Creating large language model that "speaks DNA"
Interview with Barry Canton from Gingko Bioworks about real-life implementation of ML and AI
Turing Post is made possible by the readers like you, please consider to support us today with 30% OFF. It will become your secret weapon ;)
We are happy to introduce a new series of interviews that highlights the real-world implementation of Foundation Models (FMs) and Large Language Models (LLMs). We start with an absolutely fascinating topic that can bring so many changes into our world: Bioengineering. In this insightful conversation with Barry Canton, CTO and co-founder of Ginkgo Bioworks, we discussed the evolution of synthetic biology, the integration of AI and GenAI in their R&D, and the future of bioengineering. Let's see what it takes to create a model that 'speaks DNA'.
The History of Bioengineering and the start of Gingko Bioworks
What was your inspiration to start this company?
The five of us met at MIT. Four of us – me, Jason Kelly, Reshma Shetty and Austin Che – were graduate students entering this new field at the time called “synthetic biology,” and were inspired by one of the founders of that field, our professor and co-founder Tom Knight. At the time, it was clear that biology could play a role in addressing global challenges such as climate change, food security, and health care. But it was also clear that to develop a biotechnology product could take on the order of 10 years and $100M to develop, with little certainty of ultimate success. In fact most projects failed. Tom realized that this was because the tools and technologies available to the would-be biological engineer were crude and immature. In the first half of his career Tom had seen how investment in foundational tools and technologies had been essential to realizing the potential of computers. Our inspiration was that we could use the lessons learned in the development of earlier engineering disciplines together with the unique abilities of biology to build a new discipline, biological engineering.
We started the company after we graduated in 2008 and have been pursuing that vision for the last 15 years. Today, we’re immensely fortunate to have a talented team of more than a 1000 people and more than a billion dollars of capital to help us make it real.
The Synthetic Biology Working Group at MIT, initially a collective of enthusiasts in programming biology, has seen synthetic biology evolve significantly, especially with AI integration. Initially, cell programming was a manual, project-specific process. Now, it's transformed by software, robotic automation, and AI, enabling Ginkgo to manage over 100 diverse programs simultaneously, enhancing efficiency, speed, and success rates. The field's expansion into various sectors like biopharma, agriculture, and consumer electronics demonstrates its broadening scope and reaffirms the belief in biology's global technological potential, with AI emerging as a key driver in advancing cell programming.
Sign up if this email was forwarded to you
Machine learning and Generative AI Implementation
How do you use machine learning (ML) at Ginkgo Bioworks?
We’ve been collecting large datasets for many years now and have been using state-of-the-art ML, deep learning, and now Generative AI (primarily in protein modeling) to work with this data.
But we don’t just collect and use data in the context of a single program.