Recognizing a speaker's emotion from their speech can be a key element in emergency call centers. End-to-end deep learning systems for speech emotion recognition now achieve equivalent or even better results than conventional machine learning approaches. In this paper, in order to validate the performance of our neural network architecture for emotion recognition from speech, we first trained and tested it on the widely used corpus accessible by the community, IEMOCAP. We then used the same architecture as the real life corpus, CEMO, composed of 440 dialogs (2h16m) from 485 speakers. The most frequent emotions expressed by callers in these real life emergency dialogues are fear, anger and positive emotions such as relief. In the IEMOCAP general topic conversations, the most frequent emotions are sadness, anger and happiness. Using the same end-to-end deep learning architecture, an Unweighted Accuracy Recall (UA) of 63% is obtained on IEMOCAP and a UA of 45.6% on CEMO, each with 4 classes. Using only 2 classes (Anger, Neutral), the results for CEMO are 76.9% UA compared to 81.1% UA for IEMOCAP. We expect that these encouraging results with CEMO can be improved by combining the audio channel with the linguistic channel. Real-life emotions are clearly more complex than acted ones, mainly due to the large diversity of emotional expressions of speakers. Index Terms-emotion detection, end-to-end deep learning architecture, call center, real-life database, complex emotions.

Existed pre-trained models have achieved state-of-the-art performance on various text classification tasks. These models have proven to be useful in learning universal language representations. However, the semantic discrepancy between similar texts cannot be effectively distinguished by advanced pre-trained models, which have a great influence on the performance of hard-to-distinguish classes. To address this problem, we propose a novel Contrastive Learning with Label Distance (CLLD) in this work. Inspired by recent advances in contrastive learning, we specifically design a classification method with label distance for learning contrastive classes. CLLD ensures the flexibility within the subtle differences that lead to different label assignments, and generates the distinct representations for each class having similarity simultaneously. Extensive experiments on public benchmarks and internal datasets demonstrate that our method improves the performance of pre-trained models on classification tasks. Importantly, our experiments suggest that the learned label distance relieve the adversarial nature of interclasses.

As scientists laid the foundations of artificial intelligence, Pamela McCorduck was there. McCorduck, an author who wrote some of the first novels and histories about AI and was a generous friend of CMU, died Oct. 18. McCorduck described herself as an eyewitness to the birth and growth of AI. She was possibly best known for her 1979 book, "Machines Who Think," which chronicles the history of AI from the dreams and nightmares of ancient poets and prophets to the scientific discoveries of the 20th century. The novel contains the famous quote, "Artificial intelligence began with the ancient wish to forge the gods."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A suspended Virginia Tech linebacker accused in the fatal beating of a Tinder match has been indicted on a charge of second-degree murder. Isimemen Etute, 18, who is accused in the death of 40-year-old Jerry Smith in May, was indicted by a grand jury Tuesday, The Roanoke Times reported. A hearing is scheduled Nov. 18.

When I was a kid, one of my cousins got a DVD player for Christmas. My family watched in awe as he connected it to the TV and inserted a skinny little disc which miraculously played a movie. The next day, my mom wanted to rush out to the store to buy one. VHS' worked fine in my mind. I couldn't understand what the big deal was, so I asked my Mom "Why?"

Intel's deep investments in developer ecosystems, tools, technology and an open platform are clearing the path forward to scale AI everywhere. Intel's role is to responsibly scale this technology. Intel has made AI more accessible and scalable for developers through extensive optimizations of popular libraries and frameworks on Intel Xeon Scalable processors. Intel's investment in multiple AI architectures to meet diverse customer requirements, using an open standards-based programming model, makes it easier for developers to run more AI workloads in more use cases. Many of the world's leading organizations leverage Intel AI to solve complex tasks, as evidenced by today's announcements: "Innovation thrives in open environments where developers connect, communicate and collaborate freely. Technology is a human creation and builds what is possible," said Greg Lavender, chief technology officer, senior vice president and general manager of the Software and Advanced Technology Group at Intel.

At a recent conference on the challenges of artificial intelligence, Christof Koch made clear in his remarks that the stakes were high: "By mid-century, humanity will be surrounded by ubiquitous, flexible, highly intelligent autonomous agents, and this will profoundly affect our future--including whether we have any." Dr. Koch--who is the chief scientist of the Mindscope Program at the Allen Institute for brain science in Seattle--was speaking to a group of roughly a hundred academics, diplomats and journalists. The conference was hosted by the Vatican at the Cancelleria, a 15th-century Renaissance palace in Rome, and centered around the theme of "the challenge of artificial intelligence for human society and the idea of the human person." This was the second event at the Vatican to focus on artificial intelligence, commonly abbreviated as A.I. Just before Italy entered into a nationwide lockdown last year, the Pontifical Academy for Life held a workshop on A.I. in February 2020. This workshop ultimately produced a "Call for AI Ethics," which was signed by Microsoft, IBM, the Food and Agricultural Organization of the United Nations and the Italian government, in addition to the Academy.

This article offers an overview of the main first-order ethical questions raised by robots and Artificial Intelligence (RAIs) under five broad rubrics: functionality, inherent significance, rights and responsibilities, side-effects, and threats. The

What Sassoon had heard were the early results of a curious project at the University of Edinburgh in Scotland, where Ducceschi was a researcher at the time. The Next Generation Sound Synthesis, or NESS, team had pulled together mathematicians, physicists, and computer scientists to produce the most lifelike digital music ever created, by running hyper-realistic simulations of trumpets, guitars, violins, and more on a supercomputer. Sassoon, who works with both orchestral and digital music, "trying to smash the two together," was hooked. He became a resident composer with NESS, traveling back and forth between Milan and Edinburgh for the next few years. It was a steep learning curve.

Music artists, composers and producers today swim in massive amounts of musical notes to test the barriers of what melodies, harmonies and symphonies they can create and what works best with their songs. Although the advances in technology have significantly simplified and streamlined the process, it is still a long and challenging one for everyone involved in music creation. However, a technological revolution may be about to chance music creation as we know it. A team of computer scientists were able to use AI to complete the unfinished 10th symphony, originally created over 250 years ago by Ludwig Van Beethoven. This project has provoked interesting discussions, such as whether the now completed symphony is what Beethoven was originally trying to create, and also raised the important question -- what can Artificial Intelligence (AI) and Machine learning (ML) do for music production in the music entertainment industry? The team at Brainpool have been pondering on the answer to the latter, so we took the time to test a few of the various readily available AI music demos and reflected on how they could help transform the music industry.