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    • Quite simply, it’s recommendations based on what you do rather than what you say you want.  For example, the most interesting thing I learned about Netflix’s recommender system is that it makes suggestions based on what you actually watch, not what you add to your queue: all those intellectually curious documentaries that have been sitting unwatched in your queue for six months won’t fool Netflix that what you really want is more comedy flicks.

      This is also described as explicit vs implicit ratings. Explicit means marking something as a 1-5 rating, clicking a like button, etc. Implicit ratings are deduced from monitoring people’s behavior. Illustration below from a quite helpful book on the subject: "Practical Recommender Systems"

    • Hi Stephen, Sorry for the slow response. I was out of the country for most of the last 3 weeks and largely offline during that time. Interest topic to kick around.

      It seems that monitoring our behaviors and extracting predictible patterns, which in turn can be converted into actionable insights that will drive future (buying) decisions is the holy grail of using big-data for marketing purposes. The discussion on explicit and implicit ratings touches on this. But where I think AI/ML/Big Data will largely fall a bit short is at the individual level. People are fickle. Statistically, what I did yesterday may have a strong influence on what I did today, but no AI or Big Data process will be able to monitor all of my behaviors and determine that I might wake up tomorrow and decide "today is the day that I am going to start doing X" or "ok... I'm kind of tired of binge watching netflix" or whatever. Maybe recommendation engines work well for many people, but I have yet to find one that works for me. Because I like one musical artist, does not mean I like some other musical artist that the recommendation engine thinks is closely related, because for me it may not be about "genre" but maybe about something else hidden deeper in the music. But I may just be an oddball.

      Where I see Big Data being really powerful is in sorting out problems that are ridiculously mult-variate. Better understanding how individuals in different populations will respond to different medical treatments, for example. Climate. Or for industrial applications where 2nd or 3rd order effects on individual variables interact to produce significant, but rare, ocurrances which can influence production efficiency, product quality, reliability, and safety. Marketing can fit into this category as well, across large demographics, predicting future trends based on current events, etc.. But I think it may be a fools errand to try to predict the behavior of Jane Smith as if she is nothing other than some algorithm that can be decoded given enough data, the right data scientists, and sufficient server farms.

    • our project is in education, trying to improve all aspects of the E-learning process through ML. Doing a research of the current landscape doesn’t show many results. Coursera seems to be more active with some efforts in the website (recommendations, search results) and mail marketing, but that’s about where things end from what I have seen.

      I thought the below graph was interesting. Manufacturing was generating 4X as much data as education ten years ago. Curious as to whether the ratio is still the same.

      Image credit

    • Although my understanding of data science is very limited, I hold a deep curiosity on how the use of data influences the human connection. My biggest concerns being how data is used by governments and business to influence the will of the users. Although there are countless success stories on the use of data to make our lives better there is a fundamental concern I see with a lack of user data rights and the manipulation of data through media outlets. Even though we have an incredible toolset before us I still believe we are a caveman trying to taste a burning branch struck by lightning. It looks awesome but do we really know what it is capable of?

      This notion of using data as a sword to disrupt industry is beginning to reveal itself a merely a means to monetize interests of users to build other industries off of. The hands wielding the sword grateful for such a favorable environment to sow their influence over users hungry for the carrot of convenience. So what does all of this have to do with your post? My contribution to this panel is to ask If you are to be a steward of the technology consider how the tech will impact the end user and possibly how your contribution will not only lead to the application of it but to possibly understanding the implications of it. Ultimately these technologies are not being built by huge corporations, they are being built by individuals who could eventually move the cheese to use cases favorable to the interests of the users.

      Thank you for inviting me to this panel Stephen. I have enjoyed reading the thread!!

    • But where I think AI/ML/Big Data will largely fall a bit short is at the individual level. People are fickle. Statistically, what I did yesterday may have a strong influence on what I did today, but no AI or Big Data process will be able to monitor all of my behaviors and determine that I might wake up tomorrow and decide "today is the day that I am going to start doing X" or "ok... I'm kind of tired of binge watching netflix" or whatever.

      Have you seen this ⬇️? It’s more than five years old, and I suspect the algorithms have improved significantly since then.

      They may not be able to predict when you’ll buy something, but there seems to be more and more data available to predict what you will buy.