Gabrielle Zevin likes talking about failure. Her first novel for adults, released almost 20 years ago, did "really badly," by her account. "I had really never had a failure like that in my life," Zevin said. At the time, she was living in New York City, and it seemed as though the whole world was bearing witness to her defeat. "I thought I would go into a store and they would be like, 'Here is your bagel, here is your lox and sorry your novel failed so badly,'" she said.

Moreover, linear interpolation between parameters may not result in perceptually linear interpolation between the sounds [1]. In this paper, we propose a data-driven approach to train a Generative The goal of parametric audio texture synthesis is to generate Adversarial Network (GAN) conditioned on "soft-labels" distilled novel sounds with descriptive parameters that match those of a target from the penultimate layer of an audio classifier trained on a texture. McDermott et al. [4] developed a set of statistics based on target set of audio texture classes. We demonstrate that interpolation a cochlear model to describe the perceptually relevant aspects of a between such conditions or control vectors provide smooth morphing given audio texture. Recent works [10, 5, 11] have adapted the seminal between the generated audio textures, and show similar or better work on image style transfer [12] for audio texture synthesis, audio texture morphing capability compared to the state-of-the-art where hand-crafted statistics are replaced with Gram matrix statistics methods. The proposed approach results in a well-organized latent computed as the correlation between feature activations to represent space that generates novel audio outputs while remaining consistent style. Though this method of audio style transfer produces interesting with the semantics of the conditioning parameters. This is a step combinations of the sounds, there is no control of semantic style towards a general data-driven approach to designing generative audio or content features other than through the data examples provided.

This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.

The drone has been used for various purposes, including military applications, aerial photography, and pesticide spraying. However, the drone is vulnerable to external disturbances, and malfunction in propellers and motors can easily occur. To improve the safety of drone operations, one should detect the mechanical faults of drones in real-time. This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. The dataset includes various operating conditions of drones, such as flight directions (front, back, right, left, clockwise, counterclockwise) and faults on propellers and motors. The drone sounds were then mixed with noises recorded in five different spots on the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB to 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms. We employ multitask learning (MTL) and incorporate the direction classification task as an auxiliary task to make the classifier learn more general audio features. The test over unseen data reveals that the proposed multitask model can successfully classify faults in drones and outperforms single-task models even with less training data.

This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.

Based on the 10.9K articles from top 40 news providers of South Korea, this paper analyzed the media framing of Itaewon Halloween Crowd Crush during the first 72 hours after the incident. By adopting word-vector embedding and clustering, we figured out that conservative media focused on political parties' responses and the suspect's identity while the liberal media covered the responsibility of the government and possible unequal spillover effect on the low-income industry workers. Although the social tragedy was not directly connected to institutional politics, the media clearly exhibited political bias in the coverage process.

AI chatbots are all the rage on the internet right now, but how much do you know about how the tech is being trained? The Washington Post explains how text that's mostly scraped from the internet is ingested and transformed into human-like speech, including training material from "proprietary, personal and often offensive websites." Being a celebrity in the social media age means playing a constant game of whack-a-mole fighting imposters. The Hollywood Reporter explains how paid verification has only increased the challenge and how companies like Social Imposter are enlisted to help. Great deals on consumer electronics delivered straight to your inbox, curated by Engadget's editorial team.

Eugenia Kuyda defended AI companion bots during the interview with Fox News Digital and argued that dating app Replika is just one of many possible solutions to loneliness. Tech giants are ill-prepared to combat "hallucinations" generated by artificial intelligence platforms, industry experts warned in comments to Fox News Digital, but corporations themselves say they're taking steps to ensure accuracy within the platforms. AI chatbots, such as ChatGPT and Google's Bard, can at times spew inaccurate misinformation or nonsensical text, referred to as "hallucinations." "The short answer is no, corporation and institutions are not ready for the changes coming or challenges ahead," said AI expert Stephen Wu, chair of the American Bar Association Artificial Intelligence and Robotics National Institute, and a shareholder with Silicon Valley Law Group. Often, hallucinations are honest mistakes made by technology that, despite promises, still possess flaws.

An AI art lecturer said he believes the U.S. government would encounter difficulty if it attempted to establish a watermark system for AI-generated content. The U.S. will likely have a tough time trying to regulate AI-generated content, such as requiring watermarks on computer-made media, a university art lecturer told Fox News. "[F]or us to enforce it would be a lot more difficult," Tyler Coleman, who teaches University of Texas classes focused on AI, said. "I think it will be harder to achieve in the U.S. than it would be in China." China's government announced regulations in December 2022 requiring any AI-generated content to include a flag such as a watermark to indicate its origin.

The rise of artificial intelligence is entrenching more economic and political power in the hands of Big Tech companies, according to researchers at New York University (NYU) who argue AI must undergo more scrutiny and regulation. AI Now, a research institute at NYU, released a new report detailing how major tech companies wield significant control over AI, arguing such influence must be addressed now before the situation gets too out of hand. "This report is written with this task in mind: we are drawing from our experiences inside and outside government to outline an agenda for how we -- as a group of individuals, communities, and institutions deeply concerned about the impact of AI unfolding around us -- can meaningfully confront the core problem that AI presents, and one of the most difficult challenges of our time: the concentration of economic and political power in the hands of the tech industry -- Big Tech in particular," the document states. The authors add that AI development has been "foundationally reliant" on resources controlled by Big Tech, including data and computer power. Plus, they write, Big Tech companies have gained geopolitical importance by playing a central role in the U.S.-China race for AI supremacy, thereby conflating "the continued dominance of Big Tech as synonymous with U.S. economic prowess, and [ensuring] the continued accrual of resources and political capital to these companies."