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 ...

Strange as it may feel, I’m sitting inside Google Docs, writing an article about the AI that’s been helping me all along. Google Docs is one of those apps where AI has been quietly working for years, long before “AI features” became a marketing headline. In Microsoft Word, AI is easy to spot—Copilot is front and center. In Google Docs, it’s different. The AI has been there for a long time, quietly helping users without making a big deal about it. That’s what this article explores. Smart suggestions you barely notice Smart Suggestions is one of the ways AI quietly works in the background in Google Docs, though you might not see it depending on your account type. It uses machine learning to help you write faster by suggesting words and phrases as you type, kind of like having a helpful assistant looking over your shoulder. However, features like Smart Compose are primarily available for certain work or school accounts with specific Google Workspace plans, and they work in English, Spanis...

Image by Rajashree Rajadhyax WhatsApp is one app that we use everyday. I’m sure with many that is the default way of staying connected with the world. Its easy to use, always available and the best thing is that its associated with your mobile number. I think the success behind WhatsApp over any other messaging tool is its association with the mobile number. While the interface is clean and simple and there is no bragging about use of AI, yet AI is working in the background to make our WhatsApp experience smooth. Let’s do a deep dive. Smart and optimised message delivery When you send a message on WhatsApp, it feels simple and instant. But real-world networks are messy. Signals drop, phones switch between Wi-Fi and mobile data, battery savers kick in, and the person on the other side may be offline. Yet messages usually do reach. This is because WhatsApp doesn’t just send a message once and forget about it. That would be a purely programmed or deterministic behaviour. Instead, when the...

  I was reading an article and they talked about some 10 breakthrough technologies for this year. While my interest generally is around AI, I was keen to know which other technologies are defining the future. I thought of sharing them with you. Of course this list has been curated after reading articles from the top research analysts firms like Gartner, MIT and others Geopatriation Geopatriation means moving company data and applications out of global public clouds and back into local, sovereign, or regional environments to reduce geopolitical risk. As global uncertainty grows, organizations are becoming more concerned about data sovereignty, regulations, and long-term reliability, especially for AI workloads. This shift is happening because political tensions, data localization rules, and foreign laws can interrupt cloud access or expose sensitive data to other governments. By geopatriating, companies can stay compliant, keep their systems running smoothly, and maintain stronger c...