Citizen of Silopolis: Dmitry Kan
Dmitry Kan is a Lead AI Scientist in our Helsinki office, who can be described as a technical leader, founder, software engineer, researcher and entrepreneur.
Dmitry Kan is a Lead AI Scientist in our Helsinki office, who can be described as a technical leader, founder, software engineer, researcher and entrepreneur.
Document understanding means extracting useful information about the document, and with a model called LayoutLM, it is possible to take into account both textual content and its structure as well as the visual aspect of the document.
To better serve our international client base, we’re next year focusing on building out AI labs in new markets, with Teppo Kuisma as our lead for internationalization. Teppo joins us after having spent a few years in the Bay Area, and is now focusing full speed on building new sites to create valuable long-term partnerships to better serve both new and existing international clients.
Silo AI teamed up with intelligent port platform company Awake.AI to develop machine learning and computer vision -powered AI solutions that improve the situational awareness at ports.
Silo AI helped Finnair build a solution that improved situational awareness of air traffic, enabling the airline to predict possible disruptions to air traffic more accurately.
Together with Ramboll, a leading Nordic engineering consultancy, we developed a machine learning-based solution for optimizing chemicals during the water treatment process.
Together with HSY, we developed a machine learning-powered solution to improve pipe maintenance by predicting possible blockages.
Together as part of a larger initiative, Auria Biobank and Silo AI use computer vision to analyze digital pathology images of tissue samples from one of the most common skin cancers. The goal is to find cues that would help clinicians to assess the risk of metastasis.
Public speaking is perhaps one of the most complex human skills to master. MySpeaker, a leading Nordic speaker and communications bureau, partnered with Silo AI to build a reinvented public
What are the paths and pitfalls in taking AI into production? How do you turn the first success into the next three? Taking an AI solution into production requires a
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