马克·扎克伯格的生物实验室(Biohub)正在与谷歌和联邦政府合作，其雄心勃勃的计划是利用人工智能生成大量生物学数据，以预测细胞的行为。
Mark Zuckerberg's Biohub is partnering with Google and the federal government in its ambitious effort to use AI for generating vast quantities of biological data that can predict how cells behave.
为什么重要：最终的目标是建立一个人工智能模型，让科学家们能够虚拟地测试潜在的实验，从而帮助他们在花费时间和金钱进行实验室实验之前，筛选出最有前途的实验。
Why it matters: The ultimate goal is an AI model that lets scientists test potential experiments virtually, helping them identify the most promising ones before spending the time and money to perform them in the lab.
- 人工智能能够理解蛋白质和其他生物学知识，但对整个活细胞进行建模要复杂得多——数量级的差别——而且研究人员目前还没有足够的合适数据来实现这一点。
• AI is capable of understanding proteins and other pieces of biology, but modeling an entire living cell is orders of magnitude more complex — and researchers don't yet have enough of the right data to do it.
推动这则消息的因素：生物实验室、能源部、国立卫生研究院、谷歌深思人工智能公司(DeepMind)、同构实验室(Isomorphic Labs)、Meta以及一系列科学组织正在合作，共同创建和标准化生物实验室所称的"通用虚拟细胞"的数据。
Driving the news: Biohub, the Department of Energy, the National Institutes of Health, Google DeepMind, Isomorphic Labs, Meta and a collection of scientific organizations are collaborating to create and standardize data for what Biohub calls a "universal virtual cell."
更宏观的背景：目标是利用人工智能在生物学领域探索更多的科学问题，从而虚拟地进行研究，让科学家们将昂贵的实验室工作留给那些最有可能教会他们重要知识的实验。
The big picture: The goal is to use AI to explore many more scientific questions in biology virtually, allowing scientists to reserve expensive lab work for experiments most likely to teach them something important.
- 生物实验室科学主任亚历克斯·赖夫斯(Alex Rives)告诉Axios：“如果我们能在实验室中提出的每一个问题上加入越来越多的推理和智能，那么这些实证结果的价值将会更加巨大。”
• "If we can put more and more reasoning and intelligence into every single question that we actually ask in the lab, the value of those empirical results will be far greater," Biohub head of science Alex Rives told Axios.
人工智能近期进展的很大一部分来自于将更好的算法、更多的计算能力和巨大的数据相结合。
Much of AI's recent progress has come from combining better algorithms, more computing power and enormous amounts of data.
- 生物学带来了额外的挑战：人工智能所需的大量信息尚不存在，必须从物理世界中费力地测量出来。
• Biology presents an additional challenge: Much of the information AI needs doesn't exist yet and has to be painstakingly measured from the physical world.
- 赖夫斯表示：“我们正处在人工智能驱动的新科学范式的开端。”
• "We're at the beginning of a new scientific paradigm with AI," Rives said. Yes, but:
是的，但：生物学比许多其他人工智能领域更具挑战性，因为研究人员需要赖夫斯所称的“实证人工智能”——从生物学证据中学习并能准确预测物理世界中发生的事情的模型。
Biology is harder than many other AI domains because researchers need what Rives calls "empirical AI" — models that learn from biological evidence and can accurately predict what happens in the physical world.
- “生物学面临的最大挑战，是弥合计算与数字世界，同生物与生命的真实物理世界之间的鸿沟，”里夫斯说。“实现这一目标的方法就是通过数据。”
• "The big challenge in biology is to bridge that gap between compute and the digital world and the real physical world of biology and life," Rives said. "The way to do that is through data."
- 此类模型最终可以帮助科学家研究诸如衰老和再生机制等基础问题，或是诸如哪些分子机制导致了阿尔茨海默病等医学问题。
• Such models could eventually help scientists investigate fundamental questions such as how aging and regeneration work — or medical questions such as which molecular mechanisms are responsible for Alzheimer's disease. How it works:
工作原理：第一阶段将绘制一张广泛的细胞生物学图谱，收集关于细胞及其如何对变化做出反应的各类信息。
The first phase will create a broad map of cellular biology, gathering different kinds of information about cells and how they respond to changes.
- 展望未来，里夫斯设想的模型将能够检查个体的疾病，预测其分子病因以及最佳的干预方式。
• Further out, Rives envisions models that could examine an individual's disease and predict its molecular causes and the best way to intervene.
引人关注之处：商业合作伙伴将在其开发的数据公开共享之前，享有为期一年的独家访问权。
The intrigue: The commercial partners will have one year of exclusive access to the data they develop before it is shared publicly.
- 里夫斯表示，这种暂时的优势旨在为企业出资提供动力，同时确保最终的数据成为开放的科学资源。
• Rives said the temporary advantage is intended to give companies a reason to contribute money while still ensuring the resulting data becomes an open scientific resource.
- “我们必须为商业参与者提供一些激励措施让他们参与其中，而禁运期就创造了这种条件，”里夫斯说。“但这只是一年的禁运期。这意味着数据很快就会广泛供科学研究使用。”
• "We have to have some incentive for commercial players to be a part of this, and the embargo period creates that," Rives said. "But it's a one-year embargo. So what that means is that very rapidly, data becomes available broadly to scientific efforts."
细节聚焦：这项工作涉及18亿美元的资金、数据、计算和测量技术。
Zoom in: The effort involves $1.8 billion in funding, data, computing and measurement technology.
- 美国能源部计划在五年内投资5亿美元用于生物测量、建模和计算，而美国国立卫生研究院则投入了此前超过5亿美元联邦投资所产生的数据库和其他资源。
• DOE plans to invest more than $500 million over five years in biological measurement, modeling and computing, while NIH is bringing datasets and other resources resulting from more than $500 million in previous federal investment.
- 谷歌深度思维、Isomorphic Labs和Meta将共同投资另外3.0亿美元，而Biohub此前已承诺为该项目投入5亿美元。
• Google DeepMind, Isomorphic Labs and Meta are collectively investing another $300 million, while Biohub previously committed $500 million to the effort.
背景回顾：今年4月，当Biohub宣布启动这项耗资5亿美元的初期计划时，Rives对Axios表示，目前尚未解决的最大疑问之一就是：细胞生物学是否也会遵循人工智能领域常见的“规模效应规律”——即随着训练数据量的增加，模型性能也会呈现出可预测的提升趋势。
Flashback: When Biohub announced its initial $500 million effort in April, Rives told Axios one of the biggest unanswered questions was whether cellular biology would exhibit the same kind of "scaling laws" seen elsewhere in AI — with models becoming predictably better as they are trained on increasing amounts of data.
我们正密切关注的动向： Rives认为，研究人员用不了多久就能验证这一设想是否成立。
What we're watching: Rives thinks researchers won't have to wait particularly long to find out whether the bet is working.
- 他表示，在获得首个大规模生物数据集后的一年内，研究人员便应能够训练相关模型、评估其性能，并确定哪些类型的生物数据能进一步提升模型能力。
• Within a year of having the first large-scale dataset, he said, researchers should be able to train models, measure their capabilities and determine which kinds of additional biological data make them better.
- “这一规律在所有领域都适用，在生物学领域同样如此，”Rives说道，并列举了人工智能在蛋白质生物学领域取得的进展作为例证。
• "It's worked in every field, and it works in biology too," Rives said, pointing to AI's progress in protein biology.