text: **Symbolic AI and its limits**
  Symbolic AI (or "GOFAI")[370] simulated the high-level conscious reasoning that people use when they 
  solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" 
  tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems 
  hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent 
  action."[371] 
  
  However, the symbolic approach failed on many tasks that humans solve easily, such as learning, 
  recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level 
  "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.[372] 
  Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious 
  instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than 
  explicit symbolic knowledge.[373] Although his arguments had been ridiculed and ignored when they 
  were first presented, eventually, AI research came to agree with him.[ab][16] 
  
  The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that 
  human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research 
  into symbolic AI will still be necessary to attain general intelligence,[375][376] in part because sub- 
  symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a 
  modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial 
  intelligence attempts to bridge the two approaches. 