A BSTRACT We consider the problem of learning high-level controls over the global structure of sequence generation, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer au-toencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global embedding with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and and melody. Empirically, we demonstrate the effectiveness of our method on a variety of music generation tasks on the MAESTRO dataset and a Y ouTube dataset with 10,000 hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to relevant baselines. As the number of generative applications increase, it becomes increasingly important to consider how users can interact with such systems, particularly when the generative model functions as a tool in their creative process (Engel et al., 2017a; Gillick et al., 2019) To this end, we consider how one can learn high-level controls over the global structure of a generated sample. We focus on symbolic music generation, where Music Transformer (Huang et al., 2019b) is the current state-of-the-art in generating high-quality samples that span over a minute in length. The challenge in controllable sequence generation is that Transformers (V aswani et al., 2017) and their variants excel as language models or in sequence-to-sequence tasks such as translation, but it is less clear as to how they can: (1) learn and (2) incorporate global conditioning information at inference time. This contrasts with traditional generative models for images such as the variational autoencoder (V AE) (Kingma & Welling, 2013) or generative adversarial network (GAN) (Goodfel-low et al., 2014) which can incorporate global conditioning (e.g.

--Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting outcomes of certain events such as election debates. In this work, we investigate whether crowd wisdom is useful in predicting such outcomes and whether their opinions are influenced by the experts in the field. We work in the domain of multi-label classification to perform sentiment classification of tweets and obtain the opinion of the crowd. This learnt sentiment is then used to predict outcomes of events such as: US Presidential Debate winners, Grammy A ward winners, Super Bowl Winners. We find that in most of the cases, the wisdom of the crowd does indeed match with that of the experts, and in cases where they don't (particularly in the case of debates), we see that the crowd's opinion is actually influenced by that of the experts. I NTRODUCTION Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics, discuss current issues, complain, etc. Twitter is one such popular microblogging service where users create status messages (called "tweets"). With over 400 million tweets per day on Twitter, microblog users generate large amount of data, which cover very rich topics ranging from politics, sports to celebrity gossip. Because the user generated content on microblogs covers rich topics and expresses sentiment/opinions of the mass, mining and analyzing this information can prove to be very beneficial both to the industrial and the academic community. Tweet classification has attracted considerable attention because it has become very important to analyze peoples' sentiments and opinions over social networks.

With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality. This paper identifies the characteristics of AI applications that distinguish them from traditional software, and analyses the main difficulties in applying existing testing methods. Based on this analysis, we propose a new method called datamorphic testing and illustrate the method with an example of testing face recognition applications. We also report an experiment with four real industrial application systems of face recognition to validate the proposed approach.

Plenty of games offer players multiple solutions to problems. Stick to the beaten path or explore. A game that lets you do anything, though? That's harder to come by. Released yesterday, AI Dungeon 2 purports to be one such game, and for the most part, I've found it to be true.

Built on verified architectures that combine NVIDIA DGX systems for compute and flash storage technology, the Cloud for Data Scientists provides accelerated GPU compute tuned to the demands of deep learning. Core Scientific is a leader in artificial intelligence and blockchain technologies, delivering best-in-class infrastructure and software solutions. In an increasingly distributed and connected world, Core Scientific believes that AI and blockchain are changing the way information is processed, shared and stored across a range of industries. Announced earlier this year, the Core Scientific AiLab is an artificial intelligence and machine learning cloud built from the ground up for the needs of data scientists. "Providing leading edge AI infrastructure and AI applications and tools to Equinix customers is an integral part of our strategy," said Kevin Turner, CEO of Core Scientific.

A group called The Campaign to Stop Killer Robots advocates for global treaties to stop AI from waging war without human approval. AI weapons are "grossly unethical and immoral," according to a celebrity advocate quoted in a newspaper. Unfortunately, so are any tools used to wage wars, as there's nothing ethical or moral about a sword, machine gun, or cruise missile. The decision to use them is about a lot of things, some of which can have legitimacy (like survival, freedom from fear or bondage), but weapons doing what they were designed to do have no deeper meaning than that. If the tools of war are unethical and immoral, by definition, to what higher standard should robots be held when it comes to sanctioning violence?

The mention of "Indian classical music" does not inspire images of cutting-edge technology, artificial intelligence, or smart devices. Ranade is a 38-year-old classical vocalist and software engineer based in Pune. He lent his music and technology skills to create NaadSadhana, an iOS app that uses artificial intelligence (AI) to help users practice classical music. Released on the App Store in July, NaadSadhana provides machine learning-based tuning feedback, tanpura, tabla, and swaramandal simulations to help students improve their pitch, rhythm, and performance. While iOS users can find many apps available providing similar features, NaadSadhana is unique in how it uses machine learning to simulate a live-concert experience.

From news outlets to social media sites, there are numerous places that spread fake news, but a study has uncovered a new source – you. Researchers found that people will misremember numerical statistics on a controversial topic in a way that fits their own commonly held beliefs. For example, when people were shown that the number of Mexican immigrants in the United States declined recently during the study--which is true but goes against many people's beliefs--they tended to remember the opposite. And the team also found that as people pass along this misinformation, the numbers can become further and further from the truth. The study was conducted by a team at Ohio State University, who carried out two studies to investigate how people perceive and spread fake news.

It's very easy to say that your new app or game uses AI. Doesn't mean it's any good. I'm all for people experimenting with AI in these kinds of ways, even if the results are lackluster. But there's a big difference between trying to use AI in a new and experimental way vs using it for hype and marketing. I suppose we'll see when they produce something we can scrutinize, but until then this is just advertising which isn't appropriate in this sub (although there are plenty of video game subs that may be interested in this).

What's New: This week at the annual Neural Information Processing Systems (NeurIPS) conference in Vancouver, British Columbia, Intel is contributing almost three dozen conference, workshop and spotlight papers covering deep equilibrium models, imitation learning, machine programming and more. "Intel continues to push the frontiers in fundamental and applied research as we work to infuse AI everywhere, from low-power devices to data center accelerators. This year at NeurIPS, Intel will present almost three dozen conference and workshop papers. We are fortunate to collaborate with excellent academic communities from around the world on this research, reflecting Intel's commitment to collaboratively advance machine learning." Research topics span the breadth of artificial intelligence (AI) topics, from fundamental understanding of neural networks to applying machine learning to software programming to particle physics.