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Saturday, January 27, 2018

Algorithms of Our Future Thinking Machines

More about dealing with uncertainty.    Most of this is today rolled into the statistics of machine learning, but it needs to be included in results that can be easily propagated for reasoning and assisting.   Note especially the need for this in dynamic, contextually rich systems.

The Algorithms of Our Future Thinking Machines 
Uppsala University  By Anneli Bjorkman  

Researchers at Uppsala University (UU) and the KTH Royal Institute of Technology in Sweden are building algorithms for dynamic systems under the NewLEADS project. "Uppsala's focus in this project is to build mathematical models of dynamic systems that can identify and deal with uncertainty," says UU professor Thomas Schon. Among KTH's areas of concentration are designing experiments that extract as much data as possible from a particular system that is to be modeled, notes KTH professor Hakan Hjalmarsson. "I hope...that KTH and UU will be able to build a good theoretical base that is easy to implement in various applications of dynamic systems such as smart climate control of buildings and self-driving vehicles, and that we get to see them put to good use," he says. Schon notes his team's latest area of focus is medical applications, particularly automated diagnosis as a tool for doctors.   .... " 

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