Silo AI Content

The latest articles, webinars, podcasts and more for you to learn how you can benefit from artificial intelligence.
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Versioning, transparency & monitoring in machine learning pipelines

With proper versioning, we can combine model predictions and the corresponding input data with model versions and trained data. With this kind of grouping, we can eventually detect data drifts and model miss performances. When it comes to implementation of the ML model, once we have set up the right versioning components and deployment scripts, then we can periodically run (batch) jobs that parse our predictions and analyze their quality.

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Accelerating the development of future concept vehicles with AI-driven solutions and products

Silo AI, the largest private AI lab in the Nordics, is a trusted AI partner that brings competitive  advantage to product R&D. We co-develop AI-driven solutions together with our automotive customers to build the future concepts, such as next generation autonomous vehicles and mobility services. Based on the latest technology and increased amount of sensors and data, our recent engagements have covered deep-learning based sensor fusion, localization, multi-object tracking and situational awareness to mention a few. We’re looking forward to discussing how our 180+ AI experts can help you in becoming AI-driven at IAA Mobility 2021.

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Towards autonomous navigation, localization, and mapping

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.

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Webinar recap: Silo AI x NVIDIA webinar – Situational awareness for vessels, Case: Groke Technologies

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.

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Sensor fusion

The how & why of sensor fusion

Sensor fusion in a nutshell is about combining the information from multiple different sensors in order to obtain a more accurate picture than any single sensor could provide by itself. From a theoretical point of view, sensor fusion is firmly based on understanding probability as a state of knowledge, which allows us to combine and manipulate different sources of information via the methods of Bayesian statistics. From a more practical point of view, real-time numerical sensor fusion is viable primarily due to the fact that Bayesian combinations of Gaussian probability densities yield Gaussian densities as a result. Thus if we can approximate our current state of knowledge and the incoming information using Gaussian probability densities, we can leverage analytical formulae and numerical linear algebra. This allows us to efficiently compute our updated state of knowledge.

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Marianne Rojo joins Silo AI to accelerate the adoption of AI

Our fast-growing business development team is happy to welcome a new member, Marianne Rojo, to join Silo AI as a Business Development Executive. Marianne, or Mandi as friends like to call her, is an experienced sales leader specialized in selling complex artificial intelligence solutions and managing large accounts. Having successfully led a sales career focusing on global enterprises, most notably IBM and Fujitsu, Marianne will drive the development of AI and MLOps particularly in the industrial and public sectors.

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Physical AI Systems — AI with a body

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.

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Implementing end-to-end scalable MLOps for a computer vision product

The overall goal of MLOps is to make the process of productizing ML models smoother. MLOps applies the DevOps techniques, concepts and practices to machine learning systems with an increased demand to take care of aspects around the activities, such as data versioning, data lineage and data quality. MLOps responses to an increased need for model observability and monitoring model performance.

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MLOps is not a platform, it’s a human process assisted by platforms

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.

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Rolls of paper at Kemira

Kemira and Silo AI leverage machine learning to predict and prevent process disturbances and quality issues in paper and board production

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.

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