To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.

Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.

DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time. In this paper, we address these limitations by utilizing a two-stage optimization framework. First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure. Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model. Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2x faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution. User studies show 61.7% raters to prefer our approach over DreamFusion. Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications.

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia. Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This repository can be updated periodically with contributions from other researchers. Our objective is to identify research gaps and disseminate the information to NLP researchers interested in Ethiopian languages and encourage future research in this domain.

We have a very special guest on today's episode. We talk with Kirk Borne, a top AI influencer since 2013. From his LinkedIn Bio, Kirk is the Founder of the Data Leadership Group (Data Scientist. Consultant) and Advisor to DataPrime Inc., but in the episode, you will see his background exceeds much more than this! Generative AI is making a splash across the news with ChatGPT and other large language models.

Newswire) Investorideas.com, a global investor news source covering Artificial Intelligence (AI) brings you today's edition of The AI Eye - watching stock news, deal tracker and advancements in artificial intelligence. FiscalNote Holdings, Inc. (NYSE:NOTE) has announced it "has been selected as one of 14 inaugural "trusted partners" - and the sole provider of legal, political, and regulatory data and information - to collaborate with AI research and deployment company OpenAI by enabling access to select FiscalNote market leading real-time data sets and content for users of OpenAI's ChatGPT platform." FiscalNote's Chairman, CEO, and Co-founder, Tim Hwang, said: "Since we founded FiscalNote a decade ago, the company has been an early adopter and pioneer of AI, uniquely applying it to the political and legal domain, and building a specialized expertise that has made us the unparalleled leader in this space. We're excited to collaborate with OpenAI and, as the market leader in legal and regulatory intelligence, we intend to continue to always be at the forefront as technological capabilities continue to advance. We believe this is the beginning of an innovative collaboration with a fellow AI pioneer, and we intend to continue to push the bounds of what is possible as we use this cutting-edge technology to deliver results for our global customers and advance their business objectives."

On Monday, as Americans considered the possibility of a Donald Trump indictment and a presidential perp walk, Eliot Higgins brought the hypothetical to life. Higgins, the founder of Bellingcat, an open-source investigations group, asked the latest version of the generative-AI art tool Midjourney to illustrate the spectacle of a Trump arrest. It pumped out vivid photos of a sea of police officers dragging the 45th president to the ground. He generated a series of images that became more and more absurd: Donald Trump Jr. and Melania Trump screaming at a throng of arresting officers; Trump weeping in the courtroom, pumping iron with his fellow prisoners, mopping a jailhouse latrine, and eventually breaking out of prison through a sewer on a rainy evening. The story, which Higgins tweeted over the course of two days, ends with Trump crying at a McDonald's in his orange jumpsuit. All of the tweets are compelling, but only the scene of Trump's arrest went mega viral, garnering 5.7 million views as of this morning.

Hosted by the Alan Turing Institute, AI UK is a two day conference that showcases artificial intelligence and data science research, development, and policy in the UK. This year, the event took place on 21 and 22 March, and the theme was the use of data science and AI to solve real-world challenges. Given AIhub's mission to connect the AI community to the general public, and to report the breadth of AI research without the hype, the panel session on science journalism particularly piqued my interest. Chaired by science journalist Anjana Ahuja, "Impacting technology through quality science journalism" drew on the opinions of Research Fellow Mhairi Aitken, science reporter Melissa Heikkilä, and writer, presenter and comedian Timandra Harkness. The speakers talked about the role that journalism has to play in understanding AI systems and their implementation in wider society.

HBO's hit series The Last of Us is based on a popular video game from Naughty Dog. Science fiction author Zach Chapman appreciates that the show is a faithful adaptation of one of his favorite games. "The show is in many episodes a shot-for-shot remake of the game," Chapman says in Episode 539 of the Geek's Guide to the Galaxy podcast. "The script is almost exactly the same, you just don't get the gameplay." The Last of Us has a reputation as one of the best video game stories ever told.

The rise of AI programs like ChatGPT has triggered a tidal wave of ethical handwringing, most prominently from within the industries that it threatens to destroy. After all, just because you can get a robot to instantly write code or write contracts or provide customer support for free, should you? Well, the answer from the Writers Guild of America is a qualified yes. This week, the Writers Guild of America proposed that ChatGPT would absolutely be allowed to write scripts in the future, provided that the credit (and the money) goes to the human writer who came up with the prompts in the first place. The proposal paints a scary picture of the future; a future in which even the most human of arts are crushed under the wheels of an unthinking technology.