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A team of Carnegie Mellon University researchers set out to see how accurately large language models (LLMs) can match the style of text written by humans. Their findings were recently published in the Proceedings of the National Academy of Sciences.

“We humans, we adapt how we write and how we speak to the situation. Sometimes we’re formal or informal, or there are different styles for different contexts,” said Alex Reinhart, lead author and associate teaching professor in the Department of Statistics & Data Science.

“What we learned is that LLMs, like ChatGPT and Llama, write a certain way, and they don’t necessarily adapt to the . The context and their style are actually very distinctive from how humans normally write or speak in different contexts. Nobody has measured or quantified this in the way we were able to do.”

Porcine reproductive and respiratory syndrome virus (PRRSV) infection in pigs is economically devastating for the global swine industry. The viral infection leads to reproductive disorder in sows and respiratory problems in infected newborn and growing pigs.

Unfortunately, high genetic variability of the virus and differing disease-causing strength or virulence hinders vaccine development and complicates disease management. Not much is known about the factors contributing to viral disease severity or the anti-viral immune responses.

Dr. Jun-Mo Kim, Associate Professor at the Department of Animal Science and Technology, Chung-Ang University, Korea, has focused his research efforts on filling this gap in understanding.

Ubiquitously present in plant life, coumarins, as a class of phenolic compounds, have multiple applications—in everyday life, in organic synthesis, in medicine and many others. Coumarins are well known for their broad spectrum of physiological effects. The specific structure of the coumarin scaffold involves a conjugated system with excellent charge and electron transport properties. The antioxidant activity of natural coumarins has been a subject of intense study for at least two decades. Significant research into the antioxidant behavior of natural/semi-synthetic coumarins and their complexes has been carried out and published in scientific literature.

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Furthermore, healthcare and life sciences are both booming sectors with regards to artificial intelligence applications. Many other companies are also working at the intersection of technology and biology, given the numerous challenges that are present in the fields of drug discovery and protein folding. For example, Deepmind and Isomorphic Labs have made immense progress with AlphaFold, another leading foundation model ecosystem to better understand protein folding. Meta created something similar with its ESM Metagenomic Atlas. Given the increasing rates of catastrophic disease and the rapidly evolving nature of pathogens, scientists in these sectors hope to use the best of the advancements in AI to help solve some of biology’s toughest challenges.

Indeed, the immense progress that has been made thus far has paved the way for monumental scientific inventions and developments to emerge in the years ahead. Undoubtedly, this work is just getting started.