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No Priors Ep. 34 | With Ginkgo Bioworks Co-Founder and CEO Jason Kelly

3K views|9 months ago
💫 Short Summary

The video explores the integration of biology and technology, focusing on synthetic biology, DNA manipulation, and the challenges and opportunities of bringing programming principles into biology. It discusses protein engineering, AI applications in various industries, the importance of data availability for training neural networks, and the risks associated with using AI for biology. The conversation also touches on infectious disease monitoring, biosafety levels, ethical decision-making in drug development, company leadership, and the intersection of biological inspiration and AI architecture. The video concludes with a discussion on hyper optimization and the importance of learning from evolution to create more evolvable systems.

✨ Highlights
📊 Transcript
Integration of biology and technology through synthetic biology and its potential impact on industries.
DNA is viewed as code and cells can be manipulated to produce proteins.
Biological processes are highlighted for their physical nature, unlike virtual systems.
Bringing programming principles into biology presents challenges and opportunities.
AI technology is mentioned as a tool for advancing the field of biology.
Transition from Computer Architect to DNA Programmer
Tom Knight shifted his focus from mainframe computers to DNA programming, establishing a wet lab at MIT for computer scientists to explore DNA as code.
Embracing Biological Unpredictability
Knight emphasized the challenge of understanding and working with biological systems, which lack complete predictability and can behave unexpectedly.
Importance of Systems Biology
Working with neural networks and biological engineering requires a new approach to understanding and debugging due to the complexity and unpredictability of biological systems.
Evolution of Biological Engineering and Neural Networks.
The messy nature of evolution prioritizing utility over form, resulting in redundancies and perturbations.
Excitement about the potential of self-evolving neural networks and the unpredictability of biology.
Mention of abstraction in computer science and the contrast between past requirements for programming knowledge and accessibility for children today.
Ginkgo's focus on automating lab work through their Foundry team benefits genetic design testing and AI development.
The split disciplines of electrical engineering and computer science allow for scalability and economic benefits.
Ginkgo's business model involves developing software for customers like Mercedes in exchange for royalties.
Scientists at Ginkgo showcase the challenging cultural shift towards automation in scientific experimentation.
The company's infrastructure enables efficient and effective scientific research.
Protein engineering for laundry detergent enzymes.
Enzymes act as catalysts to break down dirt faster in cold water detergents.
The goal is to improve enzyme quality and increase production by leveraging existing data assets.
DNA sequence determines enzyme effectiveness as a catalyst, and tools are used to model proteins and enhance catalytic activity.
The focus is on optimizing enzyme production and quality for various applications.
The concept of foundation models in AI and its application in bioinformatics.
A deal with Google to develop a foundation model not specific to catalysis or any other area, similar to GPT-4 in language processing.
The challenge of creating AI models that can rival human expertise, especially in legal and biological domains.
Prediction that computer brains will surpass humans in biology faster than in language processing.
Applications of AI in various industries, focusing on protein folding models and their impact on pharmaceuticals and biologics.
Highlighted the cost of drug development and the allocation of funds for molecule research versus clinical trials.
Compared different markets such as pharmaceuticals, biologics, and industrial catalysts in terms of market size and research fees.
Emphasized the importance of platform services in the tech industry and the lack of fast platforms and horizontal systems.
Pointed out the need for vertical integration in the industry.
Challenges of building platforms for different customers due to dissimilar work across products.
Argument against common platforms based on the premise that hardware variability is specific to each company or modality.
Engineering of organisms discussed with contrasting beliefs on commonality.
Analogy to human design computation highlights discovery of common systems with shared building blocks.
Emphasis on the need for data analysis and eventual commonality in AI development in Pharma, with delays in AI-discovered drugs attributed to ongoing discussions and evolving technologies.
Importance of data availability for training neural networks and challenges of limited data sets.
Ginkgo's expertise in infectious diseases and data generation process are emphasized for neural network training.
COVID-19's impact on modern healthcare systems and the need for improved pandemic preparedness are highlighted.
Suggestions for building rapid vaccine response systems and implementing monitoring systems for early warnings are proposed.
Importance of rapid response and monitoring for infectious diseases like COVID-19.
Programs collect wastewater from inbound airplanes to monitor for pathogens and variants.
Emphasis on quick action to prevent disease spread, drawing parallels to cyber security measures.
Success of rapid response in shutting down SARS outbreaks.
Significance of early detection in effectively combating diseases like COVID-19.
Risks associated with AI and Foundation models in biology.
Potential for malicious actors to create infectious and deadly viruses is a concern.
Difficulty in accumulating data on certain biological aspects emphasizes the need for proper monitoring and rapid response during pandemics.
Implementing global monitoring and quick vaccine generation are essential for effective pandemic response.
Importance of biosafety levels in labs handling dangerous viruses highlighted by lessons from COVID, stressing the need for preparedness and proactive measures against potential threats.
Importance of biosafety levels in working with viruses and agents for gene therapy purposes.
Emphasis on the rarity of lab leaks compared to poor behavior in labs.
Discussion on evolutionary defense mechanisms in organisms from solar radiation exposure.
Challenges faced in funding and industry perception during startup journey.
Unique decisions made in the startup and the entrepreneurial energy and training provided by programs like Y Combinator.
Importance of ethical decision-making in developing platforms for children involving drugs and medicine.
Cultural differences between Pharma and Tech industries, with Tech viewing Therapeutics as slow and unambitious.
Responsibility of controlling platforms impacting people's lives, with examples of founder's voting control like Mark Zuckerberg and alternative control by capital markets.
Ginkgo's approach of extending super voting shares to all employees for collective ownership and control of the platform.
Importance of company leadership and governance in relation to employee satisfaction and long-term commitment.
Impact of CEOs making unpopular decisions and the dynamics of answering to employees versus a board.
Introduction of share voting as a method to accumulate more shares within a company.
Challenges faced by hired gun CEOs and the significance of moral authority in decision-making.
Exploration of unique governance models and the balance between popularity contests and tough decision-making.
Exploring the intersection of biological inspiration and AI architecture.
Questioning the efficiency of neural networks and potential for alternate approaches.
Highlighting the complexity and innovation of biological processes.
Emphasizing the need for open-mindedness and creativity in designing advanced AI systems.
Discussing the lessons that can be applied to technological advancements from biological systems.
Importance of hyper optimization for energetics and other functionalities in code writing.
Learning from Evolution to create more evolvable systems, using examples like the adaptive nature of skin and nerves to bones.
Efficiency of exploring body plans through Evolution's layered approach and untapped potential for scientists in exploring alternative architectures.
Advice to wrap skin around bones in any crazy mixture of exports routing.
Segment concludes with social media and podcast promotion.