The Wizard of Oz movie is filled with classic quotes. One of them is simply, "Pay no attention to that man behind the curtain!" That quote, of course, occurs when Dorothy's dog, Toto, pulls back a curtain to reveal a man at a control panel and microphone. Rather than the sought-after magical Wizard of Oz, the man is, at best, a wizard of special effects. Today, nearly 80 years after the first release of the movie version of Wizard of Oz, we live with a wizard of our own making who is now often behind the curtain in video games, movies and sporting events.

Over 175 financial institutions will be able to provide 8 million customers access to the tunl.chat TORONTO -- FICANEX and Ada today announced the release of tunl.chat, an AI-powered chatbot that provides more than 175 financial institutions across Canada with another channel to support approximately 8 million customers. Financial institutions can brand and tailor tunl.chat to suit their unique requirements and deploy the chatbot within their website and mobile apps. Nine financial institutions, including FirstOntario Credit Union and Kawartha Credit Union in Ontario, plus BlueShore Financial and First West Credit Union in B.C., have already committed to adopt the service. Initial implementation projects are scheduled to commence September 2019.

You are free to share this article under the Attribution 4.0 International license. With training from humans and machines, an artificial intelligence model can outperform other clickbait detectors, according to new research. In addition, the new AI-based solution was also able to tell the difference between headlines that machines--or bots--generated and ones people wrote, they says. In a study, the researchers asked people to write their own clickbait--an interesting, but misleading, news headline designed to attract readers to click on links to other online stories. The researchers also programmed machines to generate artificial clickbait.

A new Chinese mobile deepfake application that allows users very easily swap their faces with any specific faces in any video clip was launched on Friday the 30th of August, 2019. The app, Zao, became an instant success an almost immediately became the most downloaded mobile app on China's iOS App Store, racking up several million downloads and shooting the app to the top of the store. However, several concerns have been drawn over the app's infringement of privacy and a host of other violations. The app employs advanced artificial intelligence (AI) technology, letting users upload a personal picture which then gets super-imposed on already made video clips from music, TV shows, and movies. In just a few seconds, one user created a video where he replaced Hollywood actor Leonardo DiCaprio's face with that of his, instantly adding himself to the cast of 1997's Titanic movie. What started as a really fun app now seems to be slowly becoming objectionable.

Auggie takes Her one step further. In the popular 2013 Spike Jonze film, the main character was a lonely man who fell in love with an artificially intelligent voice. In Auggie, the main character has a family and the artificially intelligent being has a physical form. But very similar issues arise. Co-written and directed by Matt Kane, Auggie stars legendary character actor and voice actor Richard Kind as Felix, a man who is given a very special gift at his retirement party.

As there have been some interesting discussions on the alternatives to backpropagation lately (e.g. this reddit thread), I am sharing our latest work just made available on arXiv: Summary: Building on feedback-alignment algorithms, we show how to train multi-layer neural networks using random projections of the target vector, which enables layerwise weight updates using only local and feedforward information. The proposed algorithm is called direct random target projection (DRTP). While backpropagation (BP) requires forward and backward weight symmetry (i.e. Indeed, estimating the layerwise loss gradients only requires a label-dependent random vector selection, making adaptive smart sensors and edge computing the ideal applications due to limited power and computing resources. Despite its simplicity, we demonstrate on the MNIST and CIFAR-10 datasets that DRTP performs close to BP, feedback alignment (FA), direct feedback alignment (DFA) algorithms.

You can certainly write rules with machine learning models. That is one approach: to create a machine learning model that is a rule or a set of rules.

The system helps significantly reduce the time consumed in post-production. In addition, based on an AI-powered celebrity database of more than 2 million characters, iQIYI has also established an intelligent index which helps improve the production efficiency and quality of large-scale variety shows. This technology has become an industrial standard that is widely adopted to produce new-gen variety shows. Aside from professional content production, iQIYI also uses AI technology to assist UGC content creators with their productions by offering them massive creative content libraries and supplementary creative functions through an intelligent recreation system. Held under the theme of "Artificial Intelligence-New Momentum for High Quality Development", this year's WAIC aims at building the world's top AI cooperation and exchange platform, as well as showing that AI is leading the transformation of the modern life in Shanghai.

Abstract: Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from the source domain to a specific known target domain. Domain Agnostic Learning (DAL) proposes a new task of transferring knowledge from the source domain to data from multiple heterogeneous target domains. In this work, we propose the Domain-Agnostic Learning framework with Anatomy-Consistent Embedding (DALACE) that works on both domain-transfer and task-transfer to learn a disentangled representation, aiming to not only be invariant to different modalities but also preserve anatomical structures for the DA and DAL tasks in cross-modality liver segmentation. We validated and compared our model with state-of-the-art methods, including CycleGAN, Task Driven Generative Adversarial Network (TD- GAN), and Domain Adaptation via Disentangled Representations (DADR).