I work at a market research firm and ever since the semester started I’ve begun to think about how disruptive technology will affect the particular industry I work in currently. Within my firm I work in the media and entertainment department which is why when it came to choosing what industry I wanted to focus my machine learning project on, I jumped at the opportunity to focus on entertainment. Spencer made a good point in one of our first classes, that when machines take over and people are out of jobs, there will always need to be a solid entertainment industry to keep people busy.
Google gives us a goo idea of what entertainment is like for us now because of the machine learning that how technology uses. It creates a more personalized experience for us and allows us to watch our content when we want.
According to Forbes, there are six major digital transformations in the media and entertainment industry. Multi-channel experiences are the norm now; Accenture Digital did a study that showed most people obviously use different devices to watch certain things, but often, people are viewing content on their devices simultaneously. Creators are becoming scared that it’s not necessarily the content that people care for, it’s more about the convenience. This is where the AI comes into play. AI is getting more and more creative, according to Forbes. The technology is used in the entertainment world in many ways, one being to create plots of shows and movies based off of box office ratings. This new wave of computer-human collaboration is already working effectively within the industry. The result of this collaboration and integration of AI in the entertainment industry is that the computer will learn how to collect box office ratings and Nielson TV ratings data on its own to then create a plot and compile a final trailer for review all within 24 hours, which is significantly less than the average 30 days spent editing manually.
A prime example of machine learning specifically in the entertainment industry is located within Netflix. Based on what you watch, Netflix recommends shows or movies that are similar to the shows you have watched or are currently viewing. To that point, in 2013, Netflix released “Max” your personal recommender on the app. However, this feature crashed and burned due to the fact that “Max” didn’t sync properly with consumers Netflix accounts making is results less and less accurate and his recommendations poor. This sent Netflix back to the drawing board to see how to use machine learning to their advantage.
Similarly to Netflix, most companies are researching how to benefit from AI and machine learning as both technologies become more prominent. There is a lot of research and development regarding machine learning for entertainment companies and they seem to have their finger on the pulse, as of right now.