This conversation with the composer Philip Glass and me discusses an exciting project in partnership with OpenAi, in which we trained a neural net on a corpus of Glass' work. He offers commentary on the music created by "his AI", as well as insights on composition and creating art. We then talk about the different limitations and capacities of humans and Artificial Intelligence–if and how neural nets can help us create art, appreciate art, and find the same things humans find meaningful. Due to the covid-19 pandemic, this call took place over video conference in December 2020. Art and tech are both captivating to me because they frame the elevation and the limitations of being human. Art is also closely intertwined with technological advancements, as movement shifting art seems predicated on tech. For example, the photography of Martin Munkacsi from the 1920s and 1930s revolutionized the art, as he is often credited for being the first photographer to explore dynamic and candid styles. The emergence and ability of these new forms of creation coincided with the technological advancements at the time that enabled flash and faster shutters–candid and spontaneous movement shots wouldn't have been technically possible to make with the cameras that existed before. The advancements in machine learning today, likewise, excite me for the possibilities and new forms in art and creation. The goal of this project is to explore the capacities of artificial intelligence as a new medium (or instrument or tool?) for art, and to create a collaborative music composition with Philip Glass and "his AI." More details about the project can be found below. Philip: Nice to see you.

If you know Siri, Cortana from Microsoft, Denise from Nextos, Alexa from Amazon or those handy voice GPS directions on smartphones, then congrats! This course will just help you bridge the gap through an ocean of knowledge with the power of Artificial intelligence based TTS tools. Text to speech, abbreviated as TTS, is a synthesis of speech that transforms text into voice output. Text to speech systems was first developed to help the visually impaired by providing the user with a spoken voice created by a machine that would "read" text. Text to speech enables content owners to adapt in terms of how they communicate with the content to the specific needs and desires of each user.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. You get married or move in together, and your lives are tied in countless ways: a mortgage, the power bill, and your relationship status on social media sites. Then it ends, and you're left with a lot of heartache and a lot of work. It's bad enough thinking about everything strangers know about you.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. "Jeopardy!" is once again looking for a host. A nearly-exhaustive search for late host Alex Trebek's replacement began not long after his passing with a slew of guest hosts taking a swing at the gig. After months of consideration, executive producer Mike Richards was offered the reigns with actress Mayim Bialik taking over the show's spin-off events.

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.

Understanding and comprehending video content is crucial for many real-world applications such as search and recommendation systems. While recent progress of deep learning has boosted performance on various tasks using visual cues, deep cognition to reason intentions, motivation, or causality remains challenging. Existing datasets that aim to examine video reasoning capability focus on visual signals such as actions, objects, relations, or could be answered utilizing text bias. Observing this, we propose a novel task, along with a new dataset: Trope Understanding in Movies and Animations (TrUMAn), with 2423 videos associated with 132 tropes, intending to evaluate and develop learning systems beyond visual signals. Tropes are frequently used storytelling devices for creative works. By coping with the trope understanding task and enabling the deep cognition skills of machines, data mining applications and algorithms could be taken to the next level. To tackle the challenging TrUMAn dataset, we present a Trope Understanding and Storytelling (TrUSt) with a new Conceptual Storyteller module, which guides the video encoder by performing video storytelling on a latent space. Experimental results demonstrate that state-of-the-art learning systems on existing tasks reach only 12.01% of accuracy with raw input signals. Also, even in the oracle case with human-annotated descriptions, BERT contextual embedding achieves at most 28% of accuracy. Our proposed TrUSt boosts the model performance and reaches 13.94% performance. We also provide detailed analysis to pave the way for future research. TrUMAn is publicly available at:https://www.cmlab.csie.ntu.edu.tw/project/trope

Free Guy, the new movie about a non-player character who discovers he's trapped inside a video game, is built around a series of fight scenes that are, for want of a better word, extremely video-gamey: Characters move stiffly, some have signature moves they repeat, and the laws of physics seem to have been imported from somewhere between The Matrix and Warner Bros. cartoons. We spoke to the film's fight coordinator, Freddy Bouciegues, to find out he choreographed human actors to fight like video game characters--and how that's different from his work on fights in actual video games. This conversation has been condensed and edited for clarity. I assume the two or three major fight sequences in the movie make up the bulk of your efforts, but were you also working on all random stunts going on in the background? Whenever Ryan Reynolds walks down the street, we get a taste of Grand Theft Auto-style mayhem caused by other players of the game-within-the-movie.

From automating the creation of personalized videos to enabling new virtual customer experiences, these companies are deploying AI to help brands and retailers create engaging digital content. Brands and retailers are relying more and more on digital content -- which can range from product images for e-commerce sites to virtual try-on features to online videos -- to increase brand awareness, convert online shoppers, and boost loyalty. Video content is gaining particular momentum, with the number of times execs have mentioned the term during earnings calls shooting up in Q2'21. But given these ever-growing digital content needs, a trend accelerated by the Covid-19 pandemic, conventional production approaches may not be sufficient for brands and retailers to deliver personalized and engaging content at scale. Enter synthetic media -- images, videos, sounds, or any other form of content that has been generated, edited, or enabled by artificial intelligence.

LyricJam, a real-time system that uses artificial intelligence (AI) to generate lyric lines for live instrumental music, was created by members of the University's Natural Language Processing Lab. The lab, led by Olga Vechtomova, a Waterloo Engineering professor cross-appointed in Computer Science, has been researching creative applications of AI for several years. The lab's initial work led to the creation of a system that learns musical expressions of artists and generates lyrics in their style. Recently, Vechtomova, along with Waterloo graduate students Gaurav Sahu and Dhruv Kumar, developed technology that relies on various aspects of music such as chord progressions, tempo, and instrumentation to synthesize lyrics reflecting the mood and emotions expressed by live music. As a musician or a band plays instrumental music, the system continuously receives the raw audio clips, which the neural network processes to generate new lyric lines.

Have you seen talented traders in the movies do their calculations in their minds or actively record their recent trades while tracking the real market movements? Such "traditions" are gradually becoming obsolete and disappearing in trading. Now those who strive to be on the wave of progress and make even higher profit ask mathematicians, programmers and analysts for help. Recent research in stock trading shows that traders who use outdated automation techniques are watching a drop in earnings. At the same time, those who use artificial intelligence (AI) for trading and analytics, like the trading company M8Trade, are getting results significantly higher than the market average.