The research lab OpenAI has released the full version of a text-generating AI system that experts warned could be used for malicious purposes. The institute originally announced the system, GPT-2, in February this year, but withheld the full version of the program out of fear it would be used to spread fake news, spam, and disinformation. Since then it's released smaller, less complex versions of GPT-@ and studied their reception. Others also replicated the work. In a blog post this week, OpenAI now says it's seen "no strong evidence of misuse" and has released the model in full. GPT-2 is part of a new breed of text-generation systems that have impressed experts with their ability to generate coherent text from minimal prompts.

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Today, we're at an inflection point in how we engage with customers to better meet their needs. According to a PwC report, 73% of all people point to customer experience as an important factor in their purchasing decisions. Yet only half of the U.S. consumers say companies are meeting their expectations. In this experience-driven economy, companies who understand the meaning behind customers' needs, listen and learn in every interaction, and consistently create engaging and personal experiences, will win. Companies should adopt AI to get the desired success.

A few years ago this month, Portland, Oregon artist Darius Kazemi watched a flood of tweets from would-be novelists. November is National Novel Writing Month, a time when people hunker down to churn out 50,000 words in a span of weeks. To Kazemi, a computational artist whose preferred medium is the Twitter bot, the idea sounded mildly tortuous. "I was thinking I would never do that," he says. "But if a computer could do it for me, I'd give it a shot."

A storyboard is a sequence of images to illustrate a story containing multiple sentences, which has been a key process to create different story products. In this paper, we tackle a new multimedia task of automatic storyboard creation to facilitate this process and inspire human artists. Inspired by the fact that our understanding of languages is based on our past experience, we propose a novel inspire-and-create framework with a story-to-image retriever that selects relevant cinematic images for inspiration and a storyboard creator that further refines and renders images to improve the relevancy and visual consistency. The proposed retriever dynamically employs contextual information in the story with hierarchical attentions and applies dense visual-semantic matching to accurately retrieve and ground images. The creator then employs three rendering steps to increase the flexibility of retrieved images, which include erasing irrelevant regions, unifying styles of images and substituting consistent characters. We carry out extensive experiments on both in-domain and out-of-domain visual story datasets. The proposed model achieves better quantitative performance than the state-of-the-art baselines for storyboard creation. Qualitative visualizations and user studies further verify that our approach can create high-quality storyboards even for stories in the wild.