The new video-generation systems could speed the work of moviemakers and other digital artists, while becoming a new and quick way to create hard-to-detect online misinformation, making it even harder to tell what's real on the internet. The systems are examples of what is known as generative A.I., which can instantly create text, images and sounds. Another example is ChatGPT, the online chatbot made by a San Francisco start-up, OpenAI, that stunned the tech industry with its abilities late last year. Google and Meta, Facebook's parent company, unveiled the first video-generation systems last year, but did not share them with the public because they were worried that the systems could eventually be used to spread disinformation with newfound speed and efficiency. But Runway's chief executive, Cristóbal Valenzuela, said he believed the technology was too important to keep in a research lab, despite its risks.

Ian Sansavera, a software architect at a New York startup called Runway AI, typed a short description of what he wanted to see in a video. "A tranquil river in the forest," he wrote. Less than two minutes later, an experimental internet service generated a short video of a tranquil river in a forest. The river's running water glistened in the sun as it cut between trees and ferns, turned a corner and splashed gently over rocks. Runway, which plans to open its service to a small group of testers this week, is one of several companies building artificial intelligence technology that will soon let people generate videos simply by typing several words into a box on a computer screen.

We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.