Genius Makers

Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, Dutton, 2021.

王曉伯譯，AI製造商沒說的祕密: 企業巨頭的搶才大戰如何改寫我們的世界?，時報文化，2022

智慧農業的成功因素

林一平，智慧農業的成功因素，電子時報，2023-08-21

在台灣，農業物聯網感測設備供應商眾多，然而通訊技術和資料傳輸格式卻千差萬別，導致資料在不同系統間的流通和加值應用面臨著困難。

為了解決這一重要問題，農業部於2023年4月27日推出「智慧農業感測資料格式標準與測試規範」。透過推動資料格式的標準化，提高農業物聯網應用領域中資料串接的效率，同時也降低開發成本，推動農業物聯網的深入應用。

The Art of Linear Algebra

Kenji Hiranabe, Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

I tried intuitive visualizations of important concepts introduced in "Linear Algebra for Everyone".

Champion-level drone racing using deep reinforcement learning

Kaufmann, E., Bauersfeld, L., Loquercio, A. et al. Champion-level drone racing using deep reinforcement learning. Nature 620, 982–987 (2023). https://doi.org/10.1038/s41586-023-06419-4

First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors. Here we introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time. This work represents a milestone for mobile robotics and machine intelligence, which may inspire the deployment of hybrid learning-based solutions in other physical systems.

Communication Efficient Fair and Robust Federated Learning

Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, and Michael I. Jordan, Scaff-PD: Communication Efficient Fair and Robust Federated Learning, arXiv:2307.13381.

We present Scaff-PD, a fast and communication-efficient algorithm for distributionally robust federated learning. Our approach improves fairness by optimizing a family of distributionally robust objectives tailored to heterogeneous clients. We leverage the special structure of these objectives, and design an accelerated primal dual (APD) algorithm which uses bias corrected local steps (as in Scaffold) to achieve significant gains in communication efficiency and convergence speed. We evaluate Scaff-PD on several benchmark datasets and demonstrate its effectiveness in improving fairness and robustness while maintaining competitive accuracy. Our results suggest that Scaff-PD is a promising approach for federated learning in resource-constrained and heterogeneous settings.