AI-generated content (AIGC), such as advertisement copy, product descriptions, and social media posts, is becoming ubiquitous in business practices. However, the value of AI-generated metadata, such as titles, remains unclear on user-generated content (UGC) platforms. To address this gap, we conducted a large-scale field experiment on a leading short-video platform in Asia to provide about 1 million users access to AI-generated titles for their uploaded videos. Our findings show that the provision of AI-generated titles significantly boosted content consumption, increasing valid watches by 1.6% and watch duration by 0.9%. When producers adopted these titles, these increases jumped to 7.1% and 4.1%, respectively. This viewership-boost effect was largely attributed to the use of this generative AI (GAI) tool increasing the likelihood of videos having a title by 41.4%. The effect was more pronounced for groups more affected by metadata sparsity. Mechanism analysis revealed that AI-generated metadata improved user-video matching accuracy in the platform's recommender system. Interestingly, for a video for which the producer would have posted a title anyway, adopting the AI-generated title decreased its viewership on average, implying that AI-generated titles may be of lower quality than human-generated ones. However, when producers chose to co-create with GAI and significantly revised the AI-generated titles, the videos outperformed their counterparts with either fully AI-generated or human-generated titles, showcasing the benefits of human-AI co-creation. This study highlights the value of AI-generated metadata and human-AI metadata co-creation in enhancing user-content matching and content consumption for UGC platforms.

Artificial intelligence (AI) has been invading our daily lives without us fully realizing it. When you wake up in the morning, you may ask Alexa to give you a run-down of your daily schedule. When you drive to work using Waze, the app is using a machine-learning algorithm to provide the best route for you. When you watch a movie or a show on Netflix or make a purchase on Amazon, the platforms use AI to make content or product recommendations for you. If Netflix can use AI to make content recommendations for us, businesses and enterprises can also use AI to make personalized content recommendations when prospects visit their websites.

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods.

But the applications of AI in media are not just limited to content personalization. Media teams have to deal with manual processes for everything - from tagging the media to creating multilingual subtitles. But recent advances in AI are automating many of these tasks. Developments in computer vision, speech to text and natural language processing algorithms are changing the face of media creation, distribution and most importantly, media consumption. Voice is the most natural way for people to communicate.

The experts say Apple's self-driving car project is canceled, delayed or converted into a software play. They'll also tell you that cars are a weird business for Apple to be in. Apple is going pedal-to-the-metal on building a car and for good reason. The late Apple founder and CEO wanted more than that, according to J. Crew CEO and chairman Mickey Drexler, who served on the Apple board from 1999 to 2015. Jobs wanted Apple to reinvent the automobile industry.

And they're really fun to write about, too. Contently has mastered the buzzword listicle year after year with witty insight on buzzspeak and content marketing lingo that constantly creeps into our conversations. While we've all grown accustomed to yesterday's buzzwords like "thought leadership," "storytelling" and "snackable content," there's a new slew of them that have entered our everyday content marketing language. Here are six more content marketing buzzwords you actually need to pay attention to in 2017 and why. This is the most basic form of personalization and has become a standard practice for email marketing.