PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. COVID: Artificial intelligence-generated COVID drug enters clinical trials. WORK TOGETHER: Embracing AI means we must mitigate risk to firms, industries, consumers and society. TERRIFYING TECH: Criminal enterprise flaunts AI in creepy commercial meant for dark web.

Join us! See the full lineup and purchase tickets. For the twenty-fourth year, the pages of The New Yorker will come to life for three days in New York City. The 2023 New Yorker Festival, hosted by the magazine's writers and editors, will take place October 6th through 8th, featuring onstage interviews, musical performances, live cartooning, and panels on topics ranging from artificial intelligence to the craft of investigative journalism. Guests will include authors, artists, filmmakers, actors, comedians, and musicians, all sharing the distinction of being at the forefront of their fields. Subscribers to The New Yorker now have exclusive access to the twenty-four-hour ticket presale.

Fox News correspondent David Spunt has the latest on questions over whether the former president can hold office again on Special Report. A liberal reporter added fuel to online fire that a conservative news outlet was duped by a former President Trump impersonator, or even artificial intelligence – resulting in an embarrassing editor's note. Last week, Trump called into right-wing channel Real America's Voice for an interview that resulted in online speculation that the outlet had spoken with an impostor. Audio was shaky, and speculation erupted that Trump either had a cold, poor service or something more malicious, such as someone impersonating the 45th president, or modern technology generating the interview with old clips of Trump. Zachary Petrizzo, a politics reporter for the left-wing Daily Beast, took things a step further and reported that Real America's Voice owner Robert Sigg told him that the company would investigate whether the call was some sort of prank.

This week, the panel jumps into Bottoms, the chaotic second feature from director and co-writer Emma Seligman that satirizes… something (what that thing is, they have yet to discover). They then discuss Telemarketers, a Michael Moore-style documentary that exposes the telemarketing industry's dark underbelly in a weirdly captivating tour de force. Finally, the trio takes on Strike Force Five, a new Spotify podcast hosted by late-night veterans Jimmy Kimmel, Jimmy Fallon, Stephen Colbert, John Oliver, and Seth Meyers that deals with the ins and outs of the trade and raises money for their striking writing staffs. In the exclusive Slate Plus segment, the panel considers the joys of trains and sleeper cars, inspired by Bryn Stole's essay for Slate, "Wake on a Train." Dana: A very funny, investigative piece in The Guardian by Elif Batuman: "Proust, ChatGPT and the case of the forgotten quote." Julia: In a wonderfully kismet moment, Julia stumbled upon Hilltown Hot Pies, a neapolitan-ish pizzeria in the Berkshires run by chef Rafi Bildner, who previously owned one of Stephen's favorite pizza spots in Ghent.

The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online. While several recent efforts have focused on detecting AI-generated text in general, these methods require enhanced reliability, given concerns about their vulnerability to simple adversarial attacks. Furthermore, due to the eccentricities of news writing, applying these detection methods for AI-generated news can produce false positives, potentially damaging the reputation of news organizations. To address these challenges, we leverage the expertise of an interdisciplinary team to develop a framework, J-Guard, capable of steering existing supervised AI text detectors for detecting AI-generated news while boosting adversarial robustness. By incorporating stylistic cues inspired by the unique journalistic attributes, J-Guard effectively distinguishes between real-world journalism and AI-generated news articles. Our experiments on news articles generated by a vast array of AI models, including ChatGPT (GPT3.5), demonstrate the effectiveness of J-Guard in enhancing detection capabilities while maintaining an average performance decrease of as low as 7% when faced with adversarial attacks.

Automatic singing voice understanding tasks, such as singer identification, singing voice transcription, and singing technique classification, benefit from data-driven approaches that utilize deep learning techniques. These approaches work well even under the rich diversity of vocal and noisy samples owing to their representation ability. However, the limited availability of labeled data remains a significant obstacle to achieving satisfactory performance. In recent years, self-supervised learning models (SSL models) have been trained using large amounts of unlabeled data in the field of speech processing and music classification. By fine-tuning these models for the target tasks, comparable performance to conventional supervised learning can be achieved with limited training data. Therefore, in this paper, we investigate the effectiveness of SSL models for various singing voice recognition tasks. We report the results of experiments comparing SSL models for three different tasks (i.e., singer identification, singing voice transcription, and singing technique classification) as initial exploration and aim to discuss these findings. Experimental results show that each SSL model achieves comparable performance and sometimes outperforms compared to state-of-the-art methods on each task. We also conducted a layer-wise analysis to further understand the behavior of the SSL models.

Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity. In this paper we propose a supervised approach for the task of music boundary detection. In our approach we simultaneously learn features and convolution kernels. For this we jointly optimize -- a loss based on the Self-Similarity-Matrix (SSM) obtained with the learned features, denoted by SSM-loss, and -- a loss based on the novelty score obtained applying the learned kernels to the estimated SSM, denoted by novelty-loss. We also demonstrate that relative feature learning, through self-attention, is beneficial for the task of MSA. Finally, we compare the performances of our approach to previously proposed approaches on the standard RWC-Pop, and various subsets of SALAMI.

The rapidly evolving field of Explainable Artificial Intelligence (XAI) has generated significant interest in developing methods to make AI systems more transparent and understandable. However, the problem of explainability cannot be exhaustively solved in the abstract, as there is no single approach that can be universally applied to generate adequate explanations for any given AI system, and this is especially true in the arts. In this position paper, we propose an Explanatory Pragmatism (EP) framework for XAI in music performance, emphasising the importance of context and audience in the development of explainability requirements. By tailoring explanations to specific audiences and continuously refining them based on feedback, EP offers a promising direction for enhancing the transparency and interpretability of AI systems in broad artistic applications and more specifically to music performance.

Our interdisciplinary study investigates how effectively U.S. laws confront the challenges posed by Generative AI to human values. Through an analysis of diverse hypothetical scenarios crafted during an expert workshop, we have identified notable gaps and uncertainties within the existing legal framework regarding the protection of fundamental values, such as privacy, autonomy, dignity, diversity, equity, and physical/mental well-being. Constitutional and civil rights, it appears, may not provide sufficient protection against AI-generated discriminatory outputs. Furthermore, even if we exclude the liability shield provided by Section 230, proving causation for defamation and product liability claims is a challenging endeavor due to the intricate and opaque nature of AI systems. To address the unique and unforeseeable threats posed by Generative AI, we advocate for legal frameworks that evolve to recognize new threats and provide proactive, auditable guidelines to industry stakeholders. Addressing these issues requires deep interdisciplinary collaborations to identify harms, values, and mitigation strategies.

Diffusion models are getting popular in generative image and video synthesis. However, due to the diffusion process, they require a large number of steps to converge. To tackle this issue, in this paper, we propose to perform the diffusion process in the gradient domain, where the convergence becomes faster. There are two reasons. First, thanks to the Poisson equation, the gradient domain is mathematically equivalent to the original image domain. Therefore, each diffusion step in the image domain has a unique corresponding gradient domain representation. Second, the gradient domain is much sparser than the image domain. As a result, gradient domain diffusion models converge faster. Several numerical experiments confirm that the gradient domain diffusion models are more efficient than the original diffusion models. The proposed method can be applied in a wide range of applications such as image processing, computer vision and machine learning tasks.