Marco Iansiti and Karim R. Lakhani, Competing in the Age of AI, Harvard Business Review, January-February 2020, pp. 61-67.
Some key points:
Removing Limits to Scale, Scope, and Learning
Strategies are shifting away from traditional differentiation based on cost, quality, and brand equity and specialized, vertical expertise and toward advantages like business network position, the accumulation of unique data, and the deployment of sophisticated analytics.
Putting AI at the Firm’s Core: One strategy, A clear architecture, The right capabilities, An agile “product” focus, Multidisciplinary governance.
這讓我想到這位教授，看了一下，跑到了Amazon 。各位可以看一看，這一篇有趣的文章。
Staff writer, How distinguished scientist Garrett van Ryzin is optimizing his time at Amazon, Amazon, October 14, 2020.
Prior to Amazon, van Ryzin was a professor of Operations, Technology and Information Management at Cornell Tech, and previously the Paul M. Montrone Professor of Decision, Risk, and Operations at the Columbia University Graduate School of Business. His university research work has focused on algorithmic pricing, demand modeling, and stochastic optimization.
van Ryzin was also the head of marketplace optimization at ridesharing companies Lyft and Uber, where he led teams that developed models for a variety of functions, such as optimally dispatching drivers to riders, and developing pricing models and driver pay systems that improve market efficiency. Interestingly, van Ryzin’s paper that he wrote while pursuing his PhD at MIT "A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane” imagined a world of on-demand transportation as far back as 1991.
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.
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.
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.