xAPI-enabled Mobile Health System with Context-Awareness & Recommendation Engine for Patients

1 Comment…

  1. Machine Learning(ML) has already in our daily life, real use cases include detecting spam email, credit card fraud, and customer service chat bot. Salesforce just announced an intelligent assistant called Einstein in its CRM to help customers close deals smarter, and also partnership with Cisco IoT solution last week. The former is leveraging ML and other techniques, the later is leveraging sensor network enabled by current technology.
    ML is a sub-domain of A.I., the advance of computing power and API technology makes it much more available to work for us, in real time. It’s powerful because it will “learn” from data itself without explicitly programming to do things for you, but every model needs “training” for specific purpose. Volume data is important as its fuel. If data are siloed, we can’t build machine intelligence.
    In my opinion, dataviz is the first step after we have xAPI data, and ML is 2nd priority to gain “smart” from xAPI data much more efficient. And we can build “Personal Learning Companion” in learning systems such as LMS, with the same system architecture explained in this article.

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