Welcome!

I wanted to introduce my topic by first giving you some insight into what led me to pick this topic. First off, my family are big partakers in the entertainment industry. For as long as I can remember we always watched different movies together as a family and often reflected on them afterward. I am very familiar with the advancements in technology that involve the entire entertainment industry from the ability to have access to a program like Netflix at your fingertips to the incredible advancements in Virtual Reality technology. I wanted to focus on the changes that audience members will be presented with in terms of interacting with the storyline in media applications. As it currently stands, you simply choose the show or movie and watch what is in front of you. Some advancements that we can already use include the ability for programs (Netflix, Hulu etc) to predict what we may watch in the future. Although these are great advancements that we didn’t have just a few years ago there are still may more to come. For example, the ability to use machine learning to analyze story arcs to further predict the emotions that the audience will exhibit during the movie.

According to an article published by the McKinsey and Company, The Massachusetts Institute of Technology (MIT) Media Lab recently investigated the potential for such machine–human collaboration in video storytelling. Was it possible, our team asked, that machines could identify common emotional arcs in video stories—the typical swings of fortune that have characters struggling through difficult times, triumphing over hardship, falling from grace, or declaring victory over evil...MIT describes the process, we developed machine-learning models that rely on deep neural networks to ‘watch’ small slices of video- movies, TV, and short online features- estimate their positive or negative emotional content by the second…

With MIT playing their role in entertainment others like IBM’s Watson has also decided to take part. In an article by Futurum Research, Shelly Kramer describes the entertainment uses of such an advances technology like Watson. According to Kramer, “Watson was taken to film school as it analyzed hundreds of existing horror movie trailers to learn what kept viewers on edge before being fed the entire film cut of the upcoming movie”. Artificial Intelligence system creating a movie trailer? That is incredible!

The idea is that Artificial intelligence will change the way we create movies will uncover many implications.

For this web story I wanted to explore the present technology as well as what the future might look like.in the future play a key role in creating stories for our entertainment. This project will explore this technology with current articles and dive into the future implications through a prediction story. I hope you enjoy!

Current Technology

For the second piece to my web story I wanted to explore what is currently being done in the entertainment industry to give my audience a better idea about how realistic or not my proposed future is. Both technologies had many people working with them, but I wanted to focus on the authors of my two sources. My first article was created by McKinsey and Company, AI in Storytelling: Machine as Cocreators. The second was created by Shelly Kramer as the principle analyst and founding partner of Futurum Research, Machine Learning Already Changing the Entertainment Industry.

MIT is at the forefront of advancement in storytelling and technology. They have so far tested two main questions in relation to the usefulness of AI in the storytelling industry. First, they realized that by analyzing emotional arcs of stories, it gives the director the ability to strengthen their story. Second, AI technology can predict the outcome of what the audience will think of the content, so they can better prepare for a great outcome. MIT recognized that for the technology to refine a story they must understand what an emotional arc is. An emotional arc according to MIT authors, With an instinctive read of our pulse, they tune their story to provoke joy, sadness, and anger at crucial moments. But even the best storytellers can deliver uneven results, with some Shakespeare plays leaving audience members feeling indifferent or disconnected. These arcs not only need to be understood by the technology, but they need to be identified in the movie clips provided to it.

Emotional arcs are the emotional responses viewers have to the content they are watching. By having the strongest arc, it ensures that the audience can connect with the material in a new level. Directors want to be able to tap into this ability because it will produce the most engaging story. I mean who wants to watch a movie that makes you sad the whole time or only happy? Nobody, we like emotional variety which is where the arc idea comes in. MIT believed that they had the ability to harness the strength of these arcs by developing artificial intelligence that can identify them. Creators of the technology explained the process, MIT’s machine-learning models have already reviewed thousands of videos and constructed emotional arcs for each one. To measure their accuracy, we asked volunteers to annotate movie clips with various emotional labels. What’s more, the volunteers had to identify which video element—such as dialogue, music, or images—triggered their response. We used these insights to refine our models.

This is only one capability that will give directors and producers the opportunity to create more emotionally engaging stories for their audience.

Researchers wanted to make the test as valid as possible by confirming the emotional arcs by volunteers to prove their system was correctly identifying these arcs in the media they were provided with. Once refined, this ability for AI to identify emotions that are elicited by humans would mean that they could further be able to create movies that engage the audience on a whole new level. By changing one score in a certain scene there could be a huge difference in how the human reacts to the overarching theme.

Another ability the MIT creators found was the application of AI in sales projections based on what the media is trending on social media platforms. They took this technology to the social media platforms to see if it could predict the outcomes. The process is explained perfectly by the creators, For each story, we used a regression model to consider arc features while controlling for various metadata that can affect online reaction, such as the video length and upload date. The goal was to predict the number of comments a video would receive on Twitter and other social media. In most cases, a large volume of comments signals strong audience engagement, although there can be some caveats.

Why would predicting sales projections help the creative process? Well, if there was a trailer that was released by the creative workers and the AI recognized there was a bad trend they could in turn release another trailer to encourage the audience to attend the premier. This would change the way movies are advertised because with the help of AI, producers would have the ability to put their best foot forward to receiving a favorable response from their audience. In the future we could have AI projecting accurate movie production budgets. This would significantly impact the average number of productions that go over budget. While MIT is on the forefront of refining the uses of AI other companies are doing work that directly affects the way movies are created.

Different companies are taking it upon themselves to create the most exciting entertainment for users with the use of AI. Three technologies are up-and-coming in the entertainment industry including the machine learning program created by IBM, Watson, successful movie trailer to the unexpected uses of AI that gives users the ability to have a personalized recommendations list that was created based on common themes that were present in other content watched. Before the integration of AI some movies trailers took upwards of months to produce and publish, but with advancements in machine learning that process is being simplified. The article painted a clearer picture of this advancement, The entire process took about 24 hours to complete, compared to a 10 to 30 day, labor-intensive, manual edit that would be the norm. According to John R. Smith, multimedia and vision manager at IBM, the capacity to reduce a process from weeks to hours reveals the true power of AI.

This newfound free time would give creative parties the ability to create more inspiring films for the audience.

This time that was previously spent creating the trailer could also give the audience more time to get excited to see the movie. I would for sure be willing to wait for a movie long enough if the trailer was released sooner rather than later. But how does a program like Watson create such a trailer? The process of creating a trailer for new horror movie “Morgan” involved using machine learning techniques and experimental APIs through IBM’s Watson platform. Watson was taken to film school as it analyzed hundreds of existing horror movie trailers to learn what kept viewers on edge before being fed the entire final cut of the upcoming movie.

To be completely honest, I am not a huge fan of scary movies. But after I watched the “Morgan” trailer I was very excited and trilled in the best way. The trailer was perfectly done to get me excited and curious to figure out what will happen to this character. Other scary trailers that I watched had me holding my breath about the jump scares, but this trailer did them in such a way that you almost lean into the screen to find out the next move of the main character. I enjoyed Watson’s result very much. So, if I felt this way about the AI created trailer, who is to say that other trailers (preferably not scary movies) will encourage a better reaction from the intended audience? Would more people be engaged with a better trailer and therefore produce a wider range of viewers? Would their advertisement skyrocket because it complies the very best scenes in the movie? The future of this technology is unclear but is obviously going to be very useful to production sales. Programs like Netflix are using this technology in homes to give viewers the most appealing viewing experience.

Machine learning is already present in homes. Netflix and other similar companies are giving viewers more personalized content based on previously watched shows. The idea is the company wants them to use their application for as long as possible by giving them accurate suggestions (ever heard of the term ‘binging’). This is great news for companies like Netflix because the longer customers watch their content, the more money they make. A refined suggestion by a program is a tricky process that involves understanding previous content watched and the common threads that are presented with different people. Kramer explains it, Machine Learning is also helping entertainment providers recommend personalized content, based on the user’s previous viewing activity and behavior. Take Netflix for example…. We aren’t talking about simple viewing recommendations though—machine learning applications are also being used to fine tune the way the home page is presented to users, in particular the “Continue Watching” (CW) row. Using data based on factors such as subscription history, previous interactions with content, and even contextual features such as time of day and device, the content and placement of the CW row can be amended for maximum effect.

By simply watching what we like we are giving the AI Netflix uses information about what types of movies and shows we like to it can better refine my personal dashboard. What powerful stuff! This gives the user the ability to watch a wide variety of shows and movies without having suggestions that they do not like.

The only flaw I have found with this technology is...

the fact that it does change the entire dashboard based on what is watched. For example, I did not watch anime but because my roommate did, my Netflix feed is not filled with anime suggestions. This is great because she could use that information, but when I want to watch a cooking show it takes me more time to find it. This is a flaw in the system, but in the future, we may see that it advances to create a dashboard by what the user interacts with the most rather than any movie or show.

Artificial Intelligence, specifically machine learning, is versatile and going to make a huge difference in how the entertainment industry will produce media. From projection sale prices to the ability to accurately predict what you will chose to watch next. Both articles made a point to say that this is only the surface of the applications machine learning will have on this technology, the Machine Learning Already Changing the Entertainment Industry did mention that in the future we could see AI taking an active role in the creativity aspect of the storytelling and possibly movies created in less time. I look forward to the witnessing these applications take shape in the future.

How will this look in society?

Steven, the family housekeeping and companion robot, approached me and asked for access to the old family albums. I didn’t think much of it as he usually takes the liberty of updating the picture frames in the house as we create new family memories. Steven was a gift from my company for working on a particular project. He is a huge help around the house as he gives me an extra set of hands when my husband is gone, gives our family the ability to go on vacation as he watches our dog when we leave, and he even has permission to bond with the kids to help them with social skills they are otherwise lacking from being home-schooled.

Then to my surprise my youngest son approached me and informed me of a surprise he had created. Like every mother I was excited but also nervous it was going to be one of his infamous ‘chocolate’ pies he likes to share. Instead I was guided to the family room with my eyes closed. “Can I open my eyes now?” I asked my youngest son Angelo, “No mom not yet. Steven has to make some minor adjustments based on your current mood”. What is going on here I am thinking as I reclose my eyes to wait for the surprise I have been promised. Angelo is turning 5 this year and with everything that he has learned it feels like he is much older than his age. His ability to sit down and use technology as quickly and effectively as he does continues to amaze me. And yet here he is again surprising me with his latest creation.

“Okay, mom now you can open!” says Angelo. I slowly open my eyes and adjust to the bright screen handed to me. I smile because I can see a grinning-toothless Angelo staring back at me. He clumsily throws his leg over the couch to sit next to me. I help him while giggling at the scene and ask him what I should do. “Mom! It is easy! Even you can do it”, he says with a big grin. “Okay, here I go, surprise of the century”. As the video starts to play I am already tearing up. From pictures of me as a teenager to our first family vacation, I can see a plethora of images that show the happiness that our family emulates daily. Images of my first bon in the bathtub to little Angelo’s first birthday, I am witnessing again every moment that has given me joy since the birth of my children. After such an emotional roller coaster the video ends with a huge “Happy Mother’s Day” complete with digital fireworks. It is signed “From your favorite child (aka Angelo)”. I close the application and take a deep breath, “Wow baby that was amazing! Did you make it all by yourself?”

He ponders this for a minute and then looks over my shoulder to Steven not too far away. “Well, I was talking to Steven about Mother’s Day trying to come up with a fantastic idea to celebrate you and he suggested pictures of me and the family!” I stare at the robot behind me and nod a thank you. Angelo then proceeded to tell me the creative process. “I started by giving Steven all of the really old pictures you have and had him scan it into one file then I added them to the video software that daddy has and started to create a story!” he says with excitement, “I then had Steven use his special powers to make my movie as great as possible, he told me all the things he did but I didn’t listen much”. I look at the shiny metallic robot in a questioning tone and he proceeded to explain, “Well, ma ’me I wanted you to elicit happiness and excitement when Angelo showed you the video, so I looked through the main pictures for the ones that in the past made you the happiest. I then created a compelling story out of what I had, quite simple really”. I raise my eyebrows and look back at my beaming son, “You were right the best Mother’s Day gift of the century”, as I lean down and over a kiss on his forehead. “Let’s show daddy and see what he thinks of our little movie maker”.

Conclusion

Currently we already have the expertise and knowledge on how to give a program like IBM’s Watson the ability to create movie trailers and in the future current technology can skyrocket to mass media outlets and give society the ability to create moves in less time than current standards. I foresee the ability for Artificial intelligence to create movies with the help of humans will drive our entertainment industry to new heights. Projects will be more engaging for the audience, created faster, and allow directors to have more time to work on more than one at a time. Instead of the previously known years timeline to create complete movies (sometimes more than that) we can simplify the entire process to mere months. Image for a minute what that could look like. Our future holds the opportunity for more people to learn how to be creative with this technology which in turn will give the audience the ability to see more media in a shorter time. This creative freedom, as we have seen in the past, will give society a new outlet to express themselves in a whole new way. I think this will be a great thing, but what do you think?

Works Cited

Chu, Eric, et al. “AI in Storytelling: Machines as Cocreators.” McKinsey & Company, Dec. 2017, www.mckinsey.com/industries/media-and-entertainment/our-insights/ai-in-storytelling.

Chu, Eric. “Project Overview ‹ The Story Learning Machine – MIT Media Lab.” MIT Media Lab, www.media.mit.edu/projects/story-learning-machine/overview/.

“IBM's Watson Creates Latest 'Morgan' Trailer.” YouTube, 1 Sept. 2016, youtu.be/5E8FkaUmoe4.

Kramer, Shelly. “Machine Learning Already Changing the Entertainment Industry.” Futurum, 7 Nov. 2016, futurumresearch.com/machine-learning-already-changing-entertainment-industry/.

“Morgan | IBM Creates First Movie Trailer by AI [HD] | 20th Century FOX.” YouTube, 31 Aug. 2016, youtu.be/gJEzuYynaiw.