If at first you don't succeed, slash your price and try, try again. That's the thinking now being applied by Caavo, the company which launched a new-age universal TV device back in February. I reviewed it and liked many things about it, but I also complained about its $400 price, difficult setup, and many quirks, including a lack of HDR support. Someone over at Caavo HQ was listening. Less than eight months later, that bulky $400 Caavo has vanished.

Google brings video to the talking speaker category with the new Google Home Hub. USA TODAY's Jefferson Graham explains why the device has potential. Google officially released the Home Hub, a $149 digital photo frame, YouTube video player, and visual recipe reciter (among other things) that looks to take on the second edition of the $229 Amazon Echo Show, which has been in release but is sold out through November 5th. The Show looks to bring the popular Alexa personal assistant into the video age, with live TV from Hulu, video-on-command from Amazon Prime Video, visual recipes, digital photos and Alexa smarts. The two are expected to be among the most heavily marketed new holiday products.

Google Home Hub combines a Google Home digital assistant with a 7-inch screen, allowing a graphic display of your schedule, weather, and commute traffic all at the command of your voice. There's more in there too: It has a good speaker for playing music, will act as a digital photo frame when not otherwise in use, and you can use it to watch your favorite shows if you have a compatible pay TV service, or monitor the feed from your home security cameras. In many ways it mirrors what a modern Android phone can already do, but with a better speaker and a bigger, always-on screen. There's a lot to like about this impressive little gadget, and its thoughtfully designed software is a big improvement on a smartphone. The Home Hub will almost certainly surprise you with its small size.

The Home Hub is Google's first own-brand smart display, combining Google Assistant, advanced smart-home control and a digital photo frame into a neat and tidy package. Google isn't the first to market with smart displays. Amazon's Echo Show put the company's Alexa on a screen a year ago, while Google Assistant smart displays made by Harman, Lenovo and LG were released a few months ago. But Google's Home Hub is slightly different. Firstly, it doesn't have a camera on it, which Google goes to great lengths to repeatedly tell you in the hope you will find it less creepy and feel more comfortable putting it in places like your bedroom.

Imagine if we could call out to our living room TV to access a show or movie, get step-by-step cooking directions, watch YouTube clips on demand, have it double as a digital photo frame, look up anything in the world, make phone calls and run your home automation system. How cool would that be? That's the promise of Google's new Home Hub, a $149 talking speaker that is taking on Amazon's $229 Echo Show in what's expected to be one of the biggest marketing battles of the holidays. How small is the Google Home Hub? So tiny that the Amazon Echo dwarfs it, and Mr. Jinx the cat towers over it.

I have heard that both feed-forward networks as well as recurrent neural networks are universal function approximators, i.e., they can approximate any function arbitrarily close with increasing hidden layers and hidden units. Is there a difference between smooth and non-smooth functions? What is better, a few hidden layers with many hidden units per layer or deep learning, i.e., many hidden layers? Has anyone tried to use LSTM networks to do this kind of stuff?

Articles that might be of interest to influencers. Inc.com--Google's spent the last 10 years studying the habits of effective managers. You can learn a lot from their conclusions. October 21, 2018 Go to Inc.com Small Biz Daily--To people outside the digital world, the term "hack" implies either a security breach or sleight of hand. You can't do as much as you think you can.

This analysis explores the temporal sequencing of objects in a movie trailer. Temporal sequencing of objects in a movie trailer (e.g., a long shot of an object vs intermittent short shots) can convey information about the type of movie, plot of the movie, role of the main characters, and the filmmakers cinematographic choices. When combined with historical customer data, sequencing analysis can be used to improve predictions of customer behavior. E.g., a customer buys tickets to a new movie and maybe the customer has seen movies in the past that contained similar sequences. To explore object sequencing in movie trailers, we propose a video convolutional network to capture actions and scenes that are predictive of customers' preferences. The model learns the specific nature of sequences for different types of objects (e.g., cars vs faces), and the role of sequences in predicting customer future behavior. We show how such a temporal-aware model outperforms simple feature pooling methods proposed in our previous works and, importantly, demonstrate the additional model explain-ability allowed by such a model.

Topic sparsity refers to the observation that individual documents usually focus on several salient topics instead of covering a wide variety of topics, and a real topic adopts a narrow range of terms instead of a wide coverage of the vocabulary. Understanding this topic sparsity is especially important for analyzing user-generated web content and social media, which are featured in the form of extremely short posts and discussions. As topic sparsity of individual documents in online social media increases, so does the difficulty of analyzing the online text sources using traditional methods. In this paper, we propose two novel neural models by providing sparse posterior distributions over topics based on the Gaussian sparsemax construction, enabling efficient training by stochastic backpropagation. We construct an inference network conditioned on the input data and infer the variational distribution with the relaxed Wasserstein (RW) divergence. Unlike existing works based on Gaussian softmax construction and Kullback-Leibler (KL) divergence, our approaches can identify latent topic sparsity with training stability, predictive performance, and topic coherence. Experiments on different genres of large text corpora have demonstrated the effectiveness of our models as they outperform both probabilistic and neural methods.