A selection of the most important recent news, articles, and papers about AI.
General News, Articles, and Analyses
What is ‘model collapse’? An expert explains the rumours about an impending AI doom
Author: Aaron J. Snoswell
(Monday, August 19, 2024) “Generative AI needs tons of data to learn. It also generates new data. So, what happens when AI starts training on AI-made content?”
How A/B Testing and Multi-Model Hosting Accelerate Generative AI Feature Development in Amazon Q | AWS DevOps & Developer Productivity Blog
(Friday, August 23, 2024) “This blog post delves into the impact of A/B testing and Multi-Model hosting on deploying Generative AI features. By leveraging these powerful techniques, our team has been able to significantly accelerate the pace of experimentation, iteration, and deployment. We have not only streamlined our development process but also gained valuable insights into model performance, user preferences, and the potential impact of new features. This data-driven approach has allowed us to make informed decisions, continuously refine our models, and provide a user experience that resonates with our customers.”
5 Ways AI Will Change MMORPGs | MMORPG.com
https://www.mmorpg.com/editorials/5-ways-ai-will-change-mmorpgs-2000132550
(Friday, August 23, 2024) “There are numerous applications of AI technology for MMORPGs, from art, to programming, and even music and voiceovers. Here are 5 that might be closer than you think.”
AI agents will transform business processes — and magnify risks | CIO
“Chatbots sit and wait to be asked questions. Agents, however, are proactive, can act autonomously, and adapt to their environments. And when multiple agents develop into agentic frameworks, the potential power increases exponentially. But with strength in numbers and added complexity comes amplified risks, and the need for more fortified checks.”
Technical Papers, Articles, and Preprints
[2408.10726] Quantum Artificial Intelligence: A Brief Survey
https://arxiv.org/abs/2408.10726
Authors: Klusch, Matthias; Lässig, Jörg; Müssig, Daniel; Macaluso, Antonio; and Wilhelm, Frank K.
(Tuesday, August 20, 2024) “Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research. In particular, we summarize some major key findings on the feasability and the potential of using quantum computing for solving computationally hard problems in various subfields of AI, and vice versa, the leveraging of AI methods for building and operating quantum computing devices.”
[2408.14080] SONICS: Synthetic Or Not – Identifying Counterfeit Songs
https://arxiv.org/abs/2408.14080
(Monday, August 26, 2024) “The recent surge in AI-generated songs presents exciting possibilities and challenges. While these tools democratize music creation, they also necessitate the ability to distinguish between human-composed and AI-generated songs for safeguarding artistic integrity and content curation. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, this approach is inadequate for contemporary end-to-end AI-generated songs where all components (vocals, lyrics, music, and style) could be AI-generated. Additionally, existing datasets lack lyrics-music diversity, long-duration songs, and open fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect overlooked in existing methods. To capture these patterns, we propose a novel model, SpecTTTra, that is up to 3 times faster and 6 times more memory efficient compared to popular CNN and Transformer-based models while maintaining competitive performance. Finally, we offer both AI-based and Human evaluation benchmarks, addressing another deficiency in current research.”
