AI

Oct. 16 is World Food Day, a global campaign to celebrate the founding of the Food and Agriculture Organization 80 years ago, and to work toward a healthy, sustainable, food-secure future. More than 670 million people in the world are facing hunger. Millions of others are facing rising obesity rates and struggle to get healthy […]

The proliferation of Internet of Things (IoT) devices has transformed how we interact with our environments, from homes to industrial settings. However, as the number of connected devices grows, so does the complexity of managing them. Traditional device management interfaces often require navigating through multiple applications, each with its own UI and learning curve. This

This post is cowritten by Salesforce’s AI Platform team members Srikanta Prasad, Utkarsh Arora, Raghav Tanaji, Nitin Surya, Gokulakrishnan Gopalakrishnan, and Akhilesh Deepak Gotmare. Salesforce’s Artificial Intelligence (AI) platform team runs customized large language models (LLMs)—fine-tuned versions of Llama, Qwen, and Mistral—for agentic AI applications like Agentforce. Deploying these models creates operational overheads: teams spend

OpenAI has announced “AI sovereignty” partnerships with governments around the world, but can proprietary models compete with Beijing’s open source offerings?

Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is helping with the former, with tools that comb through catalogs of materials to quickly tag promising candidates. But once a material is made, verifying its quality still involves scanning it

As costs for diagnostic and sequencing technologies have plummeted in recent years, researchers have collected an unprecedented amount of data around disease and biology. Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering. Now, Watershed Bio is helping scientists and bioinformaticians run experiments and

The convergence of artificial intelligence with physical systems marks a pivotal moment in technological evolution. Physical AI, where algorithms transcend digital boundaries to perceive, understand, and manipulate the tangible world, will fundamentally transform how enterprises operate across industries. These intelligent systems bridge the gap between digital intelligence and physical reality, unlocking unprecedented opportunities for efficiency

In healthcare, the ability to quickly analyze and interpret medical reports is crucial for both healthcare providers and patients. While medical reports contain valuable information, they often remain underutilized due to their complex nature and the time-intensive process of analysis. This complexity manifests in several ways: the interpretation of multiple parameters and their relationships (such

This post was written with Ajay Nyamati from Kitsa. The clinical trial industry conducts medical research studies to evaluate the safety, efficacy, and effectiveness of new drugs, treatments, or medical devices before they reach the market. The industry is a cornerstone of medical innovation, yet it continues to face a fundamental bottleneck: selection of the

Organizations need solutions for people and AI agents to securely collaborate through a single interface to the organization’s data and take actions across enterprise applications to improve productivity. The ability of an AI agent to securely and seamlessly connect with organizational knowledge bases, enterprise applications, and other AI agents is foundational to drive adoption and