We funded 10 teams from around the world to design ideas and tools to collectively govern AI. We summarize the innovations, outline our learnings, and call for researchers and engineers to join us as we continue this work.
AI voice generators are rapidly transforming the digital content landscape, offering highly realistic and versatile synthetic voices. With significant progress made in recent months, these innovative tools are empowering companies and individuals across various industries to create engaging and accessible content. From automated customer service to immersive entertainment experiences, the future of voice synthesis with AI promises even more advanced capabilities and applications. Advancement of AI-Generated Voices In recent years, AI voice synthesis has been playing a growing role in […]
The post The Future of Voice Synthesis with AI appeared first on AutoGPT Official.
Bad work meetings are rough, but here are nine easy ways you can use AI to make them better.
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts. This is the goal of semi-supervised learning, which exploits more widely available unlabeled data to complement small labeled data sets. In this paper, we propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels.
In November, Microsoft for Startups announced the availability of Azure AI infrastructure for high-end GPU virtual machine clusters for use in training and running large language models and other deep learning models, with access to Y Combinator and M12 startups. Today, we are excited to announce we are extending the offer to additional organizations that...
The post Microsoft Expands Free Azure AI Infrastructure Access to Startups appeared first on Microsoft for Startups Blog.