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- New Adventures with Innovation Toronto
  
  Innovation Toronto has been running since 2007 and as such is getting a little old in terms of its constituent parts. the theme in particular has been a problem mainly because we relied too much on some of the components attached to it and therefore could not move on without extensive work. it is now come to the point where that extensive work is going to produce good results. we are already in the process of updating and upgrading various parts of it and over time will make sure that each and every post has been Revisited and revamped so it presents as best it can.
  
  Testing

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Follow
- New Adventures with Innovation Toronto
  
  Innovation Toronto has been running since 2007 and as such is getting a little old in terms of its constituent parts. the theme in particular has been a problem mainly because we relied too much on some of the components attached to it and therefore could not move on without extensive work. it is now come to the point where that extensive work is going to produce good results. we are already in the process of updating and upgrading various parts of it and over time will make sure that each and every post has been Revisited and revamped so it presents as best it can.
  
  Testing

machine predictions

“Humans and machine algorithms have complementary strengths and weaknesses,” says study co-author Mark Steyvers (right), UCI professor of cognitive sciences, here with fellow co-author Padhraic Smyth, UCI Chancellor’s Professor of computer science. “Each uses different sources of information and strategies to make predictions and decisions.” Steve Zylius / UCI

New and improved approaches to human-AI collaboration by combining machine and human predictions