Fox News Flash top headlines for Sept. 18 are here. Check out what's clicking on Foxnews.com Artificial intelligence-based solutions may not be able to save us from deceptively altered videos, known as deepfakes, according to a new report from Data and Society. In the report, authors Britt Paris and Joan Donovan put deepfakes on a long continuum of media manipulation and say that they require social and technical fixes. "The panic around deepfakes justifies quick technical solutions that don't address structural inequality," Paris told The Verge.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

--The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy becomes plateaued. In this research, we propose an ensemble neural architecture to detect rumor on Twitter . The architecture incorporates word attention and context from the author to enhance the classification performance. In particular, the word-level attention mechanism enables the architecture to put more emphasis on important words when constructing the text representation. T o derive further context, microblog posts composed by individual authors are exploited since they can reflect style and characteristics in spreading information, which are significant cues to help classify whether the shared content is rumor or legitimate news. The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results. It is indisputable that social media has significant influences on people's lives these days.

Chinese word segmentation (CWS) is a fundamental task for Chinese language understanding. Recently, neural network-based models have attained superior performance in solving the in-domain CWS task. Last year, Bidirectional Encoder Representation from Transformers (BERT), a new language representation model, has been proposed as a backbone model for many natural language tasks and redefined the corresponding performance. The excellent performance of BERT motivates us to apply it to solve the CWS task. By conducting intensive experiments in the benchmark datasets from the second International Chinese Word Segmentation Bake-off, we obtain several keen observations. BERT can slightly improve the performance even when the datasets contain the issue of labeling inconsistency. When applying sufficiently learned features, Softmax, a simpler classifier, can attain the same performance as that of a more complicated classifier, e.g., Conditional Random Field (CRF). The performance of BERT usually increases as the model size increases. The features extracted by BERT can be also applied as good candidates for other neural network models.

You can now see what you look like through the eyes of an AI. ImageNet Roulette was trained with millions of images and uses a neural network to classify pictures of people, with some'dubious and cruel' results. The technology was developed to show the importance of choosing the correct data when training a machine learning system, as it may learn how to be bias. The AI was trained using ImageNet, which is a massive 14 million image data system created in 2009. ImageNet Roulette uses a neural network to classify pictures (such as this one of Kim Kardashian West) of people with some'dubious and cruel' results.

Artificial Intelligence (AI) and machine learning (ML) applications are growing and expanding into all industries and functions.

In recent years many use the term along with machine learning to describe developments in the capabilities of software programs and machinery.

Called Samply, Swaney's visual sample-library explorer combines music and machine learning into a new technology for producers.

Machine learning (ML) is a growing field in medicine.

And for the most part, blfloat16 can be a "drop-in" replacement for FP32 in these machine learning algorithms.