Nikhil Kunapuli

A study of safe exploration and robustness to distributional shift in biological complex systems

Nikhil Kunapuli is doing independent deconfusion research for AI safety. His approach is to develop better foundational understandings of various concepts in AI safety, like safe exploration and robustness to distributional shift, by exploring these concepts in complex systems science and theoretical biology, domains outside of machine learning for which these concepts are also applicable. To quote an illustrative passage from his application:

When an organism within an ecosystem develops a unique mutation, one of several things can happen. At the level of the organism, the mutation can either be neutral in terms of fitness, maladaptive and leading to reduced reproductive success and/or death, or adaptive. For an adaptive mutation, the upgraded fitness of the organism will change the fitness landscape for all other organisms within the ecosystem, and in response, the structure of the ecosystem will either be perturbed into a new attractor state or destabilized entirely, leading to ecosystem collapse. Remarkably, most mutations do not kill their hosts, and most mutations also do not lead to ecosystem collapse. This is actually surprising when one considers the staggering complexity present within a single genome (tens of thousands of genes deeply intertwined through genomic regulatory networks) as well as an ecosystem (billions of organisms occupying unique niches and constantly co-evolving). One would naïvely think that a system so complex must be highly sensitive to change, and yet these systems are actually surprisingly robust. Nature somehow figured out a way to create robust organisms that could respond to and function in a shifting environment, as well as how to build ecosystems in which organisms could be free to safely explore their adjacent possible new forms without killing all other species.

Nikhil spent a summer doing research for the New England Complex Systems Institute. He also spent 6 months as the cofounder and COO of an AI hardware startup, which he left because he decided that direct work on AI safety is more urgent and important.

I recommended that we fund Nikhil because I think Nikhil’s research directions are promising, and because I personally learn a lot about AI safety every time I talk with him. The quality of his work will be assessed by researchers at MIRI.