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Responsible AI at Google Research: Context in AI Research (CAIR) explores the development of AI methods with a focus on responsibility, fairness, transparency, and inclusion.

Overcoming leakage on error-corrected quantum processors: This blogpost discusses the challenges of leakage in quantum error correction and introduces a new operation, data qubit leakage removal (DQLR), that effectively mitigates leakage and improves the performance of quantum error correction.

Introducing OpenAI Data Partnerships to produce public and private datasets for AI model training to enhance understanding of various domains and improve AI's usefulness.

LLM distillation techniques will play a crucial role in data science teams' practices in 2024, enabling the creation of compact, production-ready models and accelerating data labeling.

Discover 3 new ways Duet AI can enhance your productivity in the Google Cloud console.

Google supports launch of DAOS Foundation for next-generation HPC and AI storage

Introducing Cloud SQL in-place upgrade: move from Enterprise to Enterprise Plus with ease

Improve autoscaling for your streaming jobs with Dataflow Streaming Engine autotuning

Google Cloud discusses their approach to trust and transparency in AI, focusing on responsible development, security measures, privacy safeguards, and compliance efforts.

Migrating IBM DataStage to Google Cloud: Strategies and advantages of migrating from on-prem IBM Datastage to Google Cloud for efficient and scalable data integration.

Merlyn Mind and Google Cloud collaborate to bring AI solutions to education and beyond.

Customizing coding companions for organizations with retrieval-augmented generation and fine-tuning techniques to generate organization-specific code recommendations.