Although wolf-canine interbreeding has been considered extremely rare, the latest research shows that many present-day canines carry a small amount of wolf genes. A surprising study reveals that there is a trace of wolf lurking within the tiny body of a Chihuahua and the gigantic build of a St. Bernard. An international research team from the American Museum of Natural History and the National Museum of Natural History analyzed the genomes of 2,693 dogs and wolves and found that 64.1 percent of purebred dogs carry fragments of wolf DNA. Furthermore, a study of village dogs (free-roaming dogs living in or near human communities) from around the world found genetic traces of wolves in all 280 analyzed pups. Dogs are thought to have evolved from populations of gray wolves, which became extinct during the Late Pleistocene epoch about 20,000 years ago.

Chihuahuas have about 0.2 percent wolf ancestry, according to a new study. Breakthroughs, discoveries, and DIY tips sent every weekday. While that chihuahua might seem about as similar to a wolf as a shrub is to a mighty redwood tree, some small breeds like the tiny, big-eared chihuahua have some wolf ancestry. New research published today in the journal (), finds that the majority of dogs living today have low but detectable levels of post-domestication wolf ancestry. These genes have likely helped shape multiple characteristics, including personality traits, sense of smell, and body size.

Based on 230 visualisations, this paper examines the depiction of museum curators by the popular generative Artificial Intelligence (AI) model, ChatGPT4o. While the AI-generated representations do not reiterate popular stereotypes of curators as nerdy, conservative in dress and stuck in time rummaging through collections, they contrast sharply with real-world demographics. AI-generated imagery extremely underrepresents women (3.5% vs 49% to 72% in reality) and disregards ethnic communities other than Caucasian (0% vs 18% to 36%). It only over-represents young curators (79% vs approx. 27%) but also renders curators to resemble yuppie professionals or people featuring in fashion advertising. Stereotypical attributes are prevalent, with curators widely depicted as wearing beards and holding clipboards or digital tablets. The findings highlight biases in the generative AI image creation dataset, which is poised to shape an inaccurate portrayal of museum professionals if the images were to be taken uncritically at face value.

Jim Pollard is bringing bison bones, fossilized mammoth poop, and more to Blue Earth's 3,000 residents. The Southern Minnesota Museum of Natural History features fossils that range from 7,000 to 7 million years-old. Breakthroughs, discoveries, and DIY tips sent every weekday. When Jim Pollard first laid eyes on the small town of Blue Earth, Minnesota, he was out on a fossil digging trip with his sons. The town on the border of Iowa is surrounded by potential discovery sites -Quarry Hill Nature Center, Shadow Falls Park, and Lilydale Regional Park are all within a few hours drive away.

Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantification and context-sensitivity of generics by leveraging language models as models of language. We create ConGen, a dataset of 2873 naturally occurring generic and quantified sentences in context, and define p-acceptability, a metric based on surprisal that is sensitive to quantification. Our experiments show generics are more context-sensitive than determiner quantifiers and about 20% of naturally occurring generics we analyze express weak generalisations. We also explore how human biases in stereotypes can be observed in language models.

In a recent paper, Mandelkern & Linzen (2024) - henceforth M&L - address the question of whether language models' (LMs) words refer. Their argument draws from the externalist tradition in philosophical semantics, which views reference as the capacity of words to "achieve 'word-to-world' connections". In the externalist framework, causally uninterrupted chains of usage, tracing every occurrence of a name back to its bearer, guarantee that, for example, 'Peano' refers to the individual Peano (Kripke 1980). This account is externalist both because words pick out referents 'out there' in the world, and because what determines reference are coordinated linguistic actions by members of a community, and not individual mental states. The "central question to ask", for M&L, is whether LMs too belong to human linguistic communities, such that words by LMs may also trace back causally to their bearers. Their answer is a cautious "yes": inputs to LMs are linguistic "forms with particular histories of referential use"; "those histories ground the referents of those forms"; any occurrence of 'Peano' in LM outputs is as causally connected to the individual Peano as any other occurrence of the same proper name in human speech or text; therefore, occurrences of 'Peano' in LM outputs refer to Peano. In this commentary, we first qualify M&L's claim as applying to a narrow class of natural language expressions. Thus qualified, their claim is valid, and we emphasise an additional motivation for that in Section 2. Next, we discuss the actual scope of their claim, and we suggest that the way they formulate it may lead to unwarranted generalisations about reference in LMs. Our critique is likewise applicable to other externalist accounts of LMs (e.g., Lederman & Mahowald 2024; Mollo & Milliere 2023). Lastly, we conclude with a comment on the status of LMs as members of human linguistic communities.

What do language models (LMs) do with language? Everyone agrees that they produce sequences of (mostly) coherent sentences. But are they saying anything with those strings or simply babbling in a convincing simulacrum of language use? This is a vague question, and there are many ways of making it precise. Here we will address one aspect of the question, namely, whether LMs' words refer: that is, whether the outputs of LMs achieve "word-to-world" connections. There is prima facie reason to think they do not since LMs do not interact with the world in the way that ordinary language users do. Drawing on insights from the externalist tradition in philosophy of language, we argue that appearances are misleading and that there is good reason to think that LMs can refer.

On July 13, 1833, during a visit to the Cabinet of Natural History at the Jardin des Plantes, in Paris, Ralph Waldo Emerson had an epiphany. Peering at the museum's specimens--butterflies, hunks of amber and marble, carved seashells--he felt overwhelmed by the interconnectedness of nature, and humankind's place within it. Check out more from this issue and find your next story to read. The experience inspired him to write "The Uses of Natural History," and to articulate a philosophy that put naturalism at the center of intellectual life in a technologically chaotic age--guiding him, along with the collective of writers and radical thinkers known as transcendentalists, to a new spiritual belief system. Through empirical observation of the natural world, Emerson believed, anyone could become "a definer and map-maker of the latitudes and longitudes of our condition"--finding agency, individuality, and wonder in a mechanized age. America was crackling with invention in those years, and everything seemed to be speeding up as a result.

When we humans imagine the future, it is common to picture ourselves nested within an ecosystem populated by robots, devices, and virtual realities. The future is shining and technological. The future is digital, ones and zeros, electricity and invisible connections. The dangers of the future--automation and artificial intelligence--are of our own invention. Nature is an afterthought in our contemplation of what comes next, a transgenic potted plant behind a window that does not open.

Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels. Despite this, evaluation of insect demography is strongly limited by the difficulty of collecting census data at sufficient scale. We propose a method to gather and leverage observations from bystanders, hikers, and entomology enthusiasts in order to provide researchers with data that could significantly help anticipate and identify environmental threats. Finally, we show that there is indeed interest on both sides for such collaboration.