AI Papers to Read in 2025
This article offers curated reading suggestions to keep you informed on both recent and classic advances in AI and Data Science. Returning to Towards Data Science (TDS), the author revives a favored series focused on AI paper recommendations.
Long-term readers may remember the four previous editions, and after a writing hiatus, the author chose to resume the series that was both popular and personally enjoyable.
This curated list is highly opinionated, containing unique perspectives and relevant tangents designed to update readers on AI broadly. It is not intended as a catalog of state-of-the-art models, but rather as critical insight on important trends to watch and overlooked work from the past.
Purpose and Structure
The collection consists of ten selected papers. Each paper is accompanied by a concise summary of its contributions and clear explanations of why it merits attention.
Additionally, each entry includes a dedicated section for further reading, exploring related ideas and topics.
Author’s Reflection
"In my 2022 article, I stated ‘we don’t need larger models; we need solutions’ and ‘do not expect me to suggest GPT nonsense here.’ At that time, I predicted future GPT iterations would mainly be bigger and only slightly improved, not revolutionary. Nonetheless, credit where credit is due."
This reflection sets the tone for a critical approach to AI trends and emphasizes the value of thoughtful solutions over mere model scaling.
Summary
This collection aims to sharpen critical thinking about AI by highlighting significant papers with lasting impact, encouraging readers to consider deeper insights beyond hype and incremental improvements.
Would you like the full list of the ten papers with their summaries and further reading sections included?