Today's podcast features recent interviews with [two] experts in top AI management positions discussing recent developments within their companies and the overall sector: Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC PINK:GTCH), and Mr. Babak Hodjat, VP of Evolutionary AI, Cognizant Technology Solutions Corporation (NasdaqGS:CTSH). Listen to the podcast interview with Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC PINK:GTCH) discussing the company's recently announced implementation and development of recurrent relational reasoning (RRN) in its AI, and its applications in the medical field. In a recently published press release, GBT Technologies CTO, Dr. Danny Rittman explained the company's rationale for incorporating recurrent relational reasoning (RRN) into its Avant! "Our goal is to implement a fundamental part of human intelligence called relational reasoning, which is planned to enable Avant! to acquire expertise on its own by understanding object's relations. Avant! will include an advanced artificial neural network (ANN) capable of pattern recognition and reasoning about those patterns which is very similar to the human brain."

Artificial intelligence is advancing society and creating pathways to completely transform the everyday. The new possibilities it creates are defined not by software and hardware, or numbers and data. They are instead defined by how they help people lead better, happier and safer lives. This mini-series will examine AI and how it actually affects the world around us, with one company's digital transformation in focus. At Thales, AI is being created for decisive moments.

Google's Pixel 4 phones are here. There are two new models to choose from: the Pixel 4 and the larger Pixel 4 XL. If you're trying to decide which one to get and where to buy it, look no further. We've broken down all the preordering options and found the best places to snag a new Pixel 4 before it ships on October 24. If you'd like to see what else Google announced, including other new devices like the Pixel Buds earphones, Pixelbook Go laptop, and Nest Mini speaker with Google Assistant, check out our full coverage of Google's fall hardware event.

Get the Full Experience Read the rest of this article, and view all articles in full from just £10 for 3 months.

Everybody has heard about fake news. Fake news is a neologism that can be formally defined as a type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional print and broadcast news media or online social media. It is also commonly used to refer to fabricated or junk news, with no basis in fact, but presented as being factually accurate. The reason for putting someone's efforts in creating fake news is mainly to cause financial, political or reputational damage to people, companies or organizations, using sensationalist, dishonest, or outright fabricated headlines to increase readership and dissemination among readers using viralization. In addition, clickbait stories, a special type of fake news, earn direct advertising revenue from this activity.

In 2018 the Reddit community r/deepfakes gained international attention thanks to a piece of investigative journalism by Samantha Cole, deputy editor at VICE. Members of the forum had been using a burgeoning technology to superimpose celebrities' faces onto pornographic videos. For the general public – and no doubt the unwitting stars – it was a shock. Most were unaware this technology existed. Very few believed it was possible to produce such realistic footage.

Television is an ever-evolving multi billion dollar industry. The success of a television show in an increasingly technological society is a vast multi-variable formula. The art of success is not just something that happens, but is studied, replicated, and applied. Hollywood can be unpredictable regarding success, as many movies and sitcoms that are hyped up and promise to be a hit end up being box office failures and complete disappointments. In current studies, linguistic exploration is being performed on the relationship between Television series and target community of viewers. Having a decision support system that can display sound and predictable results would be needed to build confidence in the investment of a new TV series. The models presented in this study use data to study and determine what makes a sitcom successful. In this paper, we use descriptive and predictive modeling techniques to assess the continuing success of television comedies: The Office, Big Bang Theory, Arrested Development, Scrubs, and South Park. The factors that are tested for statistical significance on episode ratings are character presence, director, and writer. These statistics show that while characters are indeed crucial to the shows themselves, the creation and direction of the shows pose implication upon the ratings and therefore the success of the shows. We use machine learning based forecasting models to accurately predict the success of shows. The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows. Due to the many factors that go into a series, the empirical analysis in this work shows that there is no one-fits-all model to forecast the rating or success of a television show.

--Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. T o transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping. The domain adaptive user mapping module can help the incompatible domains to transfer the knowledge across each other . Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9 .2% Recommending users with a set of preferred items is still an open problem [1]-[6], especially when the dataset is very sparse. To remedy the data sparsity issue, broad-leraning based model [7] and cross-domain recommender system [4], [8] are proposed where the information from other source domains can be transferred to the target domain. To transfer the knowledge from one domain to another, one can use the overlapping users [4], [6], [8], [9] in two ways: (1) the neighborhood information of common users stores the structure information of different domains with which we can do cross-domain recommendation [6], [10]; or (2) we can learn a mapping function [4], [8] to project latent vectors learned in one domain into another, and thus the knowledge can be transferred.

It's no secret that government is hot on artificial intelligence as a tool to improve the citizen experience – especially given the president's recent signing …