Such is its meteoric rise, that its adoption among businesses increased by 60% between 2017 and 2018. Despite the obvious potential, recent events have exposed how automated systems can both intentionally and unintentionally lead to bias. For example, accidental bias was identified in cases where algorithms manage digital ads for STEM roles. With this trend only expected to accelerate, it is critical that the risk of bias is recognised and addressed. While AI bias is creeping into the business world, a recent UNESCO report provided more concerning findings, revealing that voice-activated assistants with female voices such as Amazon's Alexa instil views of gender subservience.

In early 2017, Alex Borek of Volkswagen convinced me that "this time, machine learning is real" and that data quality was a real problem. So I dug in--researched what we knew, talked to a lot of people, and thought through the various ways that bad data could do harm. And it struck me--this is really scary. As I use that metaphor, I find that practically everyone agrees! The next steps were to sort out what to do and write a straightforward article.

Dr. Madeline Gannon is a multidisciplinary designer inventing better ways to communicate with machines. In her work, Gannon seeks to blend knowledge from design, robotics, and human-computer interaction to innovate at the intersection of art and technology. Gannon designs her research to engage with wide audiences across scientific and cultural communities: her work has been exhibited at international cultural institutions, published at ACM conferences, and covered by diverse global media outlets. Her 2016 interactive installation, Mimus, even earned her the nickname, "The Robot Whisperer." She is three-time World Economic Forum Cultural Leader, and serves as a council member on the World Economic Forum Global Council for IoT, Robotics, & Smart Cities.

Music is the most popular art form that is performed and listened to by billions of people every day. There are many genres of music such as pop, classical, jazz, folk etc. Each genre has different music instruments, tone, rhythm, beats, flow etc. Digital music and online streaming have become very popular these days due to the increase in the number of users. To create a machine learning model, which classifies music samples into different genres. To classify a music sample or song manually, the person has to listen to the song and select the genre.

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.