Sora, an artificial intelligence (AI) video generator program created by startup OpenAI in 2021, is making waves as it has now moved out of the research phase and has been officially released to the public under the new name of Sora Turbo. The launch has triggered an online frenzy among users, causing the company to temporarily halt new account creations after finding itself overwhelmed by an avalanche of traffic. Sora uses text prompts to create content, similar to other content creation programs such as ChatGPT. Unlike traditional AI programs which produce written responses, Sora creates high-quality videos based on a user's text input. Adding it to @everartai asap so you can bring your images to life pic.twitter.com/wMehxOc8cm

Young cast member Nora has benefited from this opportunity. She openly thanks those in game for giving her the opportunity to act and express herself freely, particularly as someone going through a gender transition. "It's amazing that her first production experience of Shakespeare, beyond studying in school, was in Grand Theft Auto," Grylls says. "That's what kept us going really, the fact people kept coming back because they wanted to." Grylls, Crane and Oosterveen's committed madness has paid off.

For more than two years, every new AI announcement has lived in the shadow of ChatGPT. No model from any company has eclipsed or matched that initial fever. But perhaps the closest any firm has come to replicating the buzz was this past February, when OpenAI first teased its video-generating AI model, Sora. Tantalizing clips--woolly mammoths kicking up clouds of snow, Pixar-esque animations of adorable fluffy critters--promised a stunning future, one in which anyone can whip up high-quality clips by typing simple text prompts into a computer program. But Sora, which was not immediately available to the public, remained just that: a teaser.

We leverage a natural app-update event at Replika AI, a popular US-based AI companion, to shed light on these questions. We find that, after the app removed its erotic role play (ERP) feature, preventing intimate interactions between consumers and chatbots that were previously possible, this event triggered perceptions in customers that their AI companion's identity had discontinued. This in turn predicted negative consumer welfare and marketing outcomes related to loss, including mourning the loss, and devaluing the'new' AI relative to the'original'. Experimental evidence confirms these findings. Further experiments find that AI companions users feel closer to their AI companion than even their best human friend, and mourn a loss of their AI companion more than a loss of various other inanimate products. In short, consumers are forming human-level relationships with AI companions; disruptions to these relationships trigger real patterns of mourning as well as devaluation of the offering; and the degree of mourning and devaluation are explained by perceived discontinuity in the AIs identity. Our results illustrate that relationships with AI are truly personal, creating unique benefits and risks for consumers and firms alike. The development of large language models (LLMs) and generative artificial intelligence (AI) has not only led to many new business applications (e.g., search, education software), but also enabled a new class of chatbots that has the potential to be used for building'synthetic' social relationships, which we refer to as AI companions. An increasing number of consumers use this technology to satisfy social goals (Broadbent et al. 2023; Chaturvedi et al. 2023; De Freitas et al. 2023).

Chord progressions encapsulate important information about music, pertaining to its structure and conveyed emotions. They serve as the backbone of musical composition, and in many cases, they are the sole information required for a musician to play along and follow the music. Despite their importance, chord progressions as a data domain remain underexplored. There is a lack of large-scale datasets suitable for deep learning applications, and limited research exploring chord progressions as an input modality. In this work, we present Chordonomicon, a dataset of over 666,000 songs and their chord progressions, annotated with structural parts, genre, and release date - created by scraping various sources of user-generated progressions and associated metadata. We demonstrate the practical utility of the Chordonomicon dataset for classification and generation tasks, and discuss its potential to provide valuable insights to the research community. Chord progressions are unique in their ability to be represented in multiple formats (e.g. text, graph) and the wealth of information chords convey in given contexts, such as their harmonic function . These characteristics make the Chordonomicon an ideal testbed for exploring advanced machine learning techniques, including transformers, graph machine learning, and hybrid systems that combine knowledge representation and machine learning.

Estimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users. However, prior approaches to uncertainty estimation focus on the final answer in generated text, ignoring intermediate steps that might dramatically impact the outcome. We hypothesize that there exist key forking tokens, such that re-sampling the system at those specific tokens, but not others, leads to very different outcomes. To test this empirically, we develop a novel approach to representing uncertainty dynamics across individual tokens of text generation, and applying statistical models to test our hypothesis. Our approach is highly flexible: it can be applied to any dataset and any LLM, without fine tuning or accessing model weights. We use our method to analyze LLM responses on 7 different tasks across 4 domains, spanning a wide range of typical use cases. We find many examples of forking tokens, including surprising ones such as punctuation marks, suggesting that LLMs are often just a single token away from saying something very different.

In this paper we introduce the Frechet Music Distance (FMD), a novel evaluation metric for generative symbolic music models, inspired by the Frechet Inception Distance (FID) in computer vision and Frechet Audio Distance (FAD) in generative audio. FMD calculates the distance between distributions of reference and generated symbolic music embeddings, capturing abstract musical features. We validate FMD across several datasets and models. Results indicate that FMD effectively differentiates model quality, providing a domain-specific metric for evaluating symbolic music generation, and establishing a reproducible standard for future research in symbolic music modeling.

This article presents the Quasar system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. The system adopts a RAG-based architecture, with a pipeline of evidence retrieval followed by answer generation, with the latter powered by a moderate-sized language model. Additionally and uniquely, Quasar has components for question understanding, to derive crisper input for evidence retrieval, and for re-ranking and filtering the retrieved evidence before feeding the most informative pieces into the answer generation. Experiments with three different benchmarks demonstrate the high answering quality of our approach, being on par with or better than large GPT models, while keeping the computational cost and energy consumption orders of magnitude lower.

In this paper, we present HalluCana, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation. HalluCana detects and intervenes as soon as traces of hallucination emerge, during and even before generation. To support timely detection, we exploit the internal factuality representation in the LLM hidden space, where we investigate various proxies to the LLMs' factuality self-assessment, and discuss its relation to the models' context familiarity from their pre-training. On biography generation, our method improves generation quality by up to 2.5x, while consuming over 6 times less compute.

The identity of a speaker significantly influences spoken language comprehension by affecting both perception and expectation. This review explores speaker effects, focusing on how speaker information impacts language processing. We propose an integrative model featuring the interplay between bottom-up perception-based processes driven by acoustic details and top-down expectation-based processes driven by a speaker model. The acoustic details influence lower-level perception, while the speaker model modulates both lower-level and higher-level processes such as meaning interpretation and pragmatic inferences. We define speaker-idiosyncrasy and speaker-demographics effects and demonstrate how bottom-up and top-down processes interact at various levels in different scenarios. This framework contributes to psycholinguistic theory by offering a comprehensive account of how speaker information interacts with linguistic content to shape message construction. We suggest that speaker effects can serve as indices of a language learner's proficiency and an individual's characteristics of social cognition. We encourage future research to extend these findings to AI speakers, probing the universality of speaker effects across humans and artificial agents.