AI safety and alignment

Expanding on how Voice Engine works and our safety research

Exploring the technology behind our text-to-speech model.

OpenAI Board Forms Safety and Security Committee

OpenAI safety practices

Artificial general intelligence has the potential to benefit nearly every aspect of our lives—so it must be developed and deployed responsibly.

StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation

OpenAI’s commitment to child safety: adopting safety by design principles

Running Privacy-Preserving Inferences on Hugging Face Endpoints

AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks

Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability), collecting evidence (e.

Introducing the Chatbot Guardrails Arena

SCIN: A new resource for representative dermatology images

Posted by Pooja Rao, Research Scientist, Google Research Health datasets play a crucial role in research and medical education, but it can be challenging to create a dataset that represents the real world. For example, dermatology conditions are diverse in their appearance and severity and manifest differently across skin tones.

MELON: Reconstructing 3D objects from images with unknown poses

Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years.

Enterprise-ready trust and safety

Salesforce integrates OpenAI’s enterprise-ready LLMs to transform customer applications.

HEAL: A framework for health equity assessment of machine learning performance

Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer & Director, Google Core Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the diagnostic technology.

Cappy: Outperforming and boosting large multi-task language models with a small scorer

Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0 , FLAN , and OPT-IML .

Chain-of-table: Evolving tables in the reasoning chain for table understanding

Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP).

Social learning: Collaborative learning with large language models

Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be beneficial for them to learn effectively from each other, much like people do in social settings, which would allow LLM-based agents to improve each other’s performance.

Croissant: a metadata format for ML-ready datasets

Posted by Omar Benjelloun, Software Engineer, Google Research, and Peter Mattson, Software Engineer, Google Core ML and President, MLCommons Association Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring out what subset to use as features. So much time, in fact, that progress in the field of ML is hampered by a fundamental obstacle: the wide variety of data representations.

Introducing the Red-Teaming Resistance Leaderboard

Advances in private training for production on-device language models

Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given input text are the key technology for many applications [ 1 , 2 ]. In Gboard , LMs are used to improve users’ typing experience by supporting features like next word prediction (NWP), Smart Compose , smart completion and suggestion , slide to type , and proofread .

DP-Auditorium: A flexible library for auditing differential privacy

Posted by Mónica Ribero Díaz, Research Scientist, Google Research Differential privacy (DP) is a property of randomized mechanisms that limit the influence of any individual user’s information while processing and analyzing data. DP offers a robust solution to address growing concerns about data protection, enabling technologies across industries and government applications (e.

Intervening on early readouts for mitigating spurious features and simplicity bias

Posted by Rishabh Tiwari, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research Machine learning models in the real world are often trained on limited data that may contain unintended statistical biases . For example, in the CELEBA celebrity image dataset, a disproportionate number of female celebrities have blond hair, leading to classifiers incorrectly predicting “blond” as the hair color for most female faces — here, gender is a spurious feature for predicting hair color.