We are thrilled to announce that Juan Carlos Niebles, one of our talented researchers, has been named one of the top 100 in AI by the Artificial Intelligence Observatory in Colombia. This recognition…
Our team at Salesforce Research introduces Text2Data, an innovative framework specifically designed to generate high-quality, controllable data from limited textual input.
AI is rapidly transforming industries, helping businesses enhance customer experiences, improve efficiency, and make smarter decisions. But an essential question arises: How can we ensure that AI is creating accurate and grounded answers?…
In this blog, we’ll dive into the challenges of AI personalization, why current systems fall short, and how PersonaBench helps bridge the gap—paving the way for smarter, more reliable AI assistants.
AI-powered solutions like Salesforce CRM are revolutionizing customer engagement, streamlining workflows, and providing deeper insights into customer needs. However, with the rise of large language models (LLMs), new security challenges have emerged. One…
In the rapidly evolving landscape of artificial intelligence (AI), we're witnessing jagged intelligence in the enterprise. Here's a closer look.
Today's investments in digital automation are laying the groundwork for a future where agents and robots converge in ways that reshape how work gets done.
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.
