"Many a true word hath been spoken in jest." "O, beware, my lord, of jealousy; It is the green-ey'd monster, which doth mock The meat it feeds on." "There was a star danced, and under that was I born." Who can write like Shakespeare? Or even just spell like Shakespeare?

Netflix has just announced they are using real people to help their users find new movies to watch. They are trialling a new feature called Collections. In their words, it adds a'human touch' to film recommendations when used on their iOS mobile platform. It's a bold move considering that many other publishers prefer to only rely on automated or AI algorithm-based recommendations. But Netflix isn't about to do away with machines altogether.

Deep learning (DL) is becoming increasingly popular in sev- eral application domains and has made several new applica- tion features involving computer vision, speech recognition and synthesis, self-driving automobiles, drug design, etc. fea- sible and accurate. As a result, large scale "on-premise" and "cloud-hosted" deep learning platforms have become essential infrastructure in many organizations. These systems accept, schedule, manage and execute DL training jobs at scale. This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM. We describe how our design balances dependability with scalability, elasticity, flexibility and efficiency.

In this paper, we present a new modality for intelligent inte r-active narratives within the theatre domain. We discuss the possibilities of using an intelligent agent that serves as a drama manager a nd as an actor that plays a character within the live theatre exper ience. We pose a set of research challenges that arise from our analysi s towards the implementation of such an agent, as well as potential method ologies as a starting point to bridge the gaps between current literatu re and the proposed modality.

This past May I worked with the Internet Archive's Television News Archive to apply Google's suite of cloud AI APIs to analyze a week of television news coverage to examine how AI "sees" television and what insights we might gain into the world of non-consumptive deep learning-powered video understanding. Using Google's video, image, speech and natural language APIs as lenses, more than 600GB of machine annotations trace how deep learning algorithms today understand video. What lessons can we learn about the state of AI today and how it can be applied in creative ways to catalog and explore the vast world of video? Working with the Internet Archive's Television News Archive, a week of television news was selected covering CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news. This week was selected due to it having two major stories, one national (the Mueller report release on April 18th) and one international (the Notre Dame fire on April 15th).

Rhythmic flashes from a computer screen illuminate a dark room as sounds fill the air. The snare drum sample comes out crisp and clean by itself, but turns muddy in the mix, no matter how the levels are set. Welcome to the world of modern music-making -- and its discontents. Today's digital music producers face a common dilemma: how to mesh samples that may sound great on their own but do not necessarily fit into a song like they originally imagined. One solution is to find and audit dozens of different samples, a tedious process that can take time to finesse.

The global artificial intelligence market was USD 6.2 billion in 2017, and is expected to reach USD 153.4 billion by 2025, growing at a CAGR of 49.6% …

When artificial intelligence and machine learning are added to systems it opens the door for better opportunities for businesses across every industry.