Be it the Avatar, Minority Report or Iron Man, there is a common element that you can notice in most science fiction films. Very hardly can you see a futuristic narration where characters don't smoothly interact with digital content that surrounds them. With just a swipe or a gesture, you see people accessing amazing levels of information regardless of the surrounding they are in. This kind of smooth interaction with the digital world through the sense of touch is something that could soon become a norm in our day to day life. Such has been the growth of Haptic Technology.

Nintendo Labo, the whimsical collection of DIY cardboard toys for the Switch, has received a surge of mainstream attention. Recently, Jimmy Fallon assembled The Roots and Ariana Grande for a Labo-powered rendition of "No Tears Left To Cry." Now, a composer you're all very familiar with got his hands on the Nintendo Labo Piano to see if he could use nothing but cardboard and console to rock his iconic Game of Thrones theme song. Ramin Djawadi is best known for the Game of Thrones theme, but he's a prolific composer who has also scored high-profile films like Iron Man, Pacific Rim and Warcraft. Musical genius aside, he's never touched a Nintendo Switch. So IGN sat him down in front of a Nintendo Labo Piano to see what he could accomplish with the surprisingly versatile cardboard synthesizer.

BANGKOK – Household product maker Lion Corp. has opened the first movie theater for children in Thailand, in collaboration with the largest local cinema complex operator, naming it after Lion's own "Kodomo" brand for kids' products to boost name recognition. Lion recently launched Kodomo Kids Cinema with Major Cineplex Group Public Co. at Mega Bangna Shopping Mall in Bangna, in the east of Bangkok, aiming to attract children aged 5 to 12 along with their parents, according to the Japanese company. The cinema offers 84 candy-colored seats, ranging in type from single seats to double sofa-beds, and also houses entertainment facilities such as a long slide and an indoor playground. Ticket prices are between 200 baht and 500 baht ($6.20-$15.50). The cinema, located in a district with 31 international and elementary schools, is scheduled to show 20 movies this year -- including one featuring Doraemon, the robotic cat from Japan's popular cartoon series -- while various events like fashion shows and drawing workshops are held, according to the local partner.

Does anyone know of any large email data sets (that are not Enron) hopefully something over the last few years or so. We are trying to work on different platforms to test their sentiment analysis.

There are many things that Westworld is. Its scenes are sculpted with the same beauty and care that went into building the robots' exquisite bodies. It explores with delicacy the adjacent modes of consciousness that accompany sophisticated artificial intelligence. And then there are the things that Westworld is not. There is, for starters, the total absence of meaningful relationships.

The local authority, Council of Agriculture (COA), guided seeds manufacturers to improve F1 hybrid seed collecting techniques that resulted in superior harvest performance of cruciferous vegetables, watermelons and cherry tomatoes. The featured exhibitors invited to showcase at the pavilion of Taiwan Seed Varieties this year include Besgrow Seed, Ching Long Seed, Agronew Trading, Taiwan Banana Research Institute, Known-You Seed and Sing-Flow Seed Trading. One of the exhibitors - CH Biotech, developed'Weather Mend,' a product to regulate plant gene expression system with non-genetically modified active components, which is seen as a crucial solutions for crops in facing extreme climate change. This product aims to increase plant survival rate and helps steady food supply. Not only will it initiate plants' defence mechanism in advance, but also reduce losses caused by severe weather.

Westworld is an HBO television series based on American novelist Michael Crichton's 1973 futuristic science-fiction thriller about an android Western theme park. According to Variety, the first season of Westworld garnered an average of approximately 13.2 million viewers per episode. There are many reasons contributing to the show's popularity. On the surface level, the series is thrilling, complex, chilling, unpredictable, and completely unsettling – the very emblem of dystopian nightmares. On a deeper level, the show raises many philosophical questions on the potential impact of human-like robots powered by artificial intelligence (AI).

Yes, but it also groups different words that have the same base form. So biographies and texts about animals might be wrongly grouped together, introducing noise in the corpus. I suspect, depending on the language, that this can happen a lot (or not) and greatly influence the process. I know it helps in Finnish and French and doesn't help in Swedish (with the texts I've used; I have compared LDA output on lemmatised and non-lemmatised versions of the same corpus), I was wondering if you had experience with other languages?

Topic models have been widely explored as probabilistic generative models of documents. Traditional inference methods have sought closed-form derivations for updating the models, however as the expressiveness of these models grows, so does the difficulty of performing fast and accurate inference over their parameters. This paper presents alternative neural approaches to topic modelling by providing parameterisable distributions over topics which permit training by backpropagation in the framework of neural variational inference. In addition, with the help of a stick-breaking construction, we propose a recurrent network that is able to discover a notionally unbounded number of topics, analogous to Bayesian non-parametric topic models. Experimental results on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the effectiveness and efficiency of these neural topic models.

Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functions and hindsight learning, allowing agents to learn policies belonging to different time scales in parallel. We show that our method significantly accelerates learning in a variety of discrete and continuous tasks.