Discovery Loop
Discovery Loop
Discovery LP Continuous Exploration Automating discovery to accelerate science and engineering for the world.
Discovery LP 持续探索 通过自动化发现过程，加速全球科学与工程的发展。
Scientific discovery is bottlenecked. The scientific method is one of the greatest tools humanity has ever devised, yet execution entails repetitive experimental loops that are hard to scale with today’s manual efforts: you propose an experiment, implement and run it, examine the results, then iterate to refine your approach. Historically, scientific progress has relied on these sequential human iterations. In many domains, this process remains incredibly slow and labor-intensive.
科学发现正面临瓶颈。科学方法是人类有史以来最伟大的工具之一，但其实施过程涉及重复的实验循环，难以通过当今的人工努力进行扩展：你需要提出实验、实施并运行、检查结果，然后迭代以改进你的方法。从历史上看，科学进步一直依赖于这些连续的人类迭代。在许多领域，这一过程仍然极其缓慢且耗费人力。
01 — The Approach Automating the experimental loop. At Discovery Loop, we are building systems to automate these entire experimental loops. By utilizing frontier AI models and large-scale computational infrastructure, our systems will be able to rapidly propose, run, and learn from evaluations. This approach allows for the parallel execution of thousands of experiments, drastically compressing iteration time and driving up the quantity and quality of scientific and engineering output.
01 — 方法 自动化实验循环。 在 Discovery Loop，我们正在构建系统以自动化整个实验循环。通过利用前沿 AI 模型和大规模计算基础设施，我们的系统将能够快速提出、运行评估并从中学习。这种方法允许并行执行数千个实验，极大地压缩了迭代时间，并提高了科学和工程产出的数量与质量。
Start with Machine Learning We will initially focus on automating the process of machine learning research and engineering.
从机器学习开始 我们将首先专注于自动化机器学习研究和工程的过程。
Act as Our Own First Customer We will use these automated ML capabilities to rapidly optimize our own technology stack before expanding to other domains.
成为自己的首位客户 我们将利用这些自动化的机器学习能力快速优化我们自己的技术栈，然后再扩展到其他领域。
Grand Challenges We believe our approach will be able to solve any learning loop with measurable outcomes within the domains of science and engineering. Ultimately, we are building systems capable of taking on National Academy of Engineering (NAE) Grand Challenges—such as engineering better medicines, advancing health informatics, making solar energy economical, providing access to clean water, securing cyberspace, and engineering the tools of scientific discovery.
重大挑战 我们相信，我们的方法将能够解决科学和工程领域内任何具有可衡量结果的学习循环。最终，我们正在构建能够应对美国国家工程院 (NAE) 重大挑战的系统——例如研发更好的药物、推进健康信息学、使太阳能经济化、提供清洁水资源、保障网络空间安全以及设计科学发现的工具。
02 — Mission Our mission is straightforward: we are building AI solutions that can automatically solve important problems in machine learning, science, and engineering. By advancing the pace at which we conduct engineering and scientific discovery, we can bring the benefits of science and technology to the world much faster. Ultimately, our goal is to build AI systems that act as a deeply positive, empowering force for humanity, delivering technology solutions that improve people’s lives on a global scale.
02 — 使命 我们的使命很简单：我们正在构建能够自动解决机器学习、科学和工程领域重要问题的 AI 解决方案。通过加快我们进行工程和科学发现的步伐，我们可以更快地将科学技术的益处带给世界。最终，我们的目标是构建能够作为人类积极、赋能力量的 AI 系统，提供在全球范围内改善人们生活的技术解决方案。
04 — The Team The brain trust. Our founding team — Jeff Dean, Sanjay Ghemawat, Quoc Le, and Oriol Vinyals — has a shared history of deep friendship and decades of close and impactful collaboration. From left Oriol Vinyals · Sanjay Ghemawat · Jeff Dean · Quoc Le.
04 — 团队 智囊团。我们的创始团队——Jeff Dean、Sanjay Ghemawat、Quoc Le 和 Oriol Vinyals——拥有深厚的友谊和数十年来紧密且富有影响力的合作历史。从左至右：Oriol Vinyals · Sanjay Ghemawat · Jeff Dean · Quoc Le。
Collectively, we represent three of the most-cited researchers in artificial intelligence and two of the most-cited researchers in distributed systems. Between us, we have pioneered massive scale computing and led the creation of critical infrastructure, products, and foundational AI advances that the world relies on, including multiple generations of Google Search, Google Ads, Google News, Google Translate, Google File System, MapReduce, BigTable, Spanner, TensorFlow, Pathways, TPUs, AlphaChip, AlphaStar, AlphaCode, AlphaFold, Gemini, model distillation, mixture-of-experts model architectures, word2vec, sequence-to-sequence models, chain of thought reasoning, neural architecture search, and multiple generations of Large Language Models (LLMs) among others. Our relative advantage isn’t just our technical ability; it is the unprecedented scale of the systems we have previously built. We possess true full-stack depth that spans chips, hardware infrastructure, software infrastructure, ML models, and products.
我们团队中汇集了人工智能领域引用率最高的三位研究人员，以及分布式系统领域引用率最高的两位研究人员。我们共同开创了大规模计算，并主导了全球所依赖的关键基础设施、产品和基础 AI 进展的创建，包括多代 Google 搜索、Google 广告、Google 新闻、Google 翻译、Google 文件系统、MapReduce、BigTable、Spanner、TensorFlow、Pathways、TPU、AlphaChip、AlphaStar、AlphaCode、AlphaFold、Gemini、模型蒸馏、混合专家模型架构、word2vec、序列到序列模型、思维链推理、神经架构搜索以及多代大语言模型 (LLM) 等。我们的相对优势不仅在于技术能力，更在于我们此前构建的系统所具备的前所未有的规模。我们拥有跨越芯片、硬件基础设施、软件基础设施、机器学习模型和产品的真正全栈深度。
04 — What’s Next Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today. By automating the loops of discovery, the world will be able to make much more rapid advances across countless fields of science. We are building a lean, in-person team to execute this transformative vision. If this kind of work excites you, we want to hear from you. Careers at Discovery Loop →
04 — 未来展望 想象一下，在未来，少数人就能比当今庞大的科学家和工程师团队更快速、更高质量地完成科学研究和工程任务。通过自动化发现循环，世界将能够在无数科学领域取得更迅速的进展。我们正在组建一支精简的、现场办公的团队来实现这一变革性愿景。如果你对这类工作感到兴奋，我们期待你的加入。加入 Discovery Loop →