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

今天的福布斯：英偉達（NVIDIA）、谷歌（Google）和微軟（Microsoft）為何將數十億美元押注於生物技術的人工智能未來？

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.

今年 1 月在舊金山舉行的摩根大通醫療保健大會（J.P. Morgan Health Care Conference）是本年度最大的醫療技術盛會，英偉達公司首席執行官黃仁勳在會上與 Recursion 公司進行了一次爐邊談話，Recursion 是一家藥物發現公司，英偉達去年向其注資 5000 萬美元。

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

他掃視了一下臺下的聽眾，其中大多數是衛生和生物技術專家，他承認自己的處境並不尋常。

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

他說，引用他的話，你們不是我的常客。

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

觀眾可能不屬於他的核心人群，但他希望這種情況會有所改變。

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

黃博士一次又一次地把數字生物學吹捧為，引以為傲的下一次驚人的技術革命。

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.

隨著人工智能熱潮席捲硅谷，英偉達每年的業務收入超過 600 億美元，並於去年夏天成為少數幾家市值達到萬億美元的公司之一。

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

在健康和生物技術領域，它看到了更多促進增長的機會。

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.

英偉達醫療保健副總裁金伯利-鮑威爾（Kimberly Powell）告訴《福布斯》："我們已經宣佈，我們將成為英偉達下一個價值數十億美元的業務。

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

她說，公司的目標是為更多的生物技術公司提供芯片、雲基礎設施和其他工具。

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.

現在，OpenAI 的 ChatGPT 和谷歌 DeepMind 的 Gemini 等大型語言模型已經成為生成式人工智能的主流，世界上最強大的幾家科技公司正將生物技術視為人工智能的下一個前沿領域，在這個領域中，人工智能不是根據提示生成有趣的詩歌，而是生成下一種救命藥物。

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.

英偉達公司因其強大的 GPU 芯片而成為人工智能革命的中堅力量，在過去兩年中，該公司風險投資部門的大部分投資都投向了藥物發現領域。

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.

在DeepMind，谷歌人工智能實驗室的AlphaFold模型是預測蛋白質結構的開創性工具，在過去的一年裡，學術研究人員利用該模型開發了一種所謂的分子注射器，可將藥物直接注射到細胞中，並研究出對殺蟲劑依賴性更低的農作物。

The interest in biotech is industry-wide.

整個行業都對生物技術感興趣。

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

微軟、亞馬遜甚至 Salesforce 也有蛋白質設計項目。

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

雖然將人工智能用於藥物發現並不是什麼新趨勢，但 DeepMind 在 2018 年首次推出了 AlphaFold。

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.

DeepMind和英偉達的高管告訴《福布斯》，這是一個突破性的時刻，這要歸功於三件事：現在可用的海量訓練數據、計算資源的爆炸式增長以及人工智能算法的進步。

Powell said, quote,

鮑威爾說，引述如下

The three ingredients are here for the very first time.

這三種成分是第一次出現在這裡。

This was not possible five years ago.

這在五年前是不可能的。

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

由於人工智能的複雜性，它在生物技術領域有著巨大的潛力。

Just take the problem that AlphaFold targets.

就拿 AlphaFold 所針對的問題來說吧。

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

蛋白質是人體的基本機制，管理著人體的各種功能。

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

所有這些功能都依賴於蛋白質的三維形狀。

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.

每種蛋白質都由一系列氨基酸組成，這些氨基酸與外部環境之間的相互作用決定了蛋白質的摺疊方式，從而決定了蛋白質的最終形狀。

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.

生物技術公司對能夠根據氨基酸序列預測蛋白質的形狀非常感興趣，他們可以利用這些知識設計出從新藥、改良作物到生物可降解塑料等各種產品。

This is where deep learning comes in.

這就是深度學習的作用所在。

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.

在數以億計的不同蛋白質序列及其基本結構上訓練人工智能模型，有助於這些模型發現生物學中的模式，而不一定需要進行真正的分子動力學模擬所需的昂貴計算。

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

完全模擬蛋白質需要高強度的計算資源，是以一些機構專門設計和建造了超級計算機來處理這類問題，例如

Anton 2 at the Pittsburgh Supercomputing Center.

匹茲堡超級計算中心的安東 2 號。

For full coverage, check out Richard Nieva and Alex Knapp's piece on Forbes.com.

有關報道全文，請查看 Richard Nieva 和 Alex Knapp 在 Forbes.com 上發表的文章。

This is Kieran Meadows from Forbes.

我是《福布斯》雜誌的基蘭-米多斯。

Thanks for tuning in.

感謝您的收聽。