Charlie Blake from Graphcore’s research team discusses their AI Papers of the Month for January 2024.  Graphcore research has been collating and sharing a review of the most consequential AI papers internally, every month, for a number of years.  Now – for the first time – the research team is making this valuable resource public, to help the wider AI community keep up-to-date with the most exciting breakthroughs.  Papers of the Month for January 2024 (with some work from December 2023) includes:  Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding  https://arxiv.org/abs/2312.05328 Authors: Talfan Evans, Shreya Pathak, Hamza Merzic, et al. (Google DeepMind, UCL)  Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws https://arxiv.org/abs/2401.00448 Authors: Nikhil Sardana and Jonathan Frankle (MosaicML)  Analyzing and Improving the Training Dynamics of Diffusion Models https://arxiv.org/abs/2312.02696 Authors: Tero Karras et al. (Nvidia, Aalto University)  Solving olympiad geometry without human demonstrations https://www.nature.com/articles/s41586-023-06747-5 Authors: Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He and Thang Luong (Google DeepMind, New York University)  To read about January’s Papers of the Month, visit the Graphcore blog. https://www.graphcore.ai/posts/great-teachers-and-beyond-chinchilla-papers-of-the-month-jan-2024