Nearly seven months after it began publishing machine-generated stories without disclosing their true authorship (or lack thereof) to readers, CNET has finally, publicly changed its policy on the use of AI in its journalistic endeavors. In short, stories written by its in-house artificial intelligence -- which it calls Responsible AI Machine Partner (RAMP) -- are no more, but the specter of AI in its newsroom is far from exorcised. The site indicates, however, that there are still two broad categories of pursuits where RAMP will be deployed. The first, which it calls "Organizing large amounts of information" provides an example that seems more authorial than that umbrella descriptor lets on. "RAMP will help us sort things like pricing and availability data and present it in ways that tailor information to certain audiences. Without an AI assist, this volume of work wouldn't be possible."

When I first caught students attempting to use ChatGPT to write their essays, it felt like an inevitability. My initial reaction was frustration and irritation--not to mention gloom and doom about the slow collapse of higher education--and I suspect most educators feel the same way. But as I thought about how to respond, I realized there could be a teaching opportunity. Many of these essays used sources incorrectly, either quoting from books that did not exist or misrepresenting those that did. When students were starting to use ChatGPT, they seemed to have no idea that it could be wrong.

Michael Sayman has worked at Facebook, Google, Roblox, and Twitter. At 26, the software engineer has already published a memoir, App Kid. But until he began work on his latest project, he'd never built a website. "I made it in five hours over the weekend, out of frustration that there wasn't anything like this," he says. Sayman's site is AI Hits.

Folgueira said Deezer also uses its tool to root out "fake artists" - pseudonymous musicians with no discernible online presence, who create hundreds of songs (typically overly-simple mood music) to game the royalty payment system. Spotify has been routinely accused of hosting such artists.

That was the first thing I heard from one excited WWDC attendee as I waited to test Apple's Vision Pro mixed reality headset. That level is excitement is exactly what Apple is hoping for. Realistically, not everyone will be able to afford a $3,499 device. But if Apple can get mainstream consumers excited about the idea of spatial computing, then it'll be able to make a bigger splash when it inevitably unveils a more affordable follow-up. After spending thirty minutes with the Vision Pro, my reaction is more tempered than that excitable attendee.

Self-training (ST) has prospered again in language understanding by augmenting the fine-tuning of pre-trained language models when labeled data is insufficient. However, it remains challenging to incorporate ST into attribute-controllable language generation. Augmented by only self-generated pseudo text, generation models over-emphasize exploitation of the previously learned space, suffering from a constrained generalization boundary. We revisit ST and propose a novel method, DuNST to alleviate this problem. DuNST jointly models text generation and classification with a shared Variational AutoEncoder and corrupts the generated pseudo text by two kinds of flexible noise to disturb the space. In this way, our model could construct and utilize both pseudo text from given labels and pseudo labels from available unlabeled text, which are gradually refined during the ST process. We theoretically demonstrate that DuNST can be regarded as enhancing exploration towards the potential real text space, providing a guarantee of improved performance. Experiments on three controllable generation tasks show that DuNST could significantly boost control accuracy while maintaining comparable generation fluency and diversity against several strong baselines.

The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other. Online platforms seek to moderate such content to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Researchers have developed different methods for automatically detecting harmful content, often focusing on specific sub-problems or on narrow communities, as what is considered harmful often depends on the platform and on the context. We argue that there is currently a dichotomy between what types of harmful content online platforms seek to curb, and what research efforts there are to automatically detect such content. We thus survey existing methods as well as content moderation policies by online platforms in this light and we suggest directions for future work.

This study introduces a novel approach to generate dance motions using onomatopoeia as input, with the aim of enhancing creativity and diversity in dance generation. Unlike text and music, onomatopoeia conveys rhythm and meaning through abstract word expressions without constraints on expression and without need for specialized knowledge. We adapt the AI Choreographer framework and employ the Sakamoto system, a feature extraction method for onomatopoeia focusing on phonemes and syllables. Additionally, we present a new dataset of 40 onomatopoeia-dance motion pairs collected through a user survey. Our results demonstrate that the proposed method enables more intuitive dance generation and can create dance motions using sound-symbolic words from a variety of languages, including those without onomatopoeia. This highlights the potential for diverse dance creation across different languages and cultures, accessible to a wider audience. Qualitative samples from our model can be found at: https://sites.google.com/view/onomatopoeia-dance/home/.

Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are limited by the circumscribed logical paradigms of training samples, which leads to a weak generalization of unseen logic. To address these issues, we propose a plug-in module called Logic Diffusion (LoD) to discover unseen queries from surroundings and achieves dynamical equilibrium between different kinds of patterns. The basic idea of LoD is relation diffusion and sampling sub-logic by random walking as well as a special training mechanism called gradient adaption. Besides, LoD is accompanied by a novel loss function to further achieve the robust logical diffusion when facing noisy data in training or testing sets. Extensive experiments on four public datasets demonstrate the superiority of mainstream knowledge graph reasoning models with LoD over state-of-the-art. Moreover, our ablation study proves the general effectiveness of LoD on the noise-rich knowledge graph.

Recently, compressive text summarisation offers a balance between the conciseness issue of extractive summarisation and the factual hallucination issue of abstractive summarisation. However, most existing compressive summarisation methods are supervised, relying on the expensive effort of creating a new training dataset with corresponding compressive summaries. In this paper, we propose an efficient and interpretable compressive summarisation method that utilises unsupervised dual-agent reinforcement learning to optimise a summary's semantic coverage and fluency by simulating human judgment on summarisation quality. Our model consists of an extractor agent and a compressor agent, and both agents have a multi-head attentional pointer-based structure. The extractor agent first chooses salient sentences from a document, and then the compressor agent compresses these extracted sentences by selecting salient words to form a summary without using reference summaries to compute the summary reward. To our best knowledge, this is the first work on unsupervised compressive summarisation. Experimental results on three widely used datasets (e.g., Newsroom, CNN/DM, and XSum) show that our model achieves promising performance and a significant improvement on Newsroom in terms of the ROUGE metric, as well as interpretability of semantic coverage of summarisation results.