We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the developed system captures the narrative on word-level, sentence-level, and context-level. Through the hierarchical set-up, our proposed model summarizes the most salient information on each level and creates an abstract representation of the extended context. We subsequently use this representation to enhance neural language processing systems on the task of semantic error detection. To show the potential of the newly introduced topology, we compare the approach against a context-agnostic set-up including a standard neural language model and a supervised binary classification network. The performance measures on the error detection task show the advantage of the hierarchical context-aware topologies, improving the baseline by 12.75% relative for unsupervised models and 20.37% relative for supervised models.

If you haven't seen HBO's "Westworld" then… eh, actually honestly, it's a toss up, it's up to you. Personally I think they over promised and under delivered, BUT it's worth talking about because they went over some amazing concepts. For those who haven't seen the show at all, Westworld is essentially a theme park for the ultra rich in which they live out their deepest fantasies in a wild west world populated with "people" who they can … "interact" with in which ever manner they wish. The "people" are actually human replicas made in a lab with a complex AI brain and a persona written for their character in the park, like a bartender or sherif. These replicas appear to be so human like, it is difficult or impossible to tell who is real from the AI.

Object detection is a domain that has benefited immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet. Object detection is a domain that has benefited immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet. For the past few months, I've been working on improving object detection at a research lab.

That debate comes down to whether or not the current approaches to building AI are enough. With a few tweaks and the application of enough brute computational force, will the technology we have now be capable of true "intelligence," in the sense we imagine it exists in an animal or a human? On one side of this debate are the proponents of "deep learning"--an approach that, since a landmark paper in 2012 by a trio of researchers at the University of Toronto, has exploded in popularity. While far from the only approach to artificial intelligence, it has demonstrated abilities beyond what previous AI tech could accomplish. The "deep" in "deep learning" refers to the number of layers of artificial neurons in a network of them.

The business world often uses the terms machine learning, deep learning, and artificial intelligence as interchangeable buzzwords. Each is uniquely different from its siblings.

Artificial Intelligence (AI) is much in the news these days. AI is making medical diagnoses, synthesizing new chemicals, identifying the faces of criminals in a huge crowd, driving cars, and even creating new works of art. Sometimes it seems as if there is nothing that AI cannot do and that we will all soon be out of our jobs, watching the AIs do everything for us. To understand the origins of the AI technology, this blog chronicles how we got here. It also examines the state of AI chips and what they need to make a real impact on our daily lives by enabling advanced driver assistance systems (ADAS) and autonomous cars.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. The Wall Street Journal recently ran a piece that detailed how companies that provide email-based services scan the inboxes of millions of Gmail users. Their service agreements make it clear that they require access to your email so that their artificial intelligence algorithms can provide you with smart features such as price comparisons, automated calendar scheduling and more. What they don't tell you is that in some cases, their employees read your emails too, because their AI just can't perform as promised and it needs humans to fill the gap where it falls short. One of the companies presented in the Wall Street Journal article uses AI to add a "smart reply" feature to your email, which can make a big difference if you're managing your account from a mobile device.

In the video game, two teams of five players are placed at opposite ends of a square arena, and fight past each other using melee and spells to destroy their opponent's base. For the researcher's software to win against the pros, it would be like a robot learning to dunk on Michael Jordan. Games are an easy way for those of us without PhDs to understand how far AI research has come: When put in complex situations, can an AI beat humans? We understand what it meant for IBM's DeepBlue to beat Garry Kasparov in chess, and DeepMind's AlphaGo beating Lee Sedol in Go--decades of human practice and skill were defeated by mechanical computation. Outside of those publicized matches, AI researchers have worked for decades to build AI agents that are superhuman at playing Atari games, checkers, and even Super Smash Bros.

Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heuristics-based, lacking a principled foundation and do not perform satisfactorily in practice. In this paper, we propose a probabilistic framework based on deep generative models for MVI. Under this framework, imputing the missing entries amounts to seeking a fixed-point solution between two conditional distributions defined on the missing entries and latent variables respectively. These distributions are parameterized by deep neural networks (DNNs) which possess high approximation power and can capture the nonlinear relationships between missing entries and the observed values. The learning of weight parameters of DNNs is performed by maximizing an approximation of the log-likelihood of observed values. We conducted extensive evaluation on 13 datasets and compared with 11 baselines methods, where our methods largely outperforms the baselines.