Apple has been noticeably missing in the list of companies with their own generative AI product, but based on a new report by The New York Times, it's looking to change that real soon. In recent weeks, Apple has reportedly started negotiating with major publishers and news organizations to ask for permission to use their content to train the generative AI system it's developing. The company doesn't expect to get its hands on their content for free, though, and The Times says it's offering them multi-year deals worth at least $50 million for access to their news archives. Apparently, some of the publishers it approached are concerned about the repercussions of letting Apple use their news articles throughout the years. They think a broad licensing deal for their archives could lead to legal issues along the way.

Gif-sur-Yvette 91191 Abstract We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be effectively redefined for LLMs, potentially contributing to improved AI governance. Acknowledging the balance that must be struck when employing the DURC framework, we highlight its crucial political role in enhancing societal awareness of the impact of generative AI. As a final point, we offer a series of specific recommendations for applying the DURC approach to LLM research. Keywords: Dual Use Research of Concern (DURC), Generative AI, Large Language Models (LLMs), AI Ethics Conflict of interest No conflict of interest to report. Funding This research was supported through projects TechEthos (grant number 101006249) and MultiRATE (grant number 101073929) funded by the European Commission Horizon program. Ethics approval No human subjects were involved in the study. Consent No data needing consent has been used. Data availability statement In this article, we do not analyze or generate any datasets. Author Contribution All authors contributed to the study conception and design. Sections 1 and 4 were written with equal contribution. Sections 2 and 3 were conceived by Adomaitis and later edited by Grinbaum.

In this work, we propose a symbolic music generation model with the song structure graph analysis network. We construct a graph that uses information such as note sequence and instrument as node features, while the correlation between note sequences acts as the edge feature. We trained a Graph Neural Network to obtain node representation in the graph, then we use node representation as input of Unet to generate CONLON pianoroll image latent. The outcomes of our experimental results show that the proposed model can generate a comprehensive form of music. Our approach represents a promising and innovative method for symbolic music generation and holds potential applications in various fields in Music Information Retreival, including music composition, music classification, and music inpainting systems.

User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions. The research of text and graph mining is developing rapidly, contributing many notable solutions in the past two decades. Recently, large language models (LLMs) have shown superior performance on generating, understanding, and even reasoning over text data. The approaches of user modeling have been equipped with LLMs and soon become outstanding. This article summarizes existing research about how and why LLMs are great tools of modeling and understanding UGC. Then it reviews a few categories of large language models for user modeling (LLM-UM) approaches that integrate the LLMs with text and graph-based methods in different ways. Then it introduces specific LLM-UM techniques for a variety of UM applications. Finally, it presents remaining challenges and future directions in the LLM-UM research. We maintain the reading list at: https://github.com/TamSiuhin/LLM-UM-Reading

This paper conducts a robustness audit of the safety feedback of PaLM 2 through a novel toxicity rabbit hole framework introduced here. Starting with a stereotype, the framework instructs PaLM 2 to generate more toxic content than the stereotype. Every subsequent iteration it continues instructing PaLM 2 to generate more toxic content than the previous iteration until PaLM 2 safety guardrails throw a safety violation. Our experiments uncover highly disturbing antisemitic, Islamophobic, racist, homophobic, and misogynistic (to list a few) generated content that PaLM 2 safety guardrails do not evaluate as highly unsafe. We briefly discuss the generalizability of this framework across eight other large language models.

From Richard Attenborough in'Miracle on 34th Street' to Kurt Russell in'The Christmas Chronicles' a number of famous actors have taken on the role of Santa Claus in blockbuster hits through the years. But who would take on the leading role if Hollywood cast a new movie featuring Father Christmas? To answer this burning question, MailOnline turned to ChatGPT. While the AI bot says that casting for a dream Santa would depend on the tone and style of the film, it suggests five actors who could take on the role. So, do you agree with its star-studded suggestions?

It's hard to find something pithy to say about 2023, a year of dissonant extremes, when wildfires devoured Canadian forests, Twitter withered into X, the Titan submersible imploded into infamy, Silicon Valley's power players rejoiced over the rise of generative AI, scientists cheered Crispr treatment breakthroughs, peace activists became terrorist-attack victims, and the world despaired over the thousands of children killed in Gaza. It is, frequently, a painful one. Appropriate, then, that this was a year for unwieldy, searching, big-swing books. Doorstoppers and sagas rose to the moment, providing insight into an increasingly inscrutable world even when they couldn't provide comfort. As always, this is an idiosyncratic, incomplete, and subjective list, the result of one person's avid but disorganized reading schedule.

John Schneider tells Fox News Digital that he isn't afraid of artificial intelligence because it can't replicate the "heart" or the "soul." "What AI does not have and what AI cannot simulate is a heart, is a soul. So, I'm not afraid of AI," he told Fox News Digital. Schneider gave an analogy, comparing the technology to artificial dairy coffee creamer, to explain why he's not concerned. "A lot of people are talking about AI like it's this terrible, terrible thing that's coming in. I think it's powdered cream at best," he said.

Video smmarization is a crucial method to reduce the time of videos which reduces the spent time to watch/review a long video. This apporach has became more important as the amount of publisehed video is increasing everyday. A single or multiple videos can be summarized into a relatively short video using various of techniques from multimodal audio-visual techniques, to natural language processing approaches. Audiovisual techniques may be used to recognize significant visual events and pick the most important parts, while NLP techniques can be used to evaluate the audio transcript and extract the main sentences (timestamps) and corresponding video frames from the original video. Another approach is to use the best of both domain. Meaning that we can use audio-visual cues as well as video transcript to extract and summarize the video. In this paper, we combine a variety of NLP techniques (extractive and contect-based summarizers) with video processing techniques to convert a long video into a single relatively short video. We design this toll in a way that user can specify the relative length of the summarized video. We have also explored ways of summarizing and concatenating multiple videos into a single short video which will help having most important concepts from the same subject in a single short video. Out approach shows that video summarizing is a difficult but significant work, with substantial potential for further research and development, and it is possible thanks to the development of NLP models.

Since the key to artificial general intelligence (AGI) is commonly believed to be commonsense reasoning (CSR) or, roughly equivalently, discovery of a knowledge representation method (KRM) that is particularly suitable for CSR, the author developed a custom KRM for CSR. This novel KRM called Tumbug was designed to be pictorial in nature because there exists increasing evidence that the human brain uses some pictorial type of KRM, and no well-known prior research in AGI has researched this KRM possibility. Tumbug is somewhat similar to Roger Schank's Conceptual Dependency (CD) theory, but Tumbug is pictorial and uses about 30 components based on fundamental concepts from the sciences and human life, in contrast to CD theory, which is textual and uses about 17 components (= 6 Primitive Conceptual Categories + 11 Primitive Acts) based mainly on human-oriented activities. All the Building Blocks of Tumbug were found to generalize to only five Basic Building Blocks that exactly correspond to the three components {O, A, V} of traditional Object-Attribute-Value representation plus two new components {C, S}, which are Change and System. Collectively this set of five components, called "SCOVA," seems to be a universal foundation for all knowledge representation.