Detecting customized moments and highlights from videos given natural language (NL) user queries is an important but under-studied topic. One of the challenges in pursuing this direction is the lack of annotated data. To address this issue, we present the Query-based Video Highlights (QVHighlights) dataset. It consists of over 10,000 YouTube videos, covering a wide range of topics, from everyday activities and travel in lifestyle vlog videos to social and political activities in news videos. Each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w.r.t. the query, and (3) five-point scale saliency scores for all query-relevant clips. This comprehensive annotation enables us to develop and evaluate systems that detect relevant moments as well as salient highlights for diverse, flexible user queries. We also present a strong baseline for this task, Moment-DETR, a transformer encoder-decoder model that views moment retrieval as a direct set prediction problem, taking extracted video and query representations as inputs and predicting moment coordinates and saliency scores end-to-end. While our model does not utilize any human prior, we show that it performs competitively when compared to well-engineered architectures. With weakly supervised pretraining using ASR captions, Moment-DETR substantially outperforms previous methods.

Google Assistant wants to tell you some good news. A new skill aims to give users a reprieve from the oft-depressing daily news cycle by making it easier for them to find more uplifting headlines. Now, users can ask Google Assistant to'Tell me something good,' and it will trigger a'daily dose of good news,' according to the search giant. Google says the skill is launching as an'experimental feature' that's now available on any devices that are equipped with Assistant, such as phones, smart displays and the Google Home, the firm's voice-activated smart speaker. Assistant will serve up stories that are primarily focused around people who are doing things to help their communities and the world, Google explained.

In the spirit of Christmas, we're going to count down to the new year with the 12 Days of AI, bringing you a new, festive AI post every day! What better way to kick off 2018 than to look back at the RE•WORK highlights of 2017 and celebrate some of our successes of the past 12 months. This year saw RE•WORK hosting more events and bringing our globally renowned Summits to new locations. Our first ever Canadian Summit this year took place in Montreal, the'Silicon Valley of AI', and was one of our biggest events to date with over 600 attendees over the two days. We were fortunate enough to be joined by the'Godfathers of AI', Yoshua Bengio, Yann LeCun and Geoffrey Hinton who appeared on a panel together for the first time ever.

If you missed that thread and there's something you want to mention, post it and I'll put it up. Lots of categories didn't have an entry. You can also make a category yourself. "and we all realized what a pain in the ass Tensorflow was and how it didn't need to be that way. In the academic community, it certainly to me feels like pytorch has become the dominant framework (probably not backed up by actual stats...

The Golden Goose Project, a new data visualization effort from Duke University's Fuqua School of Business, attempts to highlight this paradigm shift with patent and research output statistics as well as data quantifying how research is applied, both inside companies and in the broader ecosystem. Huge research group acquisitions, like Google's $500 million purchase of Deep Mind, highlight an industry-wide desire to conduct more AI research internally. But while this research has helped companies like Facebook, Microsoft and Google make huge strides in AI, it isn't a universal formula for corporate innovation. One major shortcoming of The Golden Goose Project is that it only takes into account data collected between 1980 until 2006.

This summer's AI conference in Acapulco offered attendees wide variety of program choices as well as ample time to catch up with friends and colleagues. For many, scheduling time was probably the biggest challenge because the conference included numerous invited speakers, 189 technical paper presentations, 93 posters, a Mobile Robot Competition, 19 Innovative Applications of AI (IAAI) award-winning paper presentations, a Trading Agents Competition, a special track on AI and the web, and the vendor exhibit.

Advances in Artificial Intelligence Research and Applications at IJCAI-03

There is a great dispute among researchers about the roles, characteristics, and specifications of what are called agents, intelligent agents, and adaptive agents. Most research in the field focuses on methodologies for solving specific problems (for example, communications, cooperation, architectures), and little work has been accomplished to highlight and distinguish the field of intelligent agents. As a result, more and more research is cataloged as research on intelligent agents. The Workshop on Intelligent Adaptive Agents, presented as part of the Thirteenth National Conference on Artificial Intelligence, addressed these issues as well as many others that are presented in this article.