This Christmas we teamed up with Santa to maximise the efficiency of sleigh-based delivery, for a safe and effortless gifts distribution worldwide. We immediately thought of using Helicon to monitor in real time sleigh and reindeers’ performance, leveraging data to dynamically adjust the gift-delivery strategy.
Gartner has recently included Adaptive AI as one of the Top Strategic Technology Trends in 2023. It can be seen as an AI system that adapts to changing real-world situations and continuously evolves based on real-time data. Adaptive AI leverages event stream processing to retrain learning models and thus adjust for unforeseeable circumstances.
AI can definitely increase energy efficiency for buildings; applied to IoT sensors to collect real-time data, it suggests changes to avoid energy waste. By using AI-enabled algorithms, it can continuously monitor and adjust the energy usage of large buildings, as well as automatically adjust thermostats and lights in accordance with the occupancy of a particular space. Check out our solution to save energy in large buildings.
It’s no secret that the technologies that can help in building a value extraction process are numerous, and for this reason, choosing the right architecture can be difficult, expensive, and time-consuming. Let’s get deeper into Radicalbit’s latest platform – Helicon- application to chemical industry sector.
Applying AI to real-time IoT data brings prompt advantages for companies, like a considerable reduction in energy and raw material waste.
Artificial Intelligence and Machine Learning are already changing the world of marketing as we know it. These technologies help enhance and quicken countless marketing tasks, improving customer experiences and driving conversions. Here is a selection of some AI and Machine Learning solutions that can be extremely helpful for marketing purposes.
Just imagine the massive impact AI and Machine Learning have on our lives and jobs. According to SEM Rush, for instance, the global AI market is predicted to skyrocket in the next few years. Here are some 2022 predictions for AI and Machine Learning applications.
Building a performing Machine Learning model requires a significant amount of time to experiment. A data scientist tries different algorithms or different feature engineering strategies before getting the model right. Once the model is tuned to its best, it may be time to serve it in the production environment.
Online Learning is a branch of Machine Learning that has obtained a significant interest in recent years thanks to its peculiarities that perfectly fit numerous kinds of tasks in today’s world. Let’s dive deeper into this topic.