AI Papers to Read in 2025
These reading recommendations aim to help readers stay informed about both recent and classic breakthroughs in AI and Data Science. The author resumes their well-known series of curated AI paper suggestions published previously on Towards Data Science.
Background
The series has four earlier editions and has built a following among readers interested in critical perspectives on AI’s progress. After a break from writing, the author chose to return by reviving this acclaimed project, combining personal insights with broader analysis.
Purpose of the List
This collection does not highlight the latest state-of-the-art models but rather examines ten thought-provoking papers. Each recommendation is chosen for its contribution to AI understanding and includes a short rationale for why it deserves attention. Readers will also find supplementary reading paths for deeper exploration.
Philosophy of the Author
“We don’t need larger models; we need solutions.”
“Do not expect me to suggest GPT nonsense here.”
When the author first articulated these points in a 2022 article, they criticized the trend of endlessly scaling up GPT models. While admitting that newer iterations bring marginal improvements, the author notes that such expansions rarely achieve genuine innovation.
Conclusion
The article encourages critical thinking about AI’s overall trajectory rather than focusing solely on hype or trends. Readers are invited to engage with ideas that shape the discipline, both forward-looking and retrospective.
Author’s summary: The piece invites readers to explore ten insightful AI papers of 2025, emphasizing depth of understanding over model size or fleeting trends.