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I can total resonate this when I spent hours to search the right plane tickets for vacation.

As illustrated in the article, the application of AI in the airfare business could largely reduce the time customers spend on the “treasure-hunting” experience. While I totally support such application, I’m wondering if JetBlue is the right platform to implement. JetBlue represents only a fraction of the routes available, while machine learning results based on the customers’ data and preferences may exceed the capability of JetBlue. In this case, JetBlue may provide the service they may have to accommodate the customers using promotions or other solutions, and this new data could tamper the customers’ selection model.

So I think the best application platform is a third-party platform, like Expedia. They could provide all the information based on the customers’ preference, and give them the best match as well.

On November 14, 2018, Zixin commented on Dream On: An Exploration of Neural Networks Turned Inside Out :

This article is very proudly written and very technically presents me how the future machine learning will go.
I believe Google’s DeepDream research is very meaningful since it’s trying to mimic a way how people think. But I think this is only the first step of the endeavor. Since the machine now can only process the information given and “think” using these information, they don’t have the ability to decide what they are doing, or find the “purpose” of their “thinking”. The self consciousness is pretty crucial in developing a real AI. I’m looking forward to seeing more advanced technology which could address this in the near future.

On November 14, 2018, Zixin commented on Your Personalized Dinner is Served :

The article focuses on a very interesting point to use machine learning to prepare the necessary ingredients for the home chef. One thing I though from the application design perspective is that actually the current recipes are countable, the main job the system should do is to analyze the customers’ appetite and recommend the recipe according to their needs. In terms of how to quickly collect initial data is to promote in the “gourmet” community first, and let the users to upload their favorite recipes, then to use machine learning to provide them new foods to select “like” or “dislike” to train the system.

It’s a very well written article! It clearly elaborates the current AI application and future AI potential for Alibaba’s omnichannel development strategy. Anything I believe Alibaba could take serious consideration is how to make competitive advantages in the long run. I trust that the continuous AI investment will pay back in the near future, nevertheless, as demonstrated in the Exhibit 5, all the global companies, including Chinese companies, are investing in AI. How to make a differentiated investment in the field is quite crucial. For instance, the investment in Cainiao’s AI product development is a distinguishably competitive, since Alibaba controls the major market share of the Chinese online orders, which mean that Alibaba controls the majority of the logistic data in China.

It’s a very comprehensive article to summerize the machine learning technology implementation at a top football club.
Along with the questions mentioned at the end of article, I have two more concerns:
1. The data gathered during the training may not fully reflect the whole picture of a player’s talent. You may see a player who is very moderate in training, but will be very energetic and excellent during an official match. So the hardware to capture the during match performance is mandatory to conduct this research.
2. The players have a learning curve, we can use machine learning to predict the players’ future potential based on their current data. This requires a big sample size, which includes data from the current stars and data from their youth. This may take a long time to gather, otherwise, the results now may somehow not very accurate.

It’s a very articulate article! I believe Starbuck China can benefit a lot from the future machine learning technology application mentioned in the article.
I would like to raise two concerns about the future implementation:
1. There’s only a handful of Starbucks beverages, and most of the customers will stick to one type of their “favorite” beverage. Will the recommendation of beverages be useless in this case?
2. The store location prediction is very useful. Nevertheless, because the Chinese city develops extremely rapid, most of the locations don’t have any previous data to analyze. If you only select location based on the data you previously had, you may lose a lot of chances to open a new store in a future CBD.