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Hans Moravec — Wikipedia

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Moravec is a historically significant figure in AI forecasting and existential risk thinking; his ideas on robot intelligence timelines and mind uploading influenced later AI safety discourse, including figures like Nick Bostrom.

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Summary

Wikipedia article on Hans Moravec, a robotics researcher and futurist known for his influential ideas on mind uploading, the Moravec paradox, and predictions about machine superintelligence. His work spans robotics, AI capabilities forecasting, and transhumanist thought, with notable contributions including the concept that high-level reasoning requires less computation than low-level sensorimotor skills.

Key Points

  • Moravec is best known for the 'Moravec paradox': tasks easy for humans (perception, movement) are hard for AI, while tasks hard for humans (chess, math) are relatively easy for AI.
  • He predicted rapid growth in robot intelligence and forecast that machines would surpass human intelligence by the mid-21st century.
  • His book 'Mind Children' (1988) explored mind uploading and the idea that human consciousness could be transferred to computers.
  • His work influenced early AI safety and existential risk discourse, particularly regarding the implications of superintelligent machines.
  • Moravec co-founded the field of mobile robotics and conducted pioneering work at Carnegie Mellon's robotics institute.

Cited by 1 page

PageTypeQuality
Early Warnings EraHistorical31.0

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 From Wikipedia, the free encyclopedia 
 
 
 
 
 
 Austrian-Canadian roboticist and futurist (born 1948) 
 Hans P. Moravec Born ( 1948-11-30 ) November 30, 1948 (age 77) [ 1 ] 
 Kautzen , Austria [ 1 ] Alma mater BSc: Acadia University [ 1 ] 
MSc: University of Western Ontario [ 1 ] 
PhD: Stanford University [ 1 ] Known for Moravec's corner detector 
 Moravec's paradox 
 Bush robot 
 Occupancy grid mapping 
 Quantum suicide and immortality 
 Rotating skyhook 
 Rotovator 
 Stanford Cart Scientific career Fields computer science , Robotics , artificial intelligence Institutions Carnegie Mellon University [ 1 ] 
Stanford University [ 1 ] Thesis Obstacle avoidance and navigation in the real world by a seeing robot rover   (1980) Doctoral advisor John McCarthy [ 1 ] 
 
 Hans Peter Moravec (born November 30, 1948, Kautzen , Austria ) is a computer scientist and an adjunct faculty member at the Robotics Institute of Carnegie Mellon University in Pittsburgh , USA. He is known for his work on robotics , artificial intelligence , and writings on the impact of technology . Moravec also is a futurist with many of his publications and predictions focusing on transhumanism . Moravec developed techniques in computer vision for determining the region of interest (ROI) in a scene.

 
 Career

 [ edit ] 
 Moravec attended Loyola College in Montreal for two years and transferred to Acadia University , where he received his BSc in mathematics in 1969. He received his MSc in computer science in 1971 from the University of Western Ontario . He then earned a PhD in computer science from Stanford University in 1980 for a TV -equipped robot which was remotely controlled by a large computer (the Stanford Cart [ 2 ] ). The robot was able to negotiate cluttered obstacle courses . Another achievement in robotics was the discovery of new approaches for robot spatial representation such as 3D occupancy grids. He also developed the idea of bush robots .

 Moravec joined the newly established Robotics Institute at Carnegie Mellon in 1980 as a research scientist, becoming research professor in 1995. He has been an adjunct professor at the institute since 2005. [ 1 ] 

 Moravec was a cofounder of Seegrid Corporation [ 3 ] of Pittsburgh, Pennsylvania , [ 4 ] in 2003, which is a robotics company with one of its goals being to develop a fully autonomous robot capable of navigating its environment without human intervention.

 He is also somewhat known for his work on space tethers . [ 5 ] 

 Futurism

 [ edit ] 
 Predictions

 [ edit ] 
 Hans Moravec has made some concrete predictions as to the future of intelligence, by estimating the computational cost (measured in instructions pe

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