Ai for Business

  Why Multimodal Embeddings Could Change Enterprise Search Imagine this. A field technician sees a machine component on a factory floor, takes a photo of it, and the system instantly retrieves the relevant manual or maintenance documentation. No part numbers. No exact keywords. Just a photo. This kind of search experience is becoming possible with multimodal embeddings. In simple terms, embeddings convert things like text or images into numerical representations that capture their meaning. When two things are conceptually similar, their embeddings end up closer to each other in vector space. Recently, Google released Gemini Embedding 2, which creates embeddings for multiple types of data such as text, images, audio, and video, all in the same shared space. This means that a photo, a written description, or even a video frame of the same object can be understood as related by a search system. I ran a quick experiment using the Gemini API. I compared a sports car image with two text...

  In parts of Phoenix, it is no longer unusual to see a car arrive without a driver. Robotaxis from Waymo have been operating there for years, picking up passengers, navigating intersections, and completing trips in regular city traffic. Similar pilots have appeared in areas of San Francisco through Cruise, and in Chinese cities such as Wuhan through Baidu’s Apollo Go program. And yet, these places are exceptions. Autonomous cars have been under development for more than two decades. The required technologies—advanced sensors, high-performance computing, machine learning algorithms, real-time mapping, and connectivity—are all available. So the vehicles are adequately intelligent. The progress has not stalled for lack of time or innovation. So why are self-driving cars operating in only a handful of cities across the world? The answer lies not inside the car, but outside it. Autonomous driving succeeds where the environment supports it—where conditions such as roads and regulations ...