Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant attention from society. As a result, many individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC), which involves the creation of digital content, such as images, music, and natural language, through AI models. The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace. AIGC is achieved by extracting and understanding intent information from instructions provided by human, and generating the content according to its knowledge and the intent information. In recent years, large-scale models have become increasingly important in AIGC as they provide better intent extraction and thus, improved generation results. With the growth of data and the size of the models, the distribution that the model can learn becomes more comprehensive and closer to reality, leading to more realistic and high-quality content generation. This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimodal interaction and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, we discuss the existing open problems and future challenges in AIGC.

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM)(BigScience Workshop, 2022) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

Even with strong sequence models like Transformers, generating expressive piano performances with long-range musical structures remains challenging. Meanwhile, methods to compose well-structured melodies or lead sheets (melody + chords), i.e., simpler forms of music, gained more success. Observing the above, we devise a two-stage Transformer-based framework that Composes a lead sheet first, and then Embellishes it with accompaniment and expressive touches. Such a factorization also enables pretraining on non-piano data. Our objective and subjective experiments show that Compose & Embellish shrinks the gap in structureness between a current state of the art and real performances by half, and improves other musical aspects such as richness and coherence as well.

They actually take his arguments seriously. If I wanted to blow my life savings on some wretched crypto scam I'd certainly listen to these guys about what the best scam to fall for was. This is what it looks like when the great hero of humanity, who has always been remarkably genre-savvy, realises that the movie he's in is'Lovecraft-style Existential Cosmic Horror', rather than'Rationalist Harry Potter Fanfic'. All power to Eliezer for having had a go. What sort of fool gives up before he's actually lost?

He spent about six hours at the property, which was the scene of a quadruple homicide in November. As the University of Idaho community reels from the shocking slayings of four undergrad students in an off-campus rental home in Moscow, Idaho, this past November, school officials have already announced plans to tear the building down. "The owner of the King Street house offered to give the house to the university, which we accepted," University of Idaho President Scott Green said last week. "The house will be demolished. This is a healing step and removes the physical structure where the crime that shook our community was committed."

This can be through the face ID of your phone, the last google search you did, or the movie that you chose to watch last night. AI is a huge trend currently. This is why I decided to understand how it works. And I don't just mean reading about it. I decided to program an AI algorithm (for some context, I barely know code).

The music industry has become more popular, and how people listen to music is changing like wildfire. The development of music streaming services has increased the demand for automatic music categorization and recommendation systems. Spotify, one of the world's leading music streaming sites, has millions of subscribers and a massive song catalog. Yet, for customers to have a personalized music experience, Spotify must recommend tracks that fit their preferences. Spotify uses machine learning algorithms to guide and categorizes music based on the Genre.

Artificial intelligence happens to be a popular theme of science fiction movies highlighting its benefits and dangers, including the possibility of machines taking over the world and the human race. The 2014-film'Ex Machina' is also about a programmer becoming the human component in a test to determine the capabilities and consciousness of Ava--a robot. The movie ends with Ava walking away, leaving her creator dead and the programmer trapped in the facility. AI Ex Machina was fiction. However, the emergence and subsequent popularity of AI chatbot ChatGPT has shown that the day is not far when thousands of jobs related to research, coding, writing, human resources, etc, may become redundant.

In this Machine Learning tutorial we will learn about How to Handle Fake News with Machine Learning. In today's fast-paced digital world, spreading fake news has become a significant concern. With the increasing ease of access to social media platforms and other online sources of information, it has become more challenging to distinguish between real and fake news. In this project-based article, we will learn how to build a machine-learning model to detect fake news accurately. This article was published as a part of the Data Science Blogathon.

A little girl asks when she'll be able to bury her father. The preacher, named David, says it'll have to wait till spring. David speaks to another follower named James outside, played by Troy Baker, who portrayed Joel Miller in the video games. David may seem like a run-of-the-mill preacher, but his interaction with James suggests an insidious side. When David asks James if he's still "with me," Baker's James barely nods his head, letting out a weak "yeah."