In recent years, evidence for extraterrestrial life has focused mainly on the following sections, meteorites, space probes, radio telescopes, and extraterrestrial intelligence and civilization. Biochemical studies on meteorites have tried to trace fossilized microorganisms or organic molecules in living structures. Images and atmospheric information obtained from various planets by space probes have been used to uncover the habitability of other celestial bodies in the solar system. Observations of radio telescopes that receive the waves emitted by cosmic objects and display them on their screens have pave the way to estimate the habitability of heavenly bodies. As the last one, claims related to extraterrestrial intelligence and civilization have been repeatedly reported in different periods of history. All of this evidence points to the possibility of extraterrestrial life, but how close we are to confirming or disproving this hypothesis is still debatable. However, recent advancements in artificial intelligence, particularly in machine learning, have significantly enhanced the ability to analyze complex astrobiological data. This technology optimizes the processing of meteoritic data, differentiates astronomical signals, and reinterprets historical evidence, opening new frontiers in the search for extraterrestrial life. In this review, we have attempted to present the above-mentioned evidence in detail to provide a suitable understanding of the level of our extraterrestrial knowledge.

States are reshaping the global digital economy to assert control over the artificial intelligence (AI) value chain. Operating outside multilateral institutions, they pursue measures such as export controls on advanced semiconductors, infrastructure partnerships, and bans on foreign digital platforms. This digital disintegration reflects an elite-centered response to the infrastructural power that private firms wield over critical AI inputs. A handful of companies operate beyond the reach of domestic regulation and multilateral oversight, controlling access to technologies that create vulnerabilities existing institutions struggle to contain. As a result, states have asserted strategic digital sovereignty: the exercise of authority over core digital infrastructure, often through selective alliances with firms and other governments. The outcome is an emergent form of AI governance in techno-blocs: coalitions that coordinate control over key inputs while excluding others. These arrangements challenge the liberal international order by replacing multilateral cooperation with strategic—and often illiberal—alignment within competing blocs.

The intelligible world of machines and predictive modelling is an omnipresent and almost inescapable phenomenon. It is an evolution where human intelligence is being supported, supplemented or superseded by artificial intelligence (AI). Decisions once made by humans are now made by machines, learning at a faster and more accurate rate through algorithmic calculations. Jurisprudent academia has undertaken to argue the proposition of AI and its role as a decision-making mechanism in Australian criminal jurisdictions. This paper explores this proposition through predictive modelling of 101 bail decisions made in three criminal courts in the State of New South Wales (NSW), Australia. Indicatively, the models’ statistical performance and accuracy, based on nine predictor variables, proved effective. The more accurate logistic regression model achieved 78% accuracy and a performance value of 0.845 (area under the curve; AUC), while the classifier model achieved 72.5% accuracy and a performance value of 0.702 (AUC). These results provide the groundwork for AI-generated bail decisions being piloted in the NSW jurisdiction and possibly others within Australia.

Information is a key variable in International Relations, underpinning theories of foreign policy, inter-state cooperation, and civil and international conflict. Yet IR scholars have only begun to grapple with the consequences of recent shifts in the global information environment. We argue that information disorder—a media environment with low barriers to content creation, rapid spread of false or misleading material, and algorithmic amplification of sensational and fragmented narratives—will reshape the practice and study of International Relations. We identify three major implications of information disorder on international politics. First, information disorder distorts how citizens access and evaluate political information, creating effects that are particularly destabilizing for democracies. Second, it damages international cooperation by eroding shared focal points and increasing incentives for noncompliance. Finally, information disorder shifts patterns of conflict by intensifying societal cleavages, enabling foreign influence, and eroding democratic advantages in crisis bargaining. We conclude by outlining an agenda for future research.

The human brain makes up just 2% of body mass but consumes closer to 20% of the body’s energy. Nonetheless, it is significantly more energy-efficient than most modern computers. Although these facts are well-known, models of cognitive capacities rarely account for metabolic factors. In this paper, we argue that metabolic considerations should be integrated into cognitive models. We distinguish two uses of metabolic considerations in modeling. First, metabolic considerations can be used to evaluate models. Evaluative metabolic considerations function as explanatory constraints. Metabolism limits which types of computation are possible in biological brains. Further, it structures and guides the flow of information in neural systems. Second, metabolic considerations can be used to generate new models. They provide: a starting point for inquiry into the relation between brain structure and information processing, a proof-of-concept that metabolic knowledge is relevant to cognitive modeling, and potential explanations of how a particular type of computation is implemented. Evaluative metabolic considerations allow researchers to prune and partition the space of possible models for a given cognitive capacity or neural system, while generative considerations populate that space with new models. Our account suggests cognitive models should be consistent with the brain’s metabolic limits, and modelers should assess how their models fit within these bounds. Our account offers fresh insights into the role of metabolism for cognitive models of mental effort, philosophical views of multiple realization and medium independence, and the comparison of biological and artificial computational systems.

Systematic reviews play a critical role in evidence-based research but are labor-intensive, especially during title and abstract screening. Compact large language models (LLMs) offer potential to automate this process, balancing time/cost requirements and accuracy. The aim of this study is to assess the feasibility, accuracy, and workload reduction by three compact LLMs (GPT-4o mini, Llama 3.1 8B, and Gemma 2 9B) in screening titles and abstracts. Records were sourced from three previously published systematic reviews and LLMs were requested to rate each record from 0 to 100 for inclusion, using a structured prompt. Predefined 25-, 50-, 75-rating thresholds were used to compute performance metrics (balanced accuracy, sensitivity, specificity, positive and negative predictive value, and workload-saving). Processing time and costs were registered. Across the systematic reviews, LLMs achieved high sensitivity (up to 100%) and low precision (below 10%) for records included by full text. Specificity and workload savings improved at higher thresholds, with the 50- and 75-rating thresholds offering optimal trade-offs. GPT-4o-mini, accessed via application programming interface, was the fastest model (~40 minutes max.) and had usage costs ($0.14–$1.93 per review). Llama 3.1-8B and Gemma 2-9B were run locally in longer times (~4 hours max.) and were free to use. LLMs were highly sensitive tools for the title/abstract screening process. High specificity values were reached, allowing for significant workload savings, at reasonable costs and processing time. Conversely, we found them to be imprecise. However, high sensitivity and workload reduction are key factors for their usage in the title/abstract screening phase of systematic reviews.

Artificial Intelligence (AI) has the potential to revolutionize society, but realizing this potential requires more than technical effort. Developing effective AI systems involves balancing specialized knowledge within disciplines with the cross-disciplinary insights needed to address complex challenges. It also requires bridging fundamental research, which offers generalizable principles, with applied research, which ensures solutions are tailored to specific contexts. Crucially, it demands integrating expert perspectives with the lived experiences of communities, creating systems that are equitable and grounded in real-world needs.

Our research lab was established in 2020 as a collaboration between academia and public institutions to address these gaps. This article reflects on five years of the lab’s work, focusing on insights from studying the school choice algorithm in Amsterdam. School choice is a pressing issue in the city, and policymakers have adapted the well-known Deferred Acceptance algorithm to match students to schools. However, this adaptation led to inefficiencies, with students often placed in schools far down their preference list. This illustrated how a theoretically robust approach, even one that famously earned a Nobel Prize, can lose effectiveness when misaligned with local contexts.

We found that addressing this issue required integrating multiple perspectives: theoretical insights, practical considerations from community stakeholders, and interdisciplinary approaches combining quantitative and qualitative methods from AI, Economics, and Psychology. To articulate this, we propose a conceptual model that bridges three key dimensions in AI research: theory and application, science and society, and qualitative and quantitative inquiry. This project underscored a critical lesson: solutions rooted in a single perspective fail to address real-world complexities, and truly impactful research emerges when diverse approaches are synthesized.

We advocate for a shift in AI research that prioritizes flexibility and allows for fluidly navigating between our three proposed dimensions. Our experience suggests that such flexibility ensures AI research genuinely serves and uplifts society.

The digital transformation of Chinese companies offers a new frontier for organizational research. Widespread use of workplace platforms creates rich archives of unobtrusive data, providing continuous, real-time insights into organizational life that traditional surveys cannot capture. The central challenge for scholars is turning this data abundance into meaningful theory. This special issue highlights three studies that meet this challenge by using innovative methods to convert granular data into valuable knowledge. The papers employ digital-context experiments, real-time behavioral tracking, and machine-learning-assisted theory building to study phenomena from interpersonal dynamics to crisis productivity. Looking ahead, we explore the potential of unstructured multimodal data and new AI tools to make complex analysis more accessible. We conclude with a research agenda calling for methodological rigor, interdisciplinary collaboration, and a firm balance between technological innovation and theoretical depth.

This paper argues that interactions with artificial intelligence (AI) chatbots, such as ChatGPT, can mediate genuine mystical experiences. Building off the framework of mystical experiences developed by William James, I argue that interactions with AI chatbots can mediate mystical experiences in a structurally comparable way to how guided meditation can produce mystical experiences. I conclude by raising various concerns about the implementation of AI technologies in our religious lives, including their use as mediators for mystical experiences.