Garrett van Ryzin talks about optimization

以前教營收管理的時候，讀了不少哥倫比亞商學院 van Ryzin 教授的文章，其中一篇說，他們的研究是在解決10年後的問題。各位注意喔，是商學院！

最近在讀一本書，某教授寫的序，開頭四個字，就是學用落差。

Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among Adults

Drew Wilimitis, Robert W. Turer, Michael Ripperger, et al., Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among Adults, JAMA Netw Open. 2022; 5(5):e2212095. doi:10.1001/jamanetworkopen.2022.12095.

In this cohort study of 120 398 adult patient encounters, an ensemble learning approach combined suicide risk predictions from the Columbia Suicide Severity Rating Scale and a real-time machine learning model. Combined models outperformed either model alone for risks of suicide attempt and suicidal ideation across a variety of time periods.

外送的經驗

之前看到一個新聞報導，因為薪水不錯，而且自由，台灣的年輕人喜歡從事外送工作。

二三年前，聽到一個國外新聞，實驗外送機器人，時速30公里的機車，可以在1公尺內完全煞車停止。今天上課的時候，跟同學分享這一個報告，忍不住地提醒同學，如果畢業後，都是從事這個行業，也少了人際間的互動；等到幾年後，公司大規模使用外送機器人而裁員，你大學學的東西全忘了，如何轉職？

2021 Tesla Impact Report

2021 Tesla Impact Report

Autopilot Safety

In 2021, we recorded 0.22 crashes for every million miles driven in which drivers were using Autopilot technology (Autosteer and active safety features). For drivers who were not using Autopilot technology (no Autosteer and active safety features), we recorded 0.77 crashes for every million miles driven. By comparison, NHTSA’s most recent data shows that in the United States there are 1.81 automobile crashes for every million miles driven.

Outracing champion Gran Turismo drivers with deep reinforcement learning

Wurman, P.R., Barrett, S., Kawamoto, K. et al. Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602, 223–228 (2022). https://doi.org/10.1038/s41586-021-04357-7.

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. Here we describe how we trained agents for Gran Turismo that can compete with the world’s best e-sports drivers. We combine state-of-the-art, model-free, deep reinforcement learning algorithms with mixed-scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing’s important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world’s best Gran Turismo drivers. By describing how we trained championship-level racers, we demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.