text: Up to this point, most of AI's funding had gone to projects that used high-level symbols to represent 
  mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt 
  that this approach would be able to imitate all the processes of human cognition, especially perception, 
  robotics, learning and pattern recognition,[335] and began to look into "sub-symbolic" approaches.[336] 
  Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that 
  move and survive.[x] Judea Pearl, Lofti Zadeh, and others developed methods that handled incomplete 
  and uncertain information by making reasonable guesses rather than precise logic.[86][341] But the most 
  important development was the revival of "connectionism", including neural network research, by 
  Geoffrey Hinton and others.[342] In 1990, Yann LeCun successfully showed that convolutional neural 
  networks can recognize handwritten digits, the first of many successful applications of neural 
  networks.[343] 
  
  AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal 
  mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" 
  focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, 
  economics and mathematics).[344] By 2000, solutions developed by AI researchers were being widely 
  used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as 
  the AI effect).[345] However, several academic researchers became concerned that AI was no longer 
  pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they 
  founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded 
  institutions by the 2010s.[4] 