The news-rating group NewsGuard has found dozens of news websites generated by AI chatbots proliferating online, according to a report published Monday, raising questions about how the technology may supercharge established fraud techniques. The 49 websites, which were independently reviewed by Bloomberg, run the gamut. Some are dressed up as breaking news sites with generic-sounding names like News Live 79 and Daily Business Post, while others share lifestyle tips, celebrity news or publish sponsored content. But none disclose they're populated using AI chatbots such as OpenAI's ChatGPT and potentially Alphabet's Google Bard, which can generate detailed text based on simple user prompts. Many of the websites began publishing this year as the AI tools began to be widely used by the public.

Orthodox Catholic philosopher Joe Vukov discusses the moral implications of using artificial intelligence to cope with the loss of loved ones. This year is shaping up to be the year of artificial intelligence. ChatGPT has stolen most of the headlines, but it is only the most infamous in a wide assortment AI platforms. One of the most recent to arrive on the scene is HereAfter AI, an app that can "preserve memories with an app that interviews you about your life." The goal: to "let loved ones hear meaningful stories by chatting with the virtual you."

Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years. However, music genres like electronic dance music (EDM) has always been relatively less investigated compared to others. Considering its wide range of applications, we present a Python package for automated EDM audio generation as an infrastructure for MIR for EDM songs, to mitigate the difficulty of acquiring labelled data. It is a convenient tool that could be easily concatenated to the end of many symbolic music generation pipelines. Inside this package, we provide a framework to build professional-level templates that could render a well-produced track from specified melody and chords, or produce massive tracks given only a specific key by our probabilistic symbolic melody generator. Experiments show that our mixes could achieve the same quality of the original reference songs produced by world-famous artists, with respect to both subjective and objective criteria. Our code is accessible in this repository: https://github.com/Gariscat/loopy and the official site of the project is also online https://loopy4edm.com .

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN, which dynamically analyses each user's preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music.

Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using weak labels that can be noisy and even incorrect. To address this limitation, our work makes the following contributions: (1) We present HQP: a novel dataset (N=30,000) for detecting online propaganda with high-quality labels. To the best of our knowledge, HQP is the first dataset for detecting online propaganda that was created through human annotation. (2) We show empirically that state-of-the-art language models fail in detecting online propaganda when trained with weak labels (AUC: 64.03). In contrast, state-of-the-art language models can accurately detect online propaganda when trained with our high-quality labels (AUC: 92.25), which is an improvement of ~44%. (3) To address the cost of labeling, we extend our work to few-shot learning. Specifically, we show that prompt-based learning using a small sample of high-quality labels can still achieve a reasonable performance (AUC: 80.27). Finally, we discuss implications for the NLP community to balance the cost and quality of labeling. Crucially, our work highlights the importance of high-quality labels for sensitive NLP tasks such as propaganda detection.

Left: an abstract depiction of an MDP where single-step RL fails to discover the optimal policy. Right: A notional illustrative example where we might expect full "multi-step" RL methods (such as ILQL) to perform significantly better than "single-step" methods. In this example, good utterances tend to start with "The movie was...", while bad utterances start with "The movie wasn't..." However, the very best examples also start with "The movie wasn't...", requiring multi-step planning or multiple steps of policy improvement to derive effective strategies. Methods that implement just a single step of policy improvement will fail to produce maximally positive sentiment outputs. While this example may appear somewhat contrived, we see in our experiments that multi-step RL methods do lead to improvements in a number of more real settings. Since ILQL performs multiple steps of policy improvement, it can significantly improve over Monte Carlo estimators or single-step RL when the underlying data is sub-optimal. One example corresponds to the notional task in Figure 4, in which the optimal sequence of actions requires traversing a state that's also frequented by sub-optimal examples. In this case, single-step RL will learn to take actions that appear safer according to the dataset -- such as the transition "The movie" "was" in Figure 4 -- whereas full ("multi-step") RL methods would recover the optimal policy. We demonstrate this empirically on the Wordle game below.

Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. BERT learns bidirectional encoder representations from Transformers, which are trained on large datasets as contextual language models. Similarly, the generative pretrained transformer (GPT) method employs Transformers as the feature extractor and is trained using an autoregressive paradigm on large datasets. Recently, ChatGPT shows promising success on large language models, which applies an autoregressive language model with zero shot or few shot prompting. The remarkable achievements of PFM have brought significant breakthroughs to various fields of AI. Numerous studies have proposed different methods, raising the demand for an updated survey. This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities. The review covers the basic components and existing pretraining methods used in natural language processing, computer vision, and graph learning. Additionally, it explores advanced PFMs used for different data modalities and unified PFMs that consider data quality and quantity. The review also discusses research related to the fundamentals of PFMs, such as model efficiency and compression, security, and privacy. Finally, the study provides key implications, future research directions, challenges, and open problems in the field of PFMs. Overall, this survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and the user-friendly interactive ability for artificial general intelligence.

Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions. In this paper, we introduce the concepts of relation path coverage and relation path confidence to filter out unreliable paths prior to model training to elevate the model performance. Moreover, we propose Knowledge Reasoning Sentence Transformer (KRST) to predict inductive relations in KGs. KRST is designed to encode the extracted reliable paths in KGs, allowing us to properly cluster paths and provide multi-aspect explanations. We conduct extensive experiments on three real-world datasets. The experimental results show that compared to SOTA models, KRST achieves the best performance in most transductive and inductive test cases (4 of 6), and in 11 of 12 few-shot test cases.

Can we imagine a scenario in which the different anxieties aroused by George Romero's horror film Night of the Living Dead and Stanley Kubrick's sci-fi dystopia 2001: A Space Odyssey merge? How might a monster that combined our fear of becoming something less than human with our fear of increasingly "intelligent" machines appear to us and what might it say? There is one work – of both horror and science fiction – that imagines such a monster. Published almost exactly 150 years before Romero and Kubrick released their movies, it is a book in which physical deformity and technological mutiny coalesce, creating a monster that is both a zombie and AI, or something in between the two. A gothic fiction, it is also described by some literary historians as the first science-fiction novel.

A couple of weeks ago I tweeted a call-out for freelance journalists to pitch me feature ideas for the science and sechnology section of the Observer's New Review. Unsurprisingly, given headlines, fears and interest in LLM (large language model) chatbots such as ChatGPT, many of the suggestions that flooded in focused on artificial intelligence – including a pitch about how it is being employed to predict deforestation in the Amazon. One submission however, from an engineering student who had posted a couple of articles on Medium, seemed to be riding the artificial intelligence wave with more chutzpah. He offered three feature ideas – pitches on innovative agriculture, data storage and the therapeutic potential of VR. While coherent, the pitches had a bland authority about them, repetitive paragraph structure, and featured upbeat endings, which if you've been toying with ChatGPT or reading about Google chatbot Bard's latest mishaps, are hints of chatbot-generated content.