and advanced computational concepts of artificial intelligence, the problem of tax evader's crime against public property has been addressed. Through an empirical approach from a hypothetical case of use, induction algorithms, neural networks and bayesian networks are applied to determine the feasibility of its heuristic application by the tax public administrator. Different strategies are explored to facilitate the work of local and regional federal tax inspectors, considering their limited computational capabilities, but equally effective for those social scientist committed to handcrafting tax research. ----- Apresentando a introdução de elementos de exploração de informações para análise fiscal, por meio de software de mineração de dados e conceitos avançados computacionais de inteligência artificial, foi abordado o problema do crime de sonegador fiscal contra o patrimônio público. Através de uma abordagem empírica a partir de um caso hipotético de uso, os algoritmos de indução, redes neurais e redes bayesianas são aplicados para determinar a viabilidade de sua aplicação heurística pelo administrador público tributário. Diferentes estratégias são exploradas para facilitar o trabalho dos inspectores tributários federais locais e regionais, tendo em conta as suas capacidades computacionais limitados, mas igualmente eficaz para aqueles cientista social comprometido com a investigação fiscal.

Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers. MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware.