Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while necessitating maintaining audiovisual alignments, managing scene changes and preserving factual accuracy for the output teasers. Due to the lack of a publicly-available dataset, progress along this research direction has been hindered. In this work, we present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers, featuring multimodal data streams of video, speech, music, sound effects and narrations. With DocumentaryNet, we propose a new two-stage system for generating teasers from long documentaries. The proposed TeaserGen system first generates the teaser narration from the transcribed narration of the documentary using a pretrained large language model, and then selects the most relevant visual content to accompany the generated narration through language-vision models. For narration-video matching, we explore two approaches: a pretraining-based model using pretrained contrastive language-vision models and a deep sequential model that learns the mapping between the narrations and visuals. Our experimental results show that the pretraining-based approach is more effective at identifying relevant visual content than directly trained deep autoregressive models.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

Neuroscience and artificial intelligence (AI) are two very different scientific disciplines. Neuroscience traces back to ancient civilizations, and AI is a decidedly modern phenomenon. At a cursory glance, it would seem that a branch of science of living systems would have little in common with one that springs from inanimate machines wholly created by humans. Yet discoveries in one field may result in breakthroughs in the other-- the two fields share a significant problem, and future opportunities. The origins of modern neuroscience is rooted in ancient human civilizations. One of the first descriptions of the brain's structure and neurosurgery can be traced back to 3000 - 2500 B.C. largely due to the efforts of the American Egyptologist Edwin Smith.

A hypnotic new video reveals the unearthly beauty of life up-close. Using different colored ink in water, the team at the Macro Room has created a breathtaking short film that could rival the effects of CGI. A hypnotic new video reveals the unearthly beauty of life up-close. Ink In Motion, shared on YouTube by the Macro Room, gives a close-up look at'the hypnotising beauty of colored ink in water and the interaction of this substance with different elements.' It begins with just a tank filled with water, and 3D planet models submerged in the center.

Researchers have warned that millions of human workers in the US will be replaced by robots over the next few decades, leaving Americans to wonder what areas are at the highest risk. Now, a new map has shown where the most'automatable' jobs are in the nation - and almost every metropolitan area is set to experience a robot takeover. However, it is the low-wage cities like Las Vegas, Nevada, El Paso, Texas and San Bernardino, California that will be hit the hardest – robots are predicted to take more than 60% of jobs in these cities by 2035. A new map has shown where the most'automatable' jobs are in the nation - and almost every metropolitan area is set to experience a robot takeover. The bubble size shows the number of workers employed in the metropolitan areas in December 2016.

A senior Egyptian al-Qaida figure fighting in Syria was killed in a U.S. drone strike this week, the latest to be killed in such attacks in Syria, a Syrian opposition monitoring group and relatives said Friday. The Britain-based Syrian Observatory for Human Rights said Rifai Ahmad Taha was killed in a strike Tuesday in the northwestern Idlib province. Before joining al-Qaida, Taha was a top figure in Egypt's notorious militant group Gamaa Islamiya, which massacred 58 foreign tourists in the ancient Egyptian city of Luxor in 1997. He was also allied with Osama bin Laden in Afghanistan. The Observatory's chief Rami Abdurrahman said several al-Qaida members, including Taha, were killed in Tuesday's strike.