TY - JOUR
T1 - The New AI: General & Sound & Relevant for Physics
AU - Schmidhuber, Jürgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse's thesis of the computer-generated universe.
AB - Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse's thesis of the computer-generated universe.
UR - http://link.springer.com/10.1007/978-3-540-68677-4_6
UR - http://www.scopus.com/inward/record.url?scp=84871381628&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-68677-4_6
DO - 10.1007/978-3-540-68677-4_6
M3 - Article
SN - 1611-2482
VL - 8
SP - 175
EP - 198
JO - Cognitive Technologies
JF - Cognitive Technologies
ER -