Choose Your Weapon: Survival Strategies for Depressed AI Academics

By Julian Togelius, Georgios N. Yannakakis. 2023

18 citations recorded by the source corpus.

Are you an AI researcher at an academic institution? Are you anxious you are not coping with the current pace of AI advancements? Do you feel you have no (or very limited) access to the computational and human resources required for an AI research breakthrough? You are not alone; we feel the same way. A growing number of AI academics can no longer find the means and resources to compete at a global scale. This is a somewhat recent phenomenon, but an accelerating one, with private actors investing enormous compute resources into cutting edge AI research. Here, we discuss what you can do to stay competitive while remaining an academic. We also briefly discuss what universities and the private sector could do improve the situation, if they are so inclined. This is not an exhaustive list of strategies, and you may not agree with all of them, but it serves to start a discussion.

Subfields: Health Informatics, Information Systems and Management, Management Information Systems, Philosophy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Raymond Li
2. Siva Reddy
3. Tri Dao
4. Dzmitry Bahdanau

In Philosophy

1. Qian Liu
2. Siva Reddy
3. Christopher Akiki
4. Yacine Jernite
5. Thomas Wolf
6. Marc Marone

Citations in the corpus

• StarCoder: may the source be with you! (2023, background): “While API access allows researchers to experiment with these models, it limits their ability to research LLM safety (Perez et al., 2022), inspect the models’ inner workings (Olsson et al., 2022), and contribute to model improvements (Togelius & Yannakakis, 2023).”