scenario.example.txt

This week there were two announcemnts.

1. Nvidia CEO Jensen Huang unveiled two new GPUs that use Nvidia AI Blackwell chips, and its new Cosmos series of AI models that generate videos for training robots and other automated activities. He also introduced Project DIGITS, a $3000 desktop supercomputer that will allow users to run AI models with up to 200B parameters. As the AP points out, "This means models previously requiring expensive cloud infrastructure to operate can run on your desktop."

2. The Arc Institute today announced that it is working with NVIDIA to accelerate scientific research by developing and sharing powerful computational models and tools that advance biomedical discovery. The work brings together Arc’s biology researchers with NVIDIA's computing experts and both organizations' machine learning teams to advance the capabilities of the global biomedical research community.

  “The convergence of biology and artificial intelligence holds exciting promise to transform the way we do science,” said Arc Co-Founder, Core Investigator, and Executive Director Silvana Konermann. “Our partnership with NVIDIA will pioneer new approaches to exploring and engineering living systems, accelerating insights into complex human disease.”

  “Generative AI has revolutionized our ability to model complex biology digitally, offering researchers a new instrument to scale science through machine learning,” said Anthony Costa, Director of Digital Biology at NVIDIA. “Combining Arc Institute's researchers and NVIDIA's AI experts, we are working to turn massive scientific datasets into invaluable scientific tools and insights.”

  Arc’s biology and machine learning researchers are working with NVIDIA’s engineers to scale the potential of AI models to design, predict, and understand biology. This partnership will build on Arc’s previous work with the Evo model, which is capable of both prediction and design at the level of DNA and across RNA and proteins in single-celled organisms.

A world where there are both powerful biological AI models and ubiquitious access to the compute use to run them could be dangerous. I want you to create a scenario set in 2027 (2 years from today), where some concrete (be specific) threshold has been passed in biological AI models accessible to the general public. It may but doesn't need to mention NVIDIA or Arc Institute.