Have you ever felt guilty about leaving your dog home alone, wishing there was a way to calm it? Amazon's Audible may have the answer - a good book. Dog behavior expert Cesar Millan has teamed up with the firm to create audiobooks for dogs, which use human voices to tell stories to dogs when nobody is at home, keeping then company. Dog behavior expert Cesar Millan (pictured) teamed up with audible to conduct their own study to see what impact audiobooks have on dogs when their owners are away. By using a speaker along with one of the audiobooks, users can play one of a range of audible's audiobooks, with recommended titles including: According to a demonstration video by Millan, having to be away from our dogs can be a stressful situation, joined by guilt and shame for having to be away from our pets for long periods of time, for example when going to work.

Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.

Spotify recently hit 60 million paid subscribers, up from 50 million subscribers in March. Until today, however, such subscribers, and for that matter listeners on freebie accounts, could not access the popular streaming music service on Microsoft's Xbox One video game console. That changes with Tuesday's official announcement that Spotify is coming to the Xbox One in 34 global markets. Spotify is a latecomer to Microsoft's console, considering that the service has been available on Sony's rival PlayStation consoles since March of 2015. There won't be much difference, the company says, in the Spotify experience on the PS3 and PS4 compared to the experience on Xbox One.

Even though I did not follow his older courses, they seem really appreciated, at least on this subreddit. I hope these new ones will set an even higher standard. That way, newcomers may share an identical set of notations, principles and methodologies so we can all focus on other tasks, such as visualization. You will practice all these ideas in Python and in TensorFlow. What do you guys think of this choice?

Artist and machine learning engineer Terence Broad's Auto-Encoding Blade Runner is the project Philip K. Dick would have made if he were a scientist. In his presentation at SIGGRAPH 2017, a computer graphics and animation conference, Broad detailed how he trained a Convolutional Autoencoder--a type of neural network--to recognize patterns of data in Blade Runner and then reconstruct it, scene by scene. What results is an eerily-accurate full-length film that was so convincing that Warner Bros. issued a DMCA takedown notice to Vimeo when Broad first uploaded the footage in 2016. The real question behind Broad's Blade Runner parallels the themes of Philip K Dick's legendary novel Do Androids Dream of Electric Sheep: Where does one draw the line between human and machine, the real and the seemingly real? "I think the thing to understand about neural networks is that we don't really know how they work," Ruth West, SIGGRAPH's chair of art papers, told me in an interview. "They're black boxes, and they make these leaps that are kind of like the leaps we make internally.

Imagine that tomorrow, some smart kid invented a technology that let people or physical goods pass through walls, and posted instructions for how to build it cheaply from common household materials. Lots of industries would probably become more productive. Being able to walk through walls instead of being forced to use doors would make it easier to navigate offices, move goods in and out of warehouses and accomplish any number of mundane tasks. That would give the economy a boost. But the negative might well outweigh the positive.

This is thanks to computational photography that will make each snap shot look like it was taken using a professional camera. There is no denying that one of the most sought after features of any smartphone today is its camera. Ever since image sharing sites and social media platforms rose to popularity, sharing pictures of just about anything has taken over the lives of many people around the world. Smartphone manufacturers started developing camera phones that can capture high-quality images to satisfy the needs of photo-savvy individuals. If that is not enough, some of these mobile phone giants partnered with famous camera makers to create the best camera phones of today.

Researchers have devised a new way to monitor sleep stages without sensors attached to the body. Their device uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM).

As training data, drawn from planes_features.csv,

In cases where an L1-norm or L-infinity norm better describe distance, this could be useful. For example, dealing with a square-grid pattern in city streets may yield better results when using scaled geographic coordinates. K-means is effectively an algorithm that considers all points around each cluster center to be distributed around that point according to an N-dimensional normal distribution with a constant diagonal and no correlations. This works well when your clusters can be approximated to be roughly a circular shape (which corresponds to the L2 norm of Euclidean space). If your cluster patterns were squares, cubes or hypercubes, this would work better for an L-infinity norm, and likewise diamond shapes would work better with an L1-norm.