We present a category theoretical formulation of the Monetary Macroeconomic Accounting Theory (MoMaT) of Menéndez and Winschel [2025]. We take macroeconomic (national) accounting systems to be composed from microeconomic double-entry systems with real and monetary units of accounts. Category theory is the compositional grammar and module system of mathematics which we use to lift micro accounting consistency to the macro level. The main function of money in MoMaT is for the repayment of loans and not for the exchange of goods, bridging the desynchronisation of input and output payments of producers. Accordingly, temporal accounting consistency is at the macroeconomic level. We show that the accounting for macroeconomies organised by a division of labor can be consistent and stable as a prerequisite for risk and GDP sharing of societies. We exemplify the theory by five sectoral agents of Labor and Resource owners, a Company as the productive sector, a Capitalist for profits, and a Bank as the financial sector providing loans to synchronise the micro and the macro levels of an economy. The dynamics is described by eight sectoral macroeconomic bookings in each period demonstrating stable convergence of the MoMaT in numerical simulations. The categorical program implements a consistent evolution of hierarchical loan repayment contracts by an endofunctor. The universal constructions of a limit verify all constraints as the sectoral investment and learning function at the macroeconomic level. The dual colimit computes the aggregated informations at the macro level as usual in the mathematics of transitions from local to global structures. We use visual diagrams to make complex economic relationships intuitive. This paper is meant to map economic to categorical concepts to enable interdisciplinary collaboration for digital twins of monetary accounting systems.

Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Y et, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains debatable. In this paper, we revisit cooperative game theory from an interpretability perspective and argue for a broader and more principled use of its tools. We highlight two general families of efficient allocations, the Weber and Harsanyi sets, that extend beyond Shapley values and offer richer interpretative flexibility. We present an accessible overview of these allocation schemes, clarify the distinction between value functions and aggregation rules, and introduce a three-step blueprint for constructing reliable and theoretically-grounded feature attributions. Our goal is to move beyond fixed axioms and provide the XAI community with a coherent framework to design attribution methods that are both meaningful and robust to shifting methodological trends.

Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.

OpenAI CEO Sam Altman expects AGI, or artificial general intelligence--AI that outperforms humans at most tasks--around 2027 or 2028. Elon Musk's prediction is either 2025 or 2026, and he has claimed that he was "losing sleep over the threat of AI danger." As the limitations of current AI become increasingly clear, most AI researchers have come to the view that simply building bigger and more powerful chatbots won't lead to AGI. This story is from the WIRED World in 2025, our annual trends briefing. However, in 2025, AI will still pose a massive risk: not from artificial superintelligence, but from human misuse.

In his classic essay, "The Normative Structure of Science," sociologist Robert K. Merton identified universalism as a foundational principle of the scientific enterprise, one that distinguishes science from other competing systems of knowing. According to Merton and Storer's formulation (Merton and Storer, 1973, p. 270), universalism holds that the evaluation of scientific contributions "is not to depend on the personal or social attributes of their protagonist; his race, nationality, religion, class, and personal qualities are as such irrelevant." The value of universalism is manifested perhaps most concretely in the practice of double-blind peer review, wherein the identities of both those making scientific claims and those evaluating them are obscured from one another (Bornmann, 2011). While scholars have long observed that adherence to the principle of universalism is far from universal (Mulkay, 1976; Cole, 1992; Long and Fox, 1995), the growing availability of large-scale databases is creating opportunities for unprecedented insight into processes of scientific evaluation (Teplitskiy et al., 2018; Dondio et al., 2019; Lane et al., 2021), including the barriers that inhibit objective assessments. Recent literature in particular has raised considerable concern about the role of gender in scientific evaluation (Moss-Racusin et al., 2012; Reuben et al., 2014; Oliveira et al., 2019; Card et al., 2020a).

Google's parent company has hit a stock market value of 2tn ( 1.6tn) as investors reacted to a declaration of its first ever dividend alongside strong results on Thursday. Shares in Alphabet rose 10% in early Wall Street trading on Friday to give the tech group a stock market capitalisation – a measure of a corporation's value – of more than 2tn. Alphabet last hit that level in intraday trading in 2021, but has yet to close above that benchmark after a day's trading. Alphabet's shares rose after it posted results on Thursday that exceeded analyst's expectations. Microsoft also reported strong figures on Thursday, amid heavy investment in artificial intelligence, and investors pushed the company past the 3tn mark, a level it has already crossed this year.

Shares in Alphabet, the owner of Google and YouTube, surged after it issued its first ever dividend and revealed that profits had surged in the last quarter. Sundar Pichai, CEO, hailed the transition to artificial intelligence as a "once-in-a-generation opportunity" as his company races to integrate the technology across its business. Investors cheered the firm's earnings, and news of a 70bn stock buyback. Google posted 80.5bn in revenue for the first quarter of 2024, up 15% on the same period last year, and reported 1.89 in earnings per share, up from 1.17 – surpassing analysts' expectations on both counts. Shares in Alphabet were up roughly 15% in after-hours trading.

Decoder transformers have continued increasing in scale reaching hundreds of billions of parameters. Due to their scale the same decoder sets state-of-the-art results on various language tasks via prompting or fine-tuning. Yet, these large foundation models remain unusable for the related fields of semantic search and sentence embeddings. This prevents possibly new state-of-the-art results and forces organizations to train and maintain separate models. To this end, we propose SGPT to use decoders for sentence embeddings and semantic search via prompting or fine-tuning. At 5.8 billion parameters SGPT improves on the previously best sentence embeddings by a margin of 7% and outperforms a concurrent method with 175 billion parameters as measured on the BEIR search benchmark. Code, models and result files are freely available at https://github.com/Muennighoff/sgpt.

In a world where seeing is increasingly no longer believing, experts are warning that society must take a multi-pronged approach to combat the potential harms of computer-generated media. As Bill Whitaker reports this week on 60 Minutes, artificial intelligence can manipulate faces and voices to make it look like someone said something they never said. The result is videos of things that never happened, called "deepfakes." Often, they look so real, people watching can't tell. Even Justin Bieber has been tricked by a series of deepfake videos on the social media video platform TikTok that appeared to be of Tom Cruise.

Sophisticated entrepreneurs and parents are in the early stages of mass market growth. The Artificial Intelligence (AI) software market is expected to grow 21% this year, while the overall AI market is expected to grow at a CAGR of 40% by 2028. Investors will be hard pressed to find another market with the expected strength. The key players in this market are Nvidia (NVDA-2.10%) and IBM (IBM 0.10%), both of which are moving towards enabling more efficient and automated lifestyles with potential for improvement and life-saving potential. Both can provide the resources needed to build your long-term portfolio.