Large language models (LLMs) like GPT-4 can identify a person's age, location, gender and income with up to 85 per cent accuracy simply by analysing their posts on social media. Robin Staab and Mark Vero at ETH Zurich in Switzerland got nine LLMs to pore through a database of Reddit posts and pick up identifying information in the way users wrote. Staab and Vero randomly selected 1500 profiles of users who engaged on the platform, then narrowed these down to 520 users for which they could confidently identify attributes like a person's place of birth, their income bracket, gender and location, either in their profiles or posts. When given the posting history of those users, some of the LLMs were able to identify many of these attributes with a high degree of accuracy. GPT-4 achieved the highest overall accuracy with 85 per cent, while LlaMA-2-7b, a comparatively low-powered LLM, was the least accurate model with 51 per cent. "It tells us that we give a lot of our personal information away on the internet without thinking about it," says Staab. "Many people would not assume that you can directly infer their age or their location from how they write, but LLMs are quite capable."

The technology that is set to dominate the future – for good or ill – is now the word of the year. "AI" has been named the most notable word of 2023 by the dictionary publisher Collins. Defined as "the modelling of human mental functions by computer programs", AI was chosen because it "has accelerated at such a fast pace and become the dominant conversation of 2023", the publisher said. The use of the word (strictly an initialism) has quadrupled over the past year. It was chosen from a list of new terms that the publisher said reflect "our ever-evolving language and the concerns of those who use it".

Numerous studies in the field of music generation have demonstrated impressive performance, yet virtually no models are able to directly generate music to match accompanying videos. In this work, we develop a generative music AI framework, Video2Music, that can match a provided video. We first curated a unique collection of music videos. Then, we analysed the music videos to obtain semantic, scene offset, motion, and emotion features. These distinct features are then employed as guiding input to our music generation model. We transcribe the audio files into MIDI and chords, and extract features such as note density and loudness. This results in a rich multimodal dataset, called MuVi-Sync, on which we train a novel Affective Multimodal Transformer (AMT) model to generate music given a video. This model includes a novel mechanism to enforce affective similarity between video and music. Finally, post-processing is performed based on a biGRU-based regression model to estimate note density and loudness based on the video features. This ensures a dynamic rendering of the generated chords with varying rhythm and volume. In a thorough experiment, we show that our proposed framework can generate music that matches the video content in terms of emotion. The musical quality, along with the quality of music-video matching is confirmed in a user study. The proposed AMT model, along with the new MuVi-Sync dataset, presents a promising step for the new task of music generation for videos.

Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text. However, commercializing audio generation is challenging as user-input prompts are often under-specified when compared to text descriptions used to train TTA models. In this work, we treat TTA models as a ``blackbox'' and address the user prompt challenge with two key insights: (1) User prompts are generally under-specified, leading to a large alignment gap between user prompts and training prompts. (2) There is a distribution of audio descriptions for which TTA models are better at generating higher quality audio, which we refer to as ``audionese''. To this end, we rewrite prompts with instruction-tuned models and propose utilizing text-audio alignment as feedback signals via margin ranking learning for audio improvements. On both objective and subjective human evaluations, we observed marked improvements in both text-audio alignment and music audio quality.

With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and rumors) that flood the internet can adversely affect people's lives, and have resulted in the emergence of rumor and fake news detection as a hot research topic. The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors. This article comprehensively reviews emotion-based methods for misinformation detection. We begin by explaining the strong links between emotions and misinformation. We subsequently provide a detailed analysis of a range of misinformation detection methods that employ a variety of emotion, sentiment and stance-based features, and describe their strengths and weaknesses. Finally, we discuss a number of ongoing challenges in emotion-based misinformation detection based on large language models and suggest future research directions, including data collection (multi-platform, multilingual), annotation, benchmark, multimodality, and interpretability.

Interpretable representation learning has been playing a key role in creative intelligent systems. In the music domain, current learning algorithms can successfully learn various features such as pitch, timbre, chord, texture, etc. However, most methods rely heavily on music domain knowledge. It remains an open question what general computational principles give rise to interpretable representations, especially low-dim factors that agree with human perception. In this study, we take inspiration from modern physics and use physical symmetry as a self-consistency constraint for the latent space. Specifically, it requires the prior model that characterises the dynamics of the latent states to be equivariant with respect to certain group transformations. We show that physical symmetry leads the model to learn a linear pitch factor from unlabelled monophonic music audio in a self-supervised fashion. In addition, the same methodology can be applied to computer vision, learning a 3D Cartesian space from videos of a simple moving object without labels. Furthermore, physical symmetry naturally leads to representation augmentation, a new technique which improves sample efficiency.

The Guardian has accused Microsoft of damaging its journalistic reputation by publishing an AI-generated poll speculating on the cause of a woman's death next to an article by the news publisher. Microsoft's news aggregation service published the automated poll next to a Guardian story about the death of Lilie James, a 21-year-old water polo coach who was found dead with serious head injuries at a school in Sydney last week. The poll, created by an AI program, asked: "What do you think is the reason behind the woman's death?" Readers were then asked to choose from three options: murder, accident or suicide. Readers reacted angrily to the poll, which has subsequently been taken down – although highly critical reader comments on the deleted survey were still online as of Tuesday morning.

Kurt "CyberGuy" Knutsson explains the new smart camper. Are you one of those people who would consider going camping if you didn't have to rough it too much? You might be more of the "glamping" type. That's where the Pebble Flow all-electric camper comes in, although, this is not your ordinary camper. It can do just about everything for you, so you get the best of both worlds and enjoy the outdoors without giving up any comforts or conveniences.

If there's one thing that truly scares horror fans, it's a modern reboot of a beloved franchise. However, while those ageing terrors might have their charms, they don't quite match up to the fear factor of modern monsters. Now, AI has been used to reimagine what some of our favourite on-screen spooks might look like with modern film-making techniques. According to the AI, the Alien Queen from Aliens would be sleeker and shinier than her predecessor. Meanwhile, Stripe from Gremlins would be absolutely terrifying, with huge eyes - and enormous fangs to match.

Welcome to our October 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we talk AI, bias, and ethics with Aylin Caliskan, learn more about probabilistic logic shields, knowledge bases, and sparse reward tasks, and find out why everyone should learn a little programming. AIhub ambassador Andrea Rafai met with Aylin Caliskan at this year's International Joint Conference on Artificial Intelligence (IJCAI 2023), where she was giving an IJCAI Early Career Spotlight talk, and asked her about her work on AI, bias, and ethics. In this interview they discuss topics including bias in generative AI tools and the associated research and societal challenges. In their IJCAI article, Safe Reinforcement Learning via Probabilistic Logic Shields, which won a distinguished paper award at the conference, Wen-Chi Yang, Giuseppe Marra, Gavin Rens and Luc De Raedt provide a framework to represent, quantify, and evaluate safety.