We propose a model for the evolutionary ecology of words as one attempt to extend evolutionary game theory and agent-based models by utilizing the rich linguistic expressions of Large Language Models (LLMs). Our model enables the emergence and evolution of diverse and infinite options for interactions among agents. Within the population, each agent possesses a short word (or phrase) generated by an LLM and moves within a spatial environment. When agents become adjacent, the outcome of their interaction is determined by the LLM based on the relationship between their words, with the loser's word being replaced by the winner's. Word mutations, also based on LLM outputs, may occur. We conducted preliminary experiments assuming that ``strong animal species" would survive. The results showed that from an initial population consisting of well-known species, many species emerged both gradually and in a punctuated equilibrium manner. Each trial demonstrated the unique evolution of diverse populations, with one type of large species becoming dominant, such as terrestrial animals, marine life, or extinct species, which were ecologically specialized and adapted ones across diverse extreme habitats. We also conducted a long-term experiment with a large population, demonstrating the emergence and coexistence of diverse species.

The union of paleontology and robots has its roots in the more established field of bio-inspired robotics, in which scientists fashion robots based on modern animals. Paleo-roboticists, however, face the added complication of designing robotic systems for which there is no living reference. They work around this limitation by abstracting from the next best option, such as a modern descendant or an incomplete fossil record. To help make sure they're on the right track, they might try to derive general features from modern fauna that radiated from a common ancestor on the evolutionary tree. Or they might turn to good ol' physics to home in on the most plausible ways an animal moved.

Did humans have prehistoric ancestors we didn't know about yet? AI seems to suggest so. Did humans have prehistoric ancestors we didn't know about yet? AI seems to suggest so. An artificial intelligence system has identified a previously unknown human ancestor that roamed the planet tens of thousands of years ago and left a genomic footprint in Asian individuals, scientists say. By combining deep learning algorithms and statistical methods, researchers from the University of Tartu in Estonia, Institute of Evolutionary Biology (IBE), and the Centre for Genomic Regulation (CRG) in Spain, found that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with modern humans in Asia.

An artificial intelligence system has identified a previously unknown human ancestor that roamed the planet tens of thousands of years ago and left a genomic footprint in Asian individuals, scientists say. By combining deep learning algorithms and statistical methods, researchers from the University of Tartu in Estonia, Institute of Evolutionary Biology (IBE), and the Centre for Genomic Regulation (CRG) in Spain found that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with modern humans in Asia. The finding, published in Nature Communications, would explain that the hybrid found last year in the caves of Denisova -- the offspring of a Neanderthal mother and a Denisovan father -- was not an isolated case, but rather was part of a more general introgression process. Researchers used deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution. One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants. However, this concept is much more complex when extinct species are involved.

Modern human DNA computational analysis suggests that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with Out of Africa modern humans in Asia. This finding would explain that the hybrid found this summer in the caves of Denisova -- the offspring of a Neanderthal mother and a Denisovan father -- was not an isolated case, but rather was part of a more general introgression process. The study, published in Nature Communications, uses deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution. One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants. However, this concept is much more complex when extinct species are involved.

There's a saying, in conservation biology, credited to the plant ecologist Frank Egler: Ecosystems are not just more complex than we think, they're more complex than we can think. With doomsday narratives swirling about nuclear war, the existential threat of artificial intelligence, and runaway global warming, it's one we might want to constantly bear in mind. We are deeply interconnected with myriad species disappearing from complex ecosystems--and we might be next. The end may not necessarily be "the end," though. In case an extinction event ever wipes us out we could theoretically (at some future point) program an artificial intelligence to survive the fallout and bring us back in a genetically similar form.