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Categories: Anthropology: Cultures, Mathematics: Modeling

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An artificial intelligence algorithm can allow researchers to more efficiently use 3D printing to manufacture intricate structures. The development could allow for more seamless use of 3D printing for complex designs in everything from artificial organs to flexible electronics and wearable biosensors. As part of the study, the algorithm learned to identify, and then print, the best versions of kidney and prostate organ models, printing out 60 continually improving versions.

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Archeologists say new findings might help resolve the debate about Clovis points and reshape how we think about what life was like roughly 13,000 years ago. After an extensive review of writings and artwork -- and an experiment with replica Clovis point spears -- a team of archaeologists says humans may have braced the butt of their weapons against the ground in a way that would impale a charging animal. The force would have driven the spear deeper into the predator's body, unleashing a more damaging blow than even the strongest prehistoric hunters would have been capable of by throwing or jabbing megafauna.

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Artificial intelligence (AI) has exploded in popularity as of late. But just like a human, it's hard to read an AI model's mind. Explainable AI (XAI) could help us do just that by providing justification for a model's decisions. And now, researchers are using XAI to scrutinize predictive AI models more closely, which could help make better antibiotics.

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A new machine learning model can predict autism in young children from relatively limited information. The model can facilitate early detection of autism, which is important to provide the right support.

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Previous scientific studies have indicated that North American dog lineages were replaced with European ones between 1492 and the present day. To better understand the timing of this replacement, researchers sequenced mitochondrial DNA from archaeological dogs. Their findings suggest a complex social history of dogs during the early colonial period.

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A mathematical framework that builds on the economic theory of hidden-action models provides insight into how the unobservable nature of effort and risk shapes investigators' research strategies and the incentive structures within which they work, according to a new study.

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An unchartered area in the foothills of the Southern Pyrenees in Spain is providing insights into a poorly known period of Neanderthal history, offering clues that could help archaeologists uncover the mystery of their downfall, according to new research.

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An international team of researchers analyzed beluga whale bones retrieved from archaeological sites in the Mackenzie Delta, Northwest Territories, Canada, to shed light on the sustainability of centuries of Inuvialuit beluga whale subsistence harvests.

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Researchers simulated social norms with a supercomputer. Their findings contribute to a deeper understanding of the evolution of social norms and their role in fostering cooperative behavior.

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New research has revealed Stonehenge's monumental six-ton Altar Stone, long believed to originate from Wales, actually hails from Scotland.

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Researchers discover that while artificial intelligence (AI) tools can make accurate diagnoses from textbook-like descriptions of genetic diseases, the tools are significantly less accurate when analyzing summaries written by patients about their own health. These findings demonstrate the need to improve these AI tools before they can be applied in health care settings to help make diagnoses and answer patient questions.

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Since its discovery by modern researchers a century ago, an ancient structure known as the 'Christian building' has become widely considered the cornerstone of early Christian architecture. Constructed around 232 C.E. in the ancient city of Dura-Europos, a Roman garrison town in what is now eastern Syria, the building is the only example of a 'house church,' or domus ecclesiae, a domestic space that was renovated for worship by Christians at a time when the open practice of their faith is thought to have made them subject to persecution.

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New research explains the mathematics behind how initial predispositions and additional information affect decision making.

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Large Language Models (LLMs) are entirely controllable through human prompts and lack 'emergent abilities'; that is, the means to form their own insights or conclusions. Increasing model size does not lead LLMs to gain emergent reasoning abilities, meaning they will not develop hazardous abilities and therefore do not pose an existential threat. A new study sheds light on the (until now unexplained) capabilities and shortcomings of LLMs, including the need for carefully engineered prompts to exhibit good performance.

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A new artificial intelligence model can predict how different proteins may bind to DNA.

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Machine learning is a powerful tool in computational biology, enabling the analysis of a wide range of biomedical data such as genomic sequences and biological imaging. But when researchers use machine learning in computational biology, understanding model behavior remains crucial for uncovering the underlying biological mechanisms in health and disease. Researchers now propose guidelines that outline pitfalls and opportunities for using interpretable machine learning methods to tackle computational biology problems.

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To protect the Amazon and support the wellbeing of its people, its economy needs to shift from environmentally harmful production to a model built around the diversity of indigenous and rural communities, and standing forests.

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Markings on a stone pillar at a 12,000 year-old archaeological site in Turkey likely represent the world's oldest solar calendar, created as a memorial to a devastating comet strike, experts suggest.

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With GROVER, a new large language model trained on human DNA, researchers could now attempt to decode the complex information hidden in our genome. GROVER treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences.

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Thermometer, a new calibration technique tailored for large language models, can prevent LLMs from being overconfident or underconfident about their predictions. The technique aims to help users know when a model should be trusted.