Continuous Exploration
Automating discovery to accelerate science and engineering for the world.
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.
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.
Start with Machine Learning
We will initially focus on automating the process of machine learning research and engineering.
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.
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.
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.
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
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.
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.