Skyler Ware is a freelance science journalist covering chemistry, biology, paleontology and Earth science. She was a 2023 AAAS Mass Media Science and Engineering Fellow at Science News. Her work has also appeared in Science News Explores, ZME Science and Chembites, among others. Skyler has a Ph.D. in chemistry from Caltech.
Scientists have developed a new type of machine learning model that can understand and design genetic instructions.
Evo is a type of artificial intelligence system called a large language model , which is similar to OpenAI's GPT-4 or Google's Gemini. Researchers and developers train LLMs on vast amounts of data from publicly available resources, like the internet, and the LLMs look for patterns such as common phrases or typical sentence structures, using those patterns to supply words in a sentence one by one.
Other models have already used machine learning and even LLMs to examine genetic information. But so far they have been limited to specialized functions or hampered by high computational cost, the scientists wrote in the study. Evo, by contrast, uses a fast, high-resolution model to process long strings of information, allowing it to analyze patterns at the genome scale and to capture information about large-scale interactions that more specialized models might miss.
RELATED STORIES—'ChatGPT moment for biology': Ex-Meta scientists develop AI model that creates proteins 'not found in nature' Evo even generated sequences of DNA the size of entire genomes — but that DNA wouldn’t necessarily keep something alive. Some of the genetic instructions were similar to DNA in existing organisms. Others looked similar at first glance but didn’t make sense upon closer inspection, similar to an AI-generated image of a person with too many fingers. For example, many of the protein structures encoded in the Evo-generated DNA don’t match naturally occurring proteins.
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