April 4, 2018
Physical retail is widely believed to be playing a diminishing role in modern lifestyle. Nonetheless, the most powerful consumer market in the world shows that the future of retail is at the convergence of online and offline. Amazon + Whole Foods, Alibaba + Sun, Tencent’s WeChat Pay and its growing family of retail powerhouses – all are defining the winning strategy in global commerce and shaping the future of retail.
WeChat payment, whether in retail, catering or other formats, is gradually promoting and cooperating with the entire retail industry in China, including department stores, supermarkets, and convenience stores. There is no one format that does not support WeChat payment, Bai Zhenjie, Operations Director of WeChat Pay said in a press conference at Tencent’s smart unmanned retail industry conference held on March 30.
On top of these open platforms, including Tencent Cloud, social advertisements, and mini programs, Tencent’s smart retail solution is optimized through various tools, big data, and intelligent identification of users to further help them improve efficiency and optimize user experience. In the first stage, Tencent will cooperate with industry leaders that are large in scale, digitally capable, and resourceful, especially in supermarkets, convenience stores, shopping malls, and chain stores to provide digital solutions.
Tencent showcased some retailers in unmanned convenience stores, or vending machines, including EasyGo, Miss Fresh, and CityBox. Three companies were using WeChat payment and mini program to operate their businesses.
EasyGo Convenience store asks customers to first scan the QR code to open the door. The assorted goods in the store all have embedded RFID chips. As a customer walks in, grabs an item, and leaves the store, the storefront scanner reads the RFID chip and sends the bill to the mini program in the customer’s WeChat app. The customer can pay via WeChat and then leave the store.
The Swiss researchers found four distinct Bitcoin bubbles that correspond to the Log-Periodic Power Law Singularity model, which were followed by crashes or strong corrections.
Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of Bitcoin, suggesting many months of volatile sideways bitcoin prices ahead (from the time of writing, March 2018). – Spencer Wheatley & Didier Sornette, both professors of entrepreneurial risks at ETH Zurich
We emphasize that one should not focus on the instantaneous and rather unpredictable trigger itself, the paper said, but monitor the increasingly unstable state of the bubbly market, and prepare for a correction.
Read more (+ the research paper called Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe’s Law and the LPPLS Model).
Poor data quality is enemy number one to the widespread, profitable use of machine learning. While the caustic observation, garbage-in, garbage-out has plagued analytics and decision-making for generations, it carries a special warning for machine learning. The quality demands of machine learning are steep, and bad data can rear its ugly head twice – first in the historical data used to train the predictive model and second in the new data used by that model to make future decisions.
To properly train a predictive model, historical data must meet exceptionally broad and high quality standards. First, the data must be right: It must be correct, properly labeled, de-deduped, and so forth. But you must also have the right data – lots of unbiased data, over the entire range of inputs for which one aims to develop the predictive model.