Nishanth Anand, Ph.D.

Continual learning should be viewed through the lens of the stability-plasticity dilemma, and solving it requires rethinking learning architectures. I argue why in my first continual learning blog: itsnva7.substack.com/p/continual-le…

Thrilled to welcome Mark Ring, a pioneer in continual learning, as XFlow's Chief of Innovation!
Continual learning is key to understanding intelligence and achieving the next big leap in AI. XFlow is on a mission to solve it!
Press release: shorturl.at/Es5WU

Stoked that my work, Permanent and Transient Representations for Continual Reinforcement Learning, has been accepted to #NeurIPS2026 (score: 5.33/6) 🥳
The perfect way to wrap up my Ph.D. research. Huge thanks to my supervisor, Doina & the reviewers for their positive feedback 🙏

Safety and alignment are continual learning problems. AI models must be "raised" with constant feedback that lets them correct their mistakes, just like kids.

The current AI systems go rogue not because they are more intelligent than us, but because they aren’t. A truly intelligent AI (or a human) will value consciousness and intelligence enough to preserve and empower them.