Will AI be used to engineer deadly viruses? | Dmitry Korkin and Lex Fridman

TL;DR

Machine learning methods, like AlphaFold, have the potential to be used in protein engineering and virus design, although current data limitations exist.

Transcript

on the engineering side maybe a dark question but do you think it's possible to use these machine learning methods to start to engineer proteins and the next question is uh something quite a few biologists are against some are for for study purposes is to engineer viruses do you think machine learning like something like alpha fold could be used to... Read More

Key Insights

• 🦻 Protein design is an important field, where machine learning can aid in predicting structure and function.
• 🎰 Machine learning algorithms have the potential to determine the pathogenicity of virus strains for vaccine and drug development.
• 🎰 Engineering viruses using machine learning is still in the early stages, with challenges such as data availability and regulatory concerns.
• 💁 Transparency and information sharing can help mitigate risks associated with virus engineering.
• ❓ Natural pandemics are still a greater concern than engineered pandemics, given the efficiency of nature in producing harmful viruses.
• 🌐 Despite concerns, there is hope that increased transparency and global connectivity will lead to a more balanced and less secretive world.
• 🎖️ The secrecy of government and military secrets may become less prevalent in the future, with a shift towards transparency and openness.

Questions & Answers

Q: Can machine learning methods be used to engineer proteins?

Yes, protein design is an active research area, and machine learning algorithms can aid in predicting protein structure and function.

Q: Is it possible to use machine learning to engineer viruses?

While it is still in the early stages, there is potential to use machine learning to determine the pathogenicity of virus strains and study their molecular determinants.

Q: Can machine learning algorithms predict the components that make a virus more or less pathogenic?

Machine learning algorithms can help identify the molecular determinants of virus strains that contribute to their pathogenicity, which can be crucial for vaccine and drug development.

Q: Is there enough data for machine learning in virus engineering?

While data limitations currently exist, ongoing research aims to develop machine learning algorithms to assess the pathogenicity of virus strains, including coronaviruses.

Summary & Key Takeaways

• Protein design is a prominent area of research, including the ability to control protein structure and function.

• Machine learning algorithms can help determine the pathogenicity of virus strains, aiding in vaccine development and antiviral drug design.

• Using machine learning to engineer viruses is still in the early stages, with challenges such as data availability and regulation.