Choose Your Weapon: Survival Strategies for Depressed AI Academics
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2023.
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Citing papers per year: 2023: 6; 2024: 2.
8 papers.
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- 2024Symmetric Dot-Product Attention for Efficient Training of BERT Language Modelscited by 5
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- 2023Collectionless Artificial Intelligencecited by 2
- 2023Semantically Grounded QFormer for Efficient Vision Language Understandingcited by 2
- 2024An Introduction to Cognidynamicscited by 0
- 2024An Introduction to Cognidynamics18 Aug 2024
- 2024Symmetric Dot-Product Attention for Efficient Training of BERT Language Models10 Jun 2024
- 2023Semantically Grounded QFormer for Efficient Vision Language Understanding13 Nov 2023
- 2023First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models8 Nov 2023
- 2023Collectionless Artificial Intelligence13 Sep 2023
- 2023Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research29 Jun 2023
- 2023C-MCTS: Safe Planning with Monte Carlo Tree Search25 May 2023
- 2023StarCoder: may the source be with you!9 May 2023