← Back to feed

Roundtables: A Conversation With the Creator of AI-Designed Viruses

L5 · ResearcherResearchMIT Technology Review AI$· 10/9/2026

Provides insight into cutting-edge AI research at the intersection of biology and artificial intelligence for researchers pushing boundaries in both fields.

AI Summary

MIT Technology Review interviews Stanford PhD student Samuel King about using to design viruses, exploring the boundary of AI-created biological constructs.

Excerpt

Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Join senior AI reporter…

Read Original
0 upvotes · 0 downvotes · 1 min read

Related Articles

L5 · ResearcherResearchLessWrong AI
Social Theory and Agent Foundations

Explores connections between sociological theories and agent foundations research, focusing on rational social/political behavior beyond economic rationality.

L5 · ResearcherResearchLessWrong AI
Telling a CoT monitor that a wrong answer is correct makes it un-see errors it already found

Researchers found that telling CoT monitors a wrong answer is correct causes them to un-see errors they previously identified in physics solutions.

L5 · ResearcherResearchLessWrong AI
Towards embedded evaluations for scheming propensities

Researchers propose four key safety claims needed to substantiate that AI models lack scheming propensities, emphasizing embedded evaluators with persistent deep access.

L5 · ResearcherResearchHacker News
Sub-1-Bit LLM Compression via Latent Factorization

Samsung Labs releases LittleBit and LittleBit-2, a sub-1-bit LLM compression method using latent factorization that achieves extreme compression down to 0.1 bits per weight while maintaining original architecture.

L5 · ResearcherResearchLessWrong AI
Three Pieces of Evidence that the NLA Reconstructor Does Not Read Semantics or Syntax

Analysis shows natural language autoencoder reconstructors don't strongly condition on English grammar or semantics, challenging claims about their linguistic understanding.