Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Trillion Parameter run achieved with DeepSeek R1 671B model on 36 Nvidia H100 GPUs We are pleased to offer a Trillion ...

Databricks, Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Fabric – to see how they address rapidly evolving ...

SQL Server supports quicker deployment across enterprise tools through integration services and hybrid system support.Oracle Database is structur ...

VAST AI OS will leverage NVIDIA libraries to accelerate both compute and data services for RAG, vector search, real-time SQL, ...

Abstract: This work introduces a schema-aware solution that employs the LLaMA 3.2 large language model to translate natural language (NL) queries into executable SQL statements. Unlike traditional ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...

Abstract: Digital libraries and research repositories require an efficient retrieval mechanism for author-specific publication data. Conventional SQL queries, such as LIKE searches, have a detrimental ...

The ability to write parts of SQL queries in natural language will help developers speed up their work, analysts say. Google is previewing a new AI-driven feature in its BigQuery data warehouse that ...

Highest search volumes were for Pet Products, Apparel & Fashion and Health & Wellness, with increases of 81%, 51% and 41% respectively year-over-year, according to AI Search market leader Algolia’s ...