Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence ``Barack Obama was born in the United States'' is factually correct, though less informative than the factual sentence ``Barack Obama was born in Honolulu, Hawaii, United States''. Despite the known fact that LLMs tend to hallucinate and generate factually incorrect text, they might also tend to choose to generate factual text that is indeed factually correct and yet less informative than other, more informative choices. In this work, we tackle this problem by proposing an informativeness alignment mechanism. This mechanism takes advantage of recent factual benchmarks to propose an informativeness alignment objective. This objective prioritizes answers that are both correct and informative. A key finding of our work is that when training a model to maximize this objective or optimize its preference, we can improve not just informativeness but also factuality.

Height g rowth serves as a key health indicator, reflecting the interplay of genetic, environmental, and socioeconomic factors (Norris et al., 2022; Baxter - Jones et al., 2011; Hargreaves et al., 2022). Monitoring height growth enables early detection of disorders, facilitating timely interventions (Saari et al., 2015; Craig et al., 2011; Grote et al., 2008; Zhang et al., 2016). Accurate future height prediction is essential for diagnosing growth disorders, initiating hormone therapy, and evaluating treatment efficacy (Collett - Solberg et al., 2019; Ostojic, 2013; Cuttler & Silvers, 2004). Traditional height prediction methods rely on skeletal maturity assessment using hand - wrist radiographs. These include the Bayley - Pinneau (Bayley and Pinneau, 1952), Tanner - Whitehouse (Tanner et al., 1975), and Roche - Wainer - Thissen (Roche et al., 1975) met hods. However, these approaches have limitations including radia tion exposure, the need for specialized expertise, and high interobserver variability (Bull et al., 1999; Chávez - Vázquez et al., 2024; Prokop - Piotrkowska et al., 2021).

Mouse and human brains have different functions that depend on their neuronal networks. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. These structural features allow mouse neurons to be integrated in the limited space of the brain, though thin neurites should suppress distal connections according to cable theory. We implemented this mouse-mimetic constraint in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, and birds. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the four datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

Sentence transformers are language models designed to perform semantic search. This study investigates the capacity of sentence transformers, fine-tuned on general question-answering datasets for asymmetric semantic search, to associate descriptions of human-generated routes across Great Britain with queries often used to describe hiking experiences. We find that sentence transformers have some zero-shot capabilities to understand quasi-geospatial concepts, such as route types and difficulty, suggesting their potential utility for routing recommendation systems.

An AI has been studying the cookbooks and has taught itself how to make intriguing new pie recipes -- including Scotch egg pies and one with a salad filling. Working with a Sussex-based pie makers, the algorithm has produced thousands of recipes, five of which have been selected for production and will be going on sale. The AI works by looking for patterns in existing recipes and then trying to make its own based on what it learnt. While some of the early recipes it proposed were perhaps less-than-mouth-watering, with a little guidance it soon got the hang of cooking up new pie concepts. The experiment illustrates how artificial intelligence can provide new insights for small businesses and help dream up novel products to take to market.

For example, in Terminator XXVIII: Rise of the Earthlings (2051), a brave young android is tasked with saving the world from an army of killer humans sent from the future to destroy robotkind. Leading the human rebellion is Barry, an 18-stone unemployed bus driver from Caerphilly whose powers include the ability to eat a foot-long meatball marinara from Subway in under nine seconds. In the war zones of the future, robot generals will send human beings on to the battlefield to check for land mines and other unexploded devices. "Previously, this highly dangerous work was carried out by bomb disposal robots," explains Major-General Sir Optimus Prime. "Sending human beings instead will reduce the risk to robot life. We've lost too many good droids this way."