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  • Here's your Forbes Daily Briefing for Sunday, March 17th.


  • Today on Forbes, why NVIDIA, Google, and Microsoft are betting billions on biotech's AI future.


  • At the J.P. Morgan Health Care Conference this January in San Francisco, the biggest health tech event of the year, NVIDIA CEO Jensen Huang was taking part in a fireside chat with Recursion, a drug discovery firm that NVIDIA pumped $50 million into last year.


  • He scanned the audience, mostly health and biology technologists, and he acknowledged that he was on unusual ground.


  • He said, quote, you're not my normal crowd.


  • The audience may not have been part of his core demographic, but he's hoping that will change.


  • Over and over again, Huang has touted digital biology as the, quote, next amazing revolution in technology.


  • As the AI boom has swept Silicon Valley, NVIDIA has built a more than $60 billion a year business and last summer became one of the few companies with a market cap in the trillions.


  • In health and biotech, it sees more opportunities to fuel its growth.


  • Kimberly Powell, NVIDIA's vice president of health care, told Forbes, quote, it's been declared we're the next many billion dollar business for NVIDIA.


  • She said the company aims to provide chips, cloud infrastructure, and other tools to more biotech firms.


  • Now that large language models like OpenAI's ChatGPT and Google DeepMind's Gemini have mainstreamed generative AI, several of the world's most powerful tech companies are looking to biotech as the next frontier in artificial intelligence, a frontier where AI isn't generating funny poems from a prompt, but rather the next life-saving drug.

    OpenAIのChatGPTやGoogle DeepMindのGeminiのような大規模な言語モデルが生成AIの主流となった今、世界で最も強力なハイテク企業のいくつかは、人工知能の次のフロンティアとしてバイオテクノロジーに注目している。

  • At NVIDIA, arguably the backbone of the AI revolution because of its powerful GPU chips, the bulk of investments at the company's venture capital arm over the past two years have been in drug discovery.


  • At DeepMind, the Google AI lab's AlphaFold model, a groundbreaking tool for predicting protein structures, has been used by academic researchers over the past year to develop a so-called molecular syringe to inject medicine directly into cells and to research crops that are less dependent on pesticides.


  • The interest in biotech is industry-wide.


  • Microsoft, Amazon, and even Salesforce have protein design projects as well.


  • While using AI in drug discovery is not exactly a new trend, DeepMind first unveiled AlphaFold in 2018.


  • Executives at both DeepMind and NVIDIA told Forbes that this is a breakthrough moment thanks to the confluence of three things, the massive training data now available, the explosion of computing resources, and advancements in AI algorithms.


  • Powell said, quote,


  • The three ingredients are here for the very first time.


  • This was not possible five years ago.


  • AI has great potential in the biotech space because of its sheer complexity.


  • Just take the problem that AlphaFold targets.


  • Proteins are the basic machinery of your body, managing a wide variety of functions.


  • All of these functions are reliant on the three-dimensional shape of a protein.


  • Every protein is made up of a sequence of amino acids, and interactions between those amino acids and the external environment determine how the protein, quote, folds, which dictates its ultimate shape.


  • Being able to predict the shape of a protein based on its amino acid sequences is of intense interest to biotech companies, which can use those insights to design everything from new drugs to improved crops to biodegradable plastics.


  • This is where deep learning comes in.


  • Training AI models on hundreds of millions of different protein sequences and their underlying structures help those models uncover patterns in biology without necessarily needing to do the expensive computations required by a true molecular dynamics simulation.


  • Fully simulating proteins requires such intense computational resources that institutions have designed and built supercomputers specifically to handle this type of problem, such as the


  • Anton 2 at the Pittsburgh Supercomputing Center.


  • For full coverage, check out Richard Nieva and Alex Knapp's piece on


  • This is Kieran Meadows from Forbes.


  • Thanks for tuning in.


Here's your Forbes Daily Briefing for Sunday, March 17th.


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