The Associated Press is licensing a part its archive of news stories to OpenAI under a deal that will explore generative AI's use in news, the companies said Thursday, a move that could set the precedent for similar partnerships between the industries. The news publisher will gain access to OpenAI's technology and product expertise as part of the deal, the financial details of which were not disclosed. AP also did not reveal how it would integrate OpenAI's technology in its news operations. The publisher already uses AI for automating corporate earnings reports, recapping sporting events and transcription for certain live events. This could be due to a conflict with your ad-blocking or security software.

Subjective time-series regression (STR) tasks have gained increasing attention recently. However, most existing methods overlook the label distribution bias in STR data, which results in biased models. Emerging studies on imbalanced regression tasks, such as age estimation and depth estimation, hypothesize that the prior label distribution of the dataset is uniform. However, we observe that the label distributions of training and test sets in STR tasks are likely to be neither uniform nor identical. This distinct feature calls for new approaches that estimate more reasonable distributions to train a fair model. In this work, we propose Utopia Label Distribution Approximation (ULDA) for time-series data, which makes the training label distribution closer to real-world but unknown (utopia) label distribution. This would enhance the model's fairness. Specifically, ULDA first convolves the training label distribution by a Gaussian kernel. After convolution, the required sample quantity at each regression label may change. We further devise the Time-slice Normal Sampling (TNS) to generate new samples when the required sample quantity is greater than the initial sample quantity, and the Convolutional Weighted Loss (CWL) to lower the sample weight when the required sample quantity is less than the initial quantity. These two modules not only assist the model training on the approximated utopia label distribution, but also maintain the sample continuity in temporal context space. To the best of our knowledge, ULDA is the first method to address the label distribution bias in time-series data. Extensive experiments demonstrate that ULDA lifts the state-of-the-art performance on two STR tasks and three benchmark datasets.

Online news outlets are grappling with the moderation of user-generated content within their comment section. We present a recommender system based on ranking class probabilities to support and empower the moderator in choosing featured posts, a time-consuming task. By combining user and textual content features we obtain an optimal classification F1-score of 0.44 on the test set. Furthermore, we observe an optimum mean NDCG@5 of 0.87 on a large set of validation articles. As an expert evaluation, content moderators assessed the output of a random selection of articles by choosing comments to feature based on the recommendations, which resulted in a NDCG score of 0.83. We conclude that first, adding text features yields the best score and second, while choosing featured content remains somewhat subjective, content moderators found suitable comments in all but one evaluated recommendations. We end the paper by analyzing our best-performing model, a step towards transparency and explainability in hybrid content moderation.

Visual metaphors are powerful rhetorical devices used to persuade or communicate creative ideas through images. Similar to linguistic metaphors, they convey meaning implicitly through symbolism and juxtaposition of the symbols. We propose a new task of generating visual metaphors from linguistic metaphors. This is a challenging task for diffusion-based text-to-image models, such as DALL$\cdot$E 2, since it requires the ability to model implicit meaning and compositionality. We propose to solve the task through the collaboration between Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3 (davinci-002) with Chain-of-Thought prompting generates text that represents a visual elaboration of the linguistic metaphor containing the implicit meaning and relevant objects, which is then used as input to the diffusion-based text-to-image models.Using a human-AI collaboration framework, where humans interact both with the LLM and the top-performing diffusion model, we create a high-quality dataset containing 6,476 visual metaphors for 1,540 linguistic metaphors and their associated visual elaborations. Evaluation by professional illustrators shows the promise of LLM-Diffusion Model collaboration for this task . To evaluate the utility of our Human-AI collaboration framework and the quality of our dataset, we perform both an intrinsic human-based evaluation and an extrinsic evaluation using visual entailment as a downstream task.

We introduce Composer's Assistant, a system for interactive human-computer composition in the REAPER digital audio workstation. We consider the task of multi-track MIDI infilling when arbitrary track-measures have been deleted from a contiguous slice of measures from a MIDI file, and we train a T5-like model to accomplish this task. Composer's Assistant consists of this model together with scripts that enable interaction with the model in REAPER. We conduct objective and subjective tests of our model. We release our complete system, consisting of source code, pretrained models, and REAPER scripts. Our models were trained only on permissively-licensed MIDI files.

More than a quarter of UK adults have used generative artificial intelligence such as chatbots, according to survey showing that 4 million people have also used it for work. Generative AI, which refers to AI tools that produce convincing text or images in response to human prompts, has gripped the public imagination since the launch of ChatGPT in November. The rate of adoption of the latest generation of AI systems exceeds that of voice-assisted speakers such as Amazon's Alexa, according to accounting group Deloitte, which published the survey. Deloitte said 26% of 16- to 75-year-olds have used a generative AI tool, representing about 13 million people, with one in 10 of those respondents using it at least once a day. "It took five years for voice-assisted speakers to achieve the same adoption levels. It is incredibly rare for any emerging technology to achieve these levels of adoption and frequency of usage so rapidly," said Paul Lee, a Deloitte partner.

How much does Sound of Freedom get right about child sex trafficking? On the whole, it's pretty misleading about the nature and root causes of the problem, Molly Olmstead writes. In addition to what the movie gets wrong, she explains the religious tradition it taps into and the controversies that surround the man at the film's center. Plus: What Next examines how Sound of Freedom wove QAnon conspiracy theories into box office gold. Sam Adams reviews the whole moviegoing experience.

This spring, Congress hosted OpenAI's chief executive Sam Altman for a hearing, in which he admitted the technology could be a sousce of errors. He called for regulations to be crafted for the emerging industry and recommended that a new agency be formed to tackle it. He said he expected the technology to have a significant impact as its uses become clear, including on jobs.

Surfers at California's San Onofre Beach were seen surrounded by great white sharks while out enjoying the surf. Recently published footage from one of California's most popular surf beaches shows at least four sharks swimming beneath the waters as surfers nonchalantly chase waves. Photographer Kevin Christopherson captured the group of aquatic predators via drone camera over San Onofre State Beach in San Diego County, California. Surfers in the drone camera footage seem unaware of, or unconcerned about, the group of sharks just beneath their boards. "I counted 4 maybe 5 total great white sharks, and it didn't stop anyone from catching some waves!"

'Outnumbered' panelists discuss the vice president's approval after her latest comments on transportation and artificial intelligence. Fox News contributor Marc Thiessen argued Vice President Kamala Harris' abysmal approval is due, in part, to how she speaks to the American public. On "Outnumbered" Thursday, Thiessen responded to a montage of Harris' confusing statements by arguing that the VP "has a habit" of speaking as if she's talking to children. MARC THIESSEN: Why is she so unpopular? One may be that none of the issues she's taken on, like the border or anything else, she hasn't made any progress and so part of that is the failure of the administration.