The following examples were extracted from Forrest (2017):  Google's Cloud Machine Learning Engine and Tensor Flow allow unique access to machine learning tools without the need for PhD-educated data scientists. The following companies use Google's tools to solve the listed problem.
1. Axa International. This global insurance company uses machine learning to predict which drivers would be more likely to cause major accidents. The analysis provides prediction accuracy of 78 percent. This prediction is used to determine appropriate insurance premiums.
2. Airbus Defense & Space. Detecting clouds in satellite imagery was done manually for decades. Using machine learning, the process has been expedited by 40 percent, and the error rate has been reduced from 11 percent to 3 percent.
3. Preventing overfishing globally. A government agency previously monitored only small sample regions globally to find fishing violators. Now, using satellite AIS positioning, the agency can watch the entire ocean. Using machine learning, the agency can track all fishing vessels to find violators.
4. Detecting credit card fraud in Japan. SMFG, a Japanese financial services company, uses Google's machine learning (a deep learning application) to monitor fraud related to credit card use, with an 80-90 percent accuracy of detection. The detection generates an alarm for taking actions.
5. Kewpie Food of Japan. This company detected defective potato cubes manually using a slow and expensive process. Using Google AI tools enables it to automatically monitor video feeds and alert inspectors to remove defective potatoes.

Questions for case
1. Why use machine learning for predictions?
2. Why use machine learning for detections?
3. What specific decisions were supported in the five cases?