Interesting article! Your question about striving to not self-reinforce bias in content is a great one. The main issue I see with Netflix’s use of machine learning is that AI can’t deduce or create data from something that doesn’t yet exist (whether it be a genre, new type of show/movie, more diverse casting or characters, etc.). I hope this is something Netflix is taking into account when using their existing data to determine which new shows/movies to green-light. You also brought up an excellent point that some of Netflix’s competitors have access to lifestyle data beyond just viewing data. I imagine there is a correlation here as well, the question in my mind is this: is there enough value in additional purchasing and customer behavior data to justify Netflix looking at data outside of their own compiled information?
This was very interesting to read. You made great points and brought up fantastic questions. I think the tradeoff between customer experience and invading customer’s privacy will be at the heart of the issue Disney will face when they utilize machine learning to model customer behavior. The difference in how they use the data, on an individual level per person or by overall trends for a type of visitor, will be crucial to how I view your question. I imagine they will lean towards generalizing trends by user type, in which case I see less of an issue on the invasion of privacy front. This is definitely something Disney will have to keep an eye on as they move forward with machine learning technologies.
Instead of asking how Buzzfeed can become a more credible news source, I wonder if the question should be whether it’s in their best interest to do so? The diligence required to verify the factual accuracy of news stories often runs counter to the benefits that are gained by crowdsourcing ideas. Buzzfeed’s strength in open innovation seem to be on the creative and entertainment side of content historically. It would be interesting to see how reliable their content is and how much they engage with open innovation during the news cycle as they make the jump to news.
Great read. Seeing the growing trend of Wall Street pushing for and someday relying upon AI will be interesting to watch. Especially in terms of how it impacts efficiency, expected returns, and the role of traders within the system. When I read this, I thought of Watson identifying Toronto as a US City. A single misstep by AI of this nature, while understandable given the logic within the algorithm, could have major financial ramifications when used in trading. I wonder how firms will be able to address these risks and satisfy potential customer concern about the use of AI, beyond just in forecasting.
The concept of engineering the perfect show is fascinating! Your recommendation to break future original content into a data driven and people driven bucket is something I completely agree with. I love the idea of using data to shape the design of a show. However, creativity and inspiration in the world of TV shows and movies often comes from things that have never been done before. This is where data can not advice Netflix.
This article provided a very interesting insights on the impact of additive manufacturing in fashion. You brought up great points that are at the heart of the fashion including the speed of turnaround, individuality of design, and the price points based on quality and prestige. I wonder if we will see 3D printing utilized more in high fashion or quick fashion such as Gap or Zara. In addition to the manufacturing topics regarding this trend, the marketing that goes along with it will be interesting to watch.