(年輕人) 如何脫離貧窮

一般人都可以經由努力而達到的方法 (註 12) 

找工作可分成學校教育、個人努力、企業、和社會大環境等四大面向。不論是 22 K 政策、學費、企業的在職訓練等等，都不是我們可以掌握的。當大環境不容易改變，個人只有自求多福。所以，本文希望能夠提供一些可行的辦法，以幫助年輕人。方法是在大學四年 (和終身學習) (註 1)，培養三大類的能力，一是一般的能力，語言、電腦使用，二是專業，三是強化個人特質和團隊合作精神。有了這些能力，也才有機會選擇自己喜歡的工作。 

Interpretable predictive maintenance for hard drives

Maxime Amram, Jack Dunn, Jeremy J.Toledano, and Ying Daisy Zhuo, Interpretable predictive maintenance for hard drives, Machine Learning with Applications, Volume 5, 15 September 2021, 100042.

Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high predictive power yet cannot be understood by humans. This limits the ability of humans to use these models to derive insights and understanding of the underlying failure mechanisms, and also limits the degree of confidence that can be placed in such a system to perform well on future data. We consider the task of predicting hard drive failure in a data center using recent algorithms for interpretable machine learning. We demonstrate that these methods provide meaningful insights about short- and long-term drive health, while also maintaining high predictive performance. We also show that these analyses still deliver useful insights even when limited historical data is available, enabling their use in situations where data collection has only recently begun.

Deep Learning for AI

Yoshua Bengio, Yann Lecun, and Geoffrey Hinton, Deep Learning for AI, Communications of the ACM, July 2021, Vol. 64 No. 7, Pages 58-65.

Research on artificial neural networks was motivated by the observation that human intelligence emerges from highly parallel networks of relatively simple, non-linear neurons that learn by adjusting the strengths of their connections. This observation leads to a central computational question: How is it possible for networks of this general kind to learn the complicated internal representations that are required for difficult tasks such as recognizing objects or understanding language? Deep learning seeks to answer this question by using many layers of activity vectors as representations and learning the connection strengths that give rise to these vectors by following the stochastic gradient of an objective function that measures how well the network is performing. It is very surprising that such a conceptually simple approach has proved to be so effective when applied to large training sets using huge amounts of computation and it appears that a key ingredient is depth: shallow networks simply do not work as well.

Boeing's 737 MAX: A Failure of Management, Not Just Technology

Michael A. Cusumano, Boeing's 737 MAX: A Failure of Management, Not Just Technology, Communications of the ACM, January 2021, Vol. 64, No. 1, Pages 22-25.

The congressional report had extensive access to company email and documents as well as detailed media coverage. These sources all describe the same decisions along with gradual but fundamental changes in Boeing's strategy and culture.

The Future of Supply Chains

Paul Marks, The Future of Supply Chains, Communications of the ACM, July 2021, Vol. 64, No. 7, Pages 19-21.

Today's supply chains are labor-intensive and expensive to run. A number of autonomous systems that reduce the human factor are about to change all that.

What do the sidewalks around us, the airspace above us, interstate freeways, and deep ocean shipping lanes have in common? The answer is that they are all places where developers of autonomous technology are trying to revolutionize the economics of supply chains. The plan is to use robotic technology to deliver anything from packages to take-out food, groceries, or bulk freight in ways that can reduce the logistics industry's dependence on that most expensive of supply chain costs: human labor. if the use of electric drivetrains can cut carbon emissions too, so much the better.