We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.

Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has suggested that a hyperbolic Poincar\'e ball may be more well suited to representing multiple entity types, and in particular, hierarchies. We describe a novel method to embed a hierarchy of related music entities in hyperbolic space. We also describe how a parametric empirical Bayes approach can be used to estimate link reliability between entities in the hierarchy. Applying these methods together to build personalized playlists for users in a digital music service yielded a large and statistically significant increase in performance during an A/B test, as compared to the Euclidean model.

My grandfather, a lover of classical music, was president of the Hull Philharmonic Orchestra for many years. When I was 15, I played him an orchestrated version of Nobuo Uematsu's To Zanarkand, from the video game Final Fantasy X. "This isn't real music if it's from a video game," he told me at the time. I don't think he could ever have imagined that 12 years later, the Hull orchestra to which he had devoted so many years would be performing music from 1980s video games, in front of a packed hall. In the past, video game music concerts were a promotional novelty, but today they are regular and well-attended billings in venues across the world. From The Legend of Zelda: Symphony of the Goddess to Final Fantasy: Distant Worlds, Assassin's Creed Symphony to the recent debut by the London Video Game Orchestra and even a performance by the BBC Concert Orchestra hosted by lauded composer Jessica Curry, fans are flocking to concert halls to hear their favourite video game melodies played live.

With the internet buzzing about the viral FaceApp, which uses AI to predict how anyone will look in 30 years, there's another service that will transform you into a work of art. If you've ever dreamed of how your portrait would look if it were painted by one of the great masters, this app is for you. AI Portraits uses information from over 45,000 15th-century masterpieces to help "paint" the portrait of any photo that's uploaded. While there are plenty of apps and filters that promise to make your photo into a work of art, AI Portraits distinguishes itself with its GAN models. Many services use style transfers that alter colors, but leave the facial lines untouched.

More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long short-term memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63% on the NIST SRE 2000 CALL-HOME database.

We regularly get questions here from enterprising beginners who wonder how they can get started with AI. We have a section for that on /r/artificial's wiki, but as /u/anal_bifurcation pointed out, it's quite old. I think I fixed the few links that were actually broken (assuming springer.com Unfortunately, I haven't consumed much "getting started materials" myself in the intervening time, so I don't know what's new and what's good. If you have any general questions that the wiki does not address, please ask them here.

Artificial Intelligence (AI) is not considered just an emerging technology with a bright future, it is indeed a robust growing platform, impacting several industries and touching numerous spheres of life. AI algorithms need enormous volumes of datasets to be trained appropriately, after which the system can not only decipher pictures, such as recognizing a dog is a dog or differentiating a chair from a table, it can also generate original images and create exceptionally amazing artistry of quality associated with those of Picasso or Michelangelo. AI model that makes it possible has matured substantially over the recent years and it produces perfect output for certain applications but needs more refinement in other cases. Computer scientists have spent around two decades to teach, train and build machines which can visualize the world around them, a normal skill that humans take for granted, yet it's one that's highly challenging to train a machine to do, kudos to artificial intelligence for making it possible!! Two major ground-breaking improvements in AI image processing have been facial-recognition technology in both retail and security, as well as image generation in all fields of art. The commercialized usage of facial recognition technology is to improve sales and marketing of products including efficient targeting of audience.

Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10%. Our code is available at https://github.com/thunlp/GEAR.

Way out, beyond the heliosphere, an A.I. aboard NASA space probe Voyager 3 generates free jazz - broadcast via livestream 24/7 - until we lose contact. Its artificial neural network was trained on John Coltrane's INTERSTELLAR SPACE with modified SampleRNN. It listened to the album 16 times then continued to make music in the style. Voyager 1 and 2 launched in 1977 carrying a mixtape Carl Sagan made called THE SOUNDS OF EARTH. It featured Blind Willie Johnson, Chuck Berry, recordings of laughter, Beethoven, Bach, Stravinsky, along with diagrams of human reproductive organs. It was intended for an audience of intelligent extraterrestrial lifeforms.