Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the specific groups that will be impacted by these technologies. Among the minority groups under-represented in AI, data from low-income households are often overlooked in data collection and model evaluation. We evaluate the performance of a state-of-the-art vision-language model (CLIP) on a geo-diverse dataset containing household images associated with different income values (Dollar Street) and show that performance inequality exists among households of different income levels. Our results indicate that performance for the poorer groups is consistently lower than the wealthier groups across various topics and countries. We highlight insights that can help mitigate these issues and propose actionable steps for economic-level inclusive AI development. Code is available at https://github.com/MichiganNLP/Bridging_the_Digital_Divide.

Providing dialogue agents with a profile representation can improve their consistency and coherence, leading to better conversations. However, current profile-based dialogue datasets for training such agents contain either explicit profile representations that are simple and dialogue-specific, or implicit representations that are difficult to collect. In this work, we propose a unified framework in which we bring together both standard and more sophisticated profile representations by creating a new resource where each dialogue is aligned with all possible speaker representations such as communication style, biographies, and personality. This framework allows to test several baselines built using generative language models with several profile configurations. The automatic evaluation shows that profile-based models have better generalisation capabilities than models trained on dialogues only, both in-domain and cross-domain settings. These results are consistent for fine-tuned models and instruction-based LLMs. Additionally, human evaluation demonstrates a clear preference for generations consistent with both profile and context. Finally, to account for possible privacy concerns, all experiments are done under two configurations: inter-character and intra-character. In the former, the LM stores the information about the character in its internal representation, while in the latter, the LM does not retain any personal information but uses it only at inference time.

Automatic modulation classification (AMC) plays a critical role in wireless communications by autonomously classifying signals transmitted over the radio spectrum. Deep learning (DL) techniques are increasingly being used for AMC due to their ability to extract complex wireless signal features. However, DL models are computationally intensive and incur high inference latencies. This paper proposes the application of early exiting (EE) techniques for DL models used for AMC to accelerate inference. We present and analyze four early exiting architectures and a customized multi-branch training algorithm for this problem. Through extensive experimentation, we show that signals with moderate to high signal-to-noise ratios (SNRs) are easier to classify, do not require deep architectures, and can therefore leverage the proposed EE architectures. Our experimental results demonstrate that EE techniques can significantly reduce the inference speed of deep neural networks without sacrificing classification accuracy. We also thoroughly study the trade-off between classification accuracy and inference time when using these architectures. To the best of our knowledge, this work represents the first attempt to apply early exiting methods to AMC, providing a foundation for future research in this area.

SAG-AFTRA, the union representing Hollywood performers, has reportedly responded to studios' "last, best and final" offer to end the strike, rejecting clauses letting studios re-use AI-created likenesses of high-demand and deceased performers without consent from their estate or families. "They can't have that loophole to exploit performers," a union-side source told The Hollywood Reporter. "We could not allow that language to stand." Reportedly, the Alliance of Motion Picture and Television Producers (AMPTP) would "secure AI scans" for Schedule F performers -- union members earning more than $32,000 per TV episode or $60,000 per film. Studios would pay once to scan the likenesses of these performers without paying for their use or re-use -- essentially giving them eternal rights to their face after paying once upfront.

The Legend of Zelda, one of the most successful and beloved gaming franchises of all time, is being made into a live-action film – and with such iconic characters as Link, Princess Zelda, the demonic Ganon and that one superhot half-fish prince everyone was in love earlier in the year, it's no wonder that the internet has absolutely exploded with people suggesting which actors should play them. So, we here at the Guardian thought we would put together our own dream cast for the upcoming flick. Tom Holland trends as fans discuss the casting of Link in upcoming live-action'Legend of Zelda' movie. However, given the wild success of the two most recent iterations of the series, Breath of the Wild and Tears of the Kingdom, it seems likely that that film will borrow heavily from their roster of characters. Although there are actually four great fairies in BotW and TotK, an amalgamation of these powerful and delightfully camp guardians would make a great cameo when our hero, Link, is in need of healing and magical help.

Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim. In this paper we introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each claim is annotated with question-answer pairs supported by evidence available online, as well as textual justifications explaining how the evidence combines to produce a verdict. Through a multi-round annotation process, we avoid common pitfalls including context dependence, evidence insufficiency, and temporal leakage, and reach a substantial inter-annotator agreement of $\kappa=0.619$ on verdicts. We develop a baseline as well as an evaluation scheme for verifying claims through several question-answering steps against the open web.

Factorization Machine (FM) is the most commonly used model to build a recommendation system since it can incorporate side information to improve performance. However, producing item suggestions for a given user with a trained FM is time-consuming. It requires a run-time of $O((N_m \log N_m)^2)$, where $N_m$ is the number of items in the dataset. To address this problem, we propose a quadratic unconstrained binary optimization (QUBO) scheme to combine with FM and apply quantum annealing (QA) computation. Compared to classical methods, this hybrid algorithm provides a faster than quadratic speedup in finding good user suggestions. We then demonstrate the aforementioned computational advantage on current NISQ hardware by experimenting with a real example on a D-Wave annealer.

A live-action film based on the hit game franchise The Legend of Zelda is in development, gaming giant Nintendo confirmed on Wednesday. The film will be directed by Wes Ball, who directed The Maze Runner series and the upcoming Kingdom of the Planet of the Apes. Shigeru Miyamoto, Legend of Zelda creator and representative director at Nintendo, will produce the film with Avi Arad, producer of films including the Oscar-winning Spider-Man: Into the Spiderverse. "I have been working on the live-action film of The Legend of Zelda for many years now with Avi Arad-san, who has produced many mega hit films," Miyamoto said in a statement on X, formerly Twitter. "It will take time until its completion, but I hope you look forward to seeing it."

SAG-AFTRA, the union representing Hollywood performers, has reportedly responded to studios' "last, best and final" offer to end the strike, rejecting clauses that would let them re-use AI-created likenesses of high-demand and deceased performers without consent. The union allegedly plans to make a counter-offer that removes the current AI-related language. "They can't have that loophole to exploit performers," a union-side source told The Hollywood Reporter on Monday. "We could not allow that language to stand." THR reports that The Alliance of Motion Picture and Television Producers (AMPTP) proposed to "secure AI scans" for Schedule F performers (union members earning more than $32,000 per TV episode or $60,000 per film).

SAG-AFTRA, the union representing Hollywood performers, has reportedly responded to studios' "last, best and final" offer to end the strike, rejecting clauses that would let them re-use AI-created likenesses of high-demand and deceased performers without consent. The union allegedly plans to make a counter-offer that removes the current AI-related language. "They can't have that loophole to exploit performers," a union-side source told The Hollywood Reporter on Monday. "We could not allow that language to stand." THR reports that The Alliance of Motion Picture and Television Producers (AMPTP) proposed to "secure AI scans" for Schedule F performers (union members earning more than $32,000 per TV episode or $60,000 per film).