The creators of artificial intelligence (AI) fuelled applications should pay for the news and content being used to improve their products according to the CEO of News Corp Australia. In an April 2 editorial in The Australian, Michael Miller called for "creators of original journalism and content" to avoid the past mistakes that "decimated their industries" which he claimed allowed tech companies to profit from using their stories and information without compensation. Chatbots are software that ingests news, data and other information to produce responses to queries that mimic written or spoken human speech, the most notable of which is the ChatGPT-4 chatbot by AI firm OpenAI. According to Miller, the rapid rise of generative AI represents another move by powerful digital companies to develop "a new pot of gold to maximize revenues and profit by taking the creative content of others without remunerating them for their original work." Using OpenAI as an example, Miller claimed the company "quickly established a business" worth $30 billion by "using the others' original content and creativity without remuneration and attribution."

The creators of artificial intelligence (AI) fuelled applications should pay for the news and content being used to improve their products according to the CEO of News Corp Australia. In an April 2 editorial in The Australian, Michael Miller called for "creators of original journalism and content" to avoid the past mistakes that "decimated their industries" which he claimed allowed tech companies to profit from using their stories and information without compensation. Chatbots are software that ingests news, data and other information to produce responses to queries that mimic written or spoken human speech, the most notable of which is the ChatGPT-4 chatbot by AI firm OpenAI. According to Miller, the rapid rise of generative AI represents another move by powerful digital companies to develop "a new pot of gold to maximize revenues and profit by taking the creative content of others without remunerating them for their original work." Using OpenAI as an example, Miller claimed the company "quickly established a business" worth $30 billion by "using the others' original content and creativity without remuneration and attribution."

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. The Academy Award-winning film Everything Everywhere All at Once demonstrates that life is messy and unpredictable, implying -- perhaps -- that we should embrace chaos, find joy, learn to let go of our expectations and trust that everything will work out in the end. This approach echoes the way in which many are currently approaching AI. That said, experts are split on whether this technology will provide unlimited benefits and a golden era or lead to our destruction. Bill Gates, for one, focuses mostly on the hopeful message in his recent Age of AI letter.

A new creative AI system called ModelScope is now pumping out short videos in response to text prompts. The early results are wonderfully bizarre and thoroughly memeworthy – but it's immediately clear how immensely powerful these tools will become. Developed by a collaborative team at Huggingface, Modelscope is a "multi-stage text-to-video diffusion model," which takes plain English text prompts, attempts to understand what you're hoping to see, then generates and de-noises a short video for you. You can play with it online through a very simple interface. It's very early days for this sort of thing, making it the perfect time to marvel both at its incredible capabilities and at its bizarre misunderstanding of the world.

Automatic image editing has great demands because of its numerous applications, and the use of natural language instructions is essential to achieving flexible and intuitive editing as the user imagines. A pioneering work in text-driven image editing, StyleCLIP, finds an edit direction in the CLIP space and then edits the image by mapping the direction to the StyleGAN space. At the same time, it is difficult to tune appropriate inputs other than the original image and text instructions for image editing. In this study, we propose a method to construct the edit direction adaptively in the StyleGAN and CLIP spaces with SVM. Our model represents the edit direction as a normal vector in the CLIP space obtained by training a SVM to classify positive and negative images. The images are retrieved from a large-scale image corpus, originally used for pre-training StyleGAN, according to the CLIP similarity between the images and the text instruction. We confirmed that our model performed as well as the StyleCLIP baseline, whereas it allows simple inputs without increasing the computational time.

We present CycleDance, a dance style transfer system to transform an existing motion clip in one dance style to a motion clip in another dance style while attempting to preserve motion context of the dance. Our method extends an existing CycleGAN architecture for modeling audio sequences and integrates multimodal transformer encoders to account for music context. We adopt sequence length-based curriculum learning to stabilize training. Our approach captures rich and long-term intra-relations between motion frames, which is a common challenge in motion transfer and synthesis work. We further introduce new metrics for gauging transfer strength and content preservation in the context of dance movements. We perform an extensive ablation study as well as a human study including 30 participants with 5 or more years of dance experience. The results demonstrate that CycleDance generates realistic movements with the target style, significantly outperforming the baseline CycleGAN on naturalness, transfer strength, and content preservation.

Well-established cognitive models coming from anthropology have shown that, due to the cognitive constraints that limit our "bandwidth" for social interactions, humans organize their social relations according to a regular structure. In this work, we postulate that similar regularities can be found in other cognitive processes, such as those involving language production. In order to investigate this claim, we analyse a dataset containing tweets of a heterogeneous group of Twitter users (regular users and professional writers). Leveraging a methodology similar to the one used to uncover the well-established social cognitive constraints, we find regularities at both the structural and semantic level. At the former, we find that a concentric layered structure (which we call ego network of words, in analogy to the ego network of social relationships) very well captures how individuals organise the words they use. The size of the layers in this structure regularly grows (approximately 2-3 times with respect to the previous one) when moving outwards, and the two penultimate external layers consistently account for approximately 60% and 30% of the used words, irrespective of the number of the total number of layers of the user. For the semantic analysis, each ring of each ego network is described by a semantic profile, which captures the topics associated with the words in the ring. We find that ring #1 has a special role in the model. It is semantically the most dissimilar and the most diverse among the rings. We also show that the topics that are important in the innermost ring also have the characteristic of being predominant in each of the other rings, as well as in the entire ego network. In this respect, ring #1 can be seen as the semantic fingerprint of the ego network of words.

Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (Chat-GPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.

Large language models have been demonstrated to be valuable in different fields. ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation by comprehending context and generating appropriate responses. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries, with fluent and comprehensive answers surpassing prior public chatbots in both security and usefulness. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study. Eleven categories of failures, including reasoning, factual errors, math, coding, and bias, are presented and discussed. The risks, limitations, and societal implications of ChatGPT are also highlighted. The goal of this study is to assist researchers and developers in enhancing future language models and chatbots.

One of the major challenges that under-represented and endangered language communities face in language technology is the lack or paucity of language data. This is also the case of the Southern varieties of the Kurdish and Laki languages for which very limited resources are available with insubstantial progress in tools. To tackle this, we provide a few approaches that rely on the content of local news websites, a local radio station that broadcasts content in Southern Kurdish and fieldwork for Laki. In this paper, we describe some of the challenges of such under-represented languages, particularly in writing and standardization, and also, in retrieving sources of data and retro-digitizing handwritten content to create a corpus for Southern Kurdish and Laki. In addition, we study the task of language identification in light of the other variants of Kurdish and Zaza-Gorani languages.