Perhaps the most important lesson to be learned with AI/ML is that individual experiments and projects are educational, but they do not lead to business. An organization moving towards scaling
Autonomous navigation, localization, and mapping mean the ability of the autonomous system to create a map of the surrounding environment, localize the machine on that map and make the navigation planning accordingly. These features are crucial in the development of autonomous machinery, vehicles, and vessels, and are often enabled by techniques such as sensor fusion.
Groke Technologies is paving the way for autonomous navigation solutions in the marine industry. On June 2, 2021, we had the opportunity to learn about the future of the marine industry with Groke Technologies, a Finnish marine company. With the help of Silo AI experts and the use of NVIDIA’s products, Groke is utilizing sensor fusion technologies to achieve better situational awareness of a vessel’s surroundings.
In this article, I will cover the most common use cases I’ve seen for sensor fusion, and gather some of my own experiences in working with modern AI-driven sensor fusion techniques in the maritime and automotive industries.
An embodied AI system can actively perceive the world around them in order to gather information through sensors such as vision, process this information on different cognitive levels, both autonomously and in interaction with a human, and finally make plans or decisions based on the outcome. In the autonomous vehicle case, the internal processing consists of planning future driving actions in interaction with the surrounding world and possibly also with a driver.
Silo AI expands into industrial optimization by acquiring the business of the technology consulting company FINNOPT. The acquisition strengthens Silo AI’s ability to create value to its global clientele in Industry 4.0 and autonomous vehicles, among others.
Machine learning operations, MLOps, is the organizational practice for operationalizing AI and accelerating ML development in a sustainable way. Once you’ve validated a couple of AI use cases by pilots and proofs-of-concept, scaling the development and the use of AI in your organization will require both a solid machine learning infrastructure and an aligned way of operating.
The future of the papermaking process is data-driven. This can already be seen today in the collaboration between Kemira, the global chemicals company serving customers in the pulp and paper industry, and Silo AI, the largest private AI lab in the Nordics. The two companies are today announcing their collaboration, which has already resulted in a predictive AI-driven solution taken into production in board and paper production.
Kemira is now using an explainable AI solution to predict and prevent issues with paper and board quality and paper machine runnability. Our AI experts built a machine learning-driven solution that provides real-time predictions to a status dashboard.
Machine learning for predictive maintenance can provide a detailed risk assessment of potential failure events, and highlight situations with elevated risk. Combine it with a network of IoT sensors in your mission-critical equipment and 5G connectivity, and you’ll be on your way towards more reliable operations.
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