Konstantine Buhler
Nathan Benaich
Partner at
General Partner at
Chosen research paper:
Chosen research paper:
Released in Apr 2021
Released in January 2023
Generative Agents: Interactive Simulacra of Human Behavior
Large Language Models Generate Functional Protein Sequences Across Diverse Families
Stanford University - Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein
Profluent, Salesforce - Ali Madani, Ben Krause, Eric Greene, Subu Subramanian, Benjamin Mohr, James Holton, Jose Luis Olmos Jr, Caiming Xiong, et al.
Why it’s important:
Why it’s important:
"In this paper, the team out of Stanford places several generative agents in a shared digital world somewhat similar to the game Sims. These agents, built on LLMs, interact with each other. The interactions are surprisingly realistic, including a coordinated Valentine's day party. If the AI revolution is a continuation of the personal computer revolution, as in a revolution of computation, prediction, and work, then this type of multi-agent interaction is reminiscent of the early days of PC-networking, which eventually led to the Internet."
"Madani et al. demonstrate how a language model architecture originally designed for code can be adapted to learn the language of proteins. Through large-scale training, they use a protein language model (ProGen) to create artificial protein sequences that encode functionality that is equivalent to or better to naturally occurring proteins. This means we can generate proteins (drugs or otherwise) with desired functions in a far more systematic way than ever before."
Heartcore Capital – AI & Productivity Report
heartcore.com