It finally knows what it's talking about ...

Researchers have identified key components in large language models (LLMs) that play a critical role in ensuring these AI ...

In a demo for its Agentforce AI Agent Builder, the company showed how imposing logical rules on customer service agents could ...

Learn how to shift from rankings to real marketing with strategies designed to survive AI-driven search changes.

According to a column by the New York Times’ Kevin Roose, employees at companies including Meta and OpenAI compete on ...

Why send your data to the cloud when your PC can do it better?

The remarkable performance of Large Language Models (LLMs) has inspired many applications, which often necessitate edge-cloud collaboration due to connectivity, privacy, and cost considerations.

Database research and development often require a large number of SQL queries for benchmarking purposes. However, acquiring real-world SQL queries is challenging due to privacy concerns, and existing ...

Abstract: Electric vehicle (EV) users’ behaviors are influenced by users’ willingness, which is not directly observable. Emerging large language models (LLMs) have advantages in handling this problem.

MIT researchers developed Attention Matching, a KV cache compaction technique that compresses LLM memory by 50x in seconds — without the hours of GPU training that prior methods required.

Consider PostgreSQL. Since its origins as an OLTP database, the community has built extensions for nearly every emerging use case: PostGIS for geospatial, TimescaleDB for time-series, pgvector for ...