- Meta has revealed a new protein-folding AI to revolutionize healthcare sector.
- Facebook created the popular open-source framework PyTorch.
- Also the company has released the ESM Metagenomic Atlas, which contains over 600 million proteins and predictions for almost the entire MGnify90 database.
Meta has revealed a new protein-folding AI that has the potential to revolutionize science and the development of new medicines.
Facebook, as the company was known before changing its name, has long been regarded as a leader in artificial intelligence. Facebook created the popular open-source framework PyTorch, and earlier this year Meta became a founding member of a foundation aimed at driving AI adoption.
Many people, including shareholders, have expressed concern that in its pursuit of becoming a leader in the metaverse, it will reduce its focus on other important areas.
Brad Gerstner, the founder of Meta shareholder Altimeter Capital, penned a letter in which he urged Meta to reduce its metaverse investments and “solidify the company’s position” as one of the world’s leaders in AI.
“Meta’s investment in AI will lead to exciting and important new products that can be cross-sold to billions of customers. From Grand Teton to Universal Speech Translator to Make-A-Video, we are witnessing a Cambrian moment in AI, and Meta is no doubt well positioned to help invent and monetize that future,” wrote Gerstner.
“Perhaps it was the re-naming of the company to Meta that caused the world to conclude that you were spending 100% of your time on Reality Labs instead of AI or the core business. Whatever the reason, that is certainly the perception.”
Meta’s announcement of its protein-folding AI this week may help to alleviate such concerns.
In addition to the model used to create the database and an API that allows researchers to use it for scientific discovery, the company has released the ESM Metagenomic Atlas, which contains over 600 million proteins and predictions for almost the entire MGnify90 database.
According to Meta, using a language model of protein sequences accelerated structure prediction by up to 60 times.
“ESMFold shows how AI can give us new tools to understand the natural world, much like the microscope, which enabled us to see into the world at an infinitesimal scale and opened up a whole new understanding of life,” explained Meta.
“Much of AI research has focused on helping computers understand the world in a way similar to how humans do. The language of proteins is one that is beyond human comprehension and has eluded even the most powerful computational tools. AI has the potential to open up this language to our understanding.”
