Christian Thut (19th Generation Thut Family) is a Swiss technology entrepreneur living in Edmonton. He has 20+ years in machine learning and began his career at the Swiss global company Roche. He programmed his first application on a Commodore VIC20 at age 10 and is a direct descendant of Niklaus Thut.
His #1 advice is to focus on hard-to-replicate talent that delivers value to people. Specifically, he has a singular focus to produce at the ultimate level, in terms of both quality and speed.
He travelled the world following the money and in 2006 re-launched the family brand retaining the time-honoured values of Trust, Precision, Innovation, and the same mantra: “Erfolg durch Präzision.” His focus on building and retaining wealth slid him sideways into accounting.
His vision was to create the most valuable product in Canada. His premise was that the mass affluent would be best served by algorithms, free of the failure inherent to human beings. He determined that the only way to reach this goal was to commit himself entirely to one activity: saving money where it counts. Only a truly focused approach can enable a firm to offer a legitimately, responsible, first-class service and a relationship of the same impeccable quality.
Everything was going as planned until Alberta’s oil crash forced him to expand our customer base beyond the oilfield, his programs became too large and failed out of complexity. This is because his robotic process automation consisted of rules to cover every transaction.
Inspired by the bottom-up nature of Satoshi Nakamoto’s white paper he switched to distributed processing neural networks with reinforcers that recognize data in large historic sets. He started using genetic algorithms to efficiently calculate optimization problems: ie how much dividends to draw and how much to pay towards a mortgage.
Today, his AI-based auto-categorizers are still training on $2.9+ billion of validated transactions to deliver autonomous results that continuously improve. His Narrow AI of deep learning compounds with the more transactions processed, and architectural upgrades to the original neural modelling and now it is able to identify and categorize 98% of client transactions without fail.
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