MusicFX Integrations

MusicLM is a new experimental AI tool that can turn your text descriptions into music. Just type in a prompt like “soulful jazz for a dinner party” and MusicLM will create two versions of the song for you. You can listen to both and give a trophy to the track that you like better, which will help improve the model. We believe responsible innovation doesn’t happen in isolation. We’ve been working with musicians like Dan Deacon and hosting workshops to see how this technology can empower the creative process. Whether you're a professional musician or just starting out, MusicLM is an experimental tool that can help you express your creativity.

We’re beta launching SynthID, a tool for watermarking and identifying AI-generated images. SynthID is being released to a limited number of Vertex AI customers using Imagen, one of our latest text-to-image models that uses input text to create photorealistic images. With this tool, users can embed an imperceptible digital watermark into their AI-generated images and identify if Imagen was used for generating the image, or even part of the image. Being able to identify AI-generated content is critical to promoting trust in information. While not a silver bullet for addressing the problem of misinformation, SynthID is an early and promising technical solution to this pressing AI safety issue. This technology was developed by Google DeepMind and refined in partnership with Google Research. SynthID could be expanded for use across other AI models and we plan to integrate it into more products in the near future.

Chinchilla is a large language model. Chinchilla uses the same compute budget as Gopher but with 70B parameters and 4× more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.