“Nitarshan is a PhD student in Cambridge, previously a student at MILA, a top machine learning lab in Canada. He is interested in topics of AI safety among others. He also has an impressive repertoire of about five things he can say in Hungarian (and maybe even more).”

“Beware of Nitarshan in general… He asks thought-provoking questions that will make you rethink your whole research agenda”

“Quite fun and interesting. Those are his parameters.”


Darwin among the Machines (1863)
Progress and Poverty (1879)
The Strenuous Life (1899)
Citizenship in a Republic (1910)
Economic Possibilities for our Grandchildren (1928)
The Human Use of Human Beings (1950)
Computing Machinery and Intelligence (1950)
Can We Survive Technology? (1955)
Man-Computer Symbiosis (1960)
All Watched Over by Machines of Loving Grace (1967)
Spring Snow (1969)
See How It Flies (1995)
Sidewinder: Creative Missile Development at China Lake (1999)
Why the Future Doesn’t Need Us (2000)
The Dream Machine (2001)
Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983-1993 (2002)

Policy Proposals

Independent Review of The Future of Compute
Website Spring Budget 2023
“In line with two of the key recommendations of the Future of Compute Review, the government will invest, subject to the usual business case process to finalise exact details, in the region of £900 million to establish a new AI Research Resource and to develop an exascale supercomputer, with initial investments starting this year. These investments will provide scientists with access to cutting-edge computing power and bring a significant uplift in computing capacity to the AI community.”

A New National Purpose: Innovation Can Power the Future of Britain
Website PDF Op-ed
Tony Blair, William Hague, Luke Stanley, James Phillips, Benedict Macon-Cooney, Nitarshan Rajkumar, Tom Westgarth, Jess Northend, Jeegar Kakkad
“The world is set for the fastest and most comprehensive period of innovation in the history of human civilisation…“

Underpaid Labour

GitHub | Google Scholar | Semantic Scholar | OpenReview

Reclaiming the Digital Commons: A Public Data Trust for Training Data
arXiv AIES 2023 Blog
Alan Chan, Herbie Bradley, Nitarshan Rajkumar

Harms from Increasingly Agentic Algorithmic Systems
arXiv FAccT 2023
Too many authors tbh

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
arXiv Code ICLR 2023 ICML 2022 Workshop (DataPerf)
Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker

Evaluating the Text-to-SQL Capabilities of Large Language Models
arXiv Code
Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau

Myriad: A Real-World Testbed to Bridge Trajectory Optimization and Deep Learning
arXiv Code NeurIPS 2022
Nikolaus Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon

Self-Supervision for Data Interpretability in Image Classification and Sample Efficiency in Reinforcement Learning
MSc Thesis
Nitarshan Rajkumar

Pretraining Representations for Data-Efficient Reinforcement Learning
arXiv Code NeurIPS 2021 ICLR 2021 Workshop (SSL-RL)
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, Devon Hjelm, Phil Bachman, Aaron Courville

In Search of Robust Measures of Generalization
arXiv Code NeurIPS 2020
Karolina Dziugaite, Alex Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Dan Roy

Unpaid Labour

Weight Uncertainty in Neural Networks