Posts in category: Deepmind

We introduce GraphCast, a state-of-the-art AI model able to make medium-range weather forecasts with unprecedented accuracyRead More

Progress update: Our latest AlphaFold model shows significantly improved accuracy and expands coverage beyond proteins to other biological molecules, including ligands.Read More

Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. To ensure these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose.In our paper, we propose a three-layered framework for evaluating the social and ethical risks of AI systems. This framework includes evaluations of AI system capability, human interaction, and systemic impacts.We also map the current state of safety evaluations and find three main gaps: context, specific risks, and multimodality. To help close these gaps, we call for repurposing existing evaluation methods for generative AI and for implementing a comprehensive approach to evaluation, as in our case study on misinformation. This approach integrates findings like how likely the AI system is to provide factually incorrect information, with insights on how people use that system, and in what context. Multi-layered evaluations can draw conclusions beyond model capability and indicate whether harm — in this case, misinformation — actually occurs and spreads. To make any technology work as intended, both social and technical challenges must be solved. So to better assess AI system safety, these different layers of context must be taken into account. Here, we build upon earlier research identifying the potential risks of large-scale language models, such as privacy leaks, job automation, misinformation, and more — and introduce a way of comprehensively evaluating these risks going forward.Read More

We are launching a new set of resources for general-purpose robotics learning across different robot types, or embodiments. Together with partners from 34 academic labs we have pooled data from 22 different robot types to create the Open X-Embodiment dataset. We also release RT-1-X, a robotics transformer (RT) model derived from RT-1 and trained on our dataset, that shows skills transfer across many robot embodiments.Read More

We’ve released a catalogue of ‘missense’ mutations where researchers can learn more about what effect they may have. Missense variants are genetic mutations that can affect the function of human proteins. In some cases, they can lead to diseases such as cystic fibrosis, sickle-cell anaemia, or cancer. The AlphaMissense catalogue was developed using AlphaMissense, our new AI model which classifies missense variants.Read More

Today, in partnership with Google Cloud, we’re beta launching SynthID, a new tool for watermarking and identifying AI-generated images. It’s 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. This technology embeds a digital watermark directly into the pixels of an image, making it imperceptible to the human eye, but detectable for identification. While generative AI can unlock huge creative potential, it also presents new risks, like creators spreading false information — both intentionally or unintentionally. Being able to identify AI-generated content is critical to empowering people with knowledge of when they’re interacting with generated media, and for helping prevent the spread of misinformation.Read More