The recent launch of Agentforce marks a pivotal moment in orienting Salesforce and our customers’ businesses toward an AI-empowered future. In this emerging landscape, augmented by a network of AI agents, the role…
Simply put, AI Assistants are built to be personalized, while AI Agents are built to be shared (and scaled)—and both techniques promise extraordinary opportunities across the enterprise.
LLM benchmarks evaluate how accurately a generative AI model performs, but most benchmarks overlook the kinds of real-world tasks an LLM would perform in an enterprise setting.
In the rapidly evolving landscape of artificial intelligence (AI), we’re witnessing a Jagged Intelligence in the Enterprise In the rapidly evolving landscape of artificial intelligence (AI), we’re witnessing a fascinating paradox. AI systems…
The year was 2013. In a state-of-the-art kitchen laboratory in Stanford University’s Robotics Center, surrounded by the whir of servo motors and the aroma of brewing coffee, I observed our latest prototype attempt…
Developers face unique challenges when retrieving code snippets, such as understanding syntax, control flow, and variable dependencies. Enter SFR-Embedding-Code, a groundbreaking family of code embedding models that aims to address these challenges and revolutionize how we retrieve and generate code.
We present TACO, a family of multi-modal large action models designed to improve performance on complex questions that require multiple capabilities and demand multi-step solutions.
To address the challenges in generating multimodal instruction data, we developed ProVision, a scalable, programmatic framework that employs scene graphs and human-written programs to systematically synthesize vision-centric instruction data.