20 Million-Dollar Problems for Any Brain Models and a Holistic Solution: Conscious Learning

This is a theoretical paper. It raises 20 open problems each of which is estimated to require one million dollars of investment or more. They are (1) the image annotation problem (e.g., retina is without bounding box to learn, unlike ImageNet), (2) the sensorimotor recurrence problem (e.g., all big data sets are invalid), (3) the motor-supervision problem (e.g., impractical to supervise motors throughout lifetime), (4) the sensor calibration problem (e.g., a life calibrates the eyes automatically), (5) the inverse kinematics problem (e.g., a life calibrates all redundant limbs automatically), (6) the government-free problem (i.e., no task-aware homunculus inside a brain), (7) the closed-skull problem (e.g., supervising hidden neurons is biologically implausible), (8) the nonlinear controller problem (e.g., a brain is a nonlinear controller but task-nonspecific), (9) the curse of dimensionality problem (e.g., a set of global features is insufficient for a life), (10) the under-sample problem (i.e., few available examples in a life), (11) the distributed vs. local representations problem (i.e., how both representations emerge), (12) the symbol problem (also called grounding problem, thus must be free from any symbols), (13) the local minima problem (so, avoid error-backprop learning and Post-Selections), (14) the abstraction problem (i.e., require various invariances and transfers), (15) the compositionality problem (e.g., metonymy beyond those composable from sentences), (16) the smooth representations problem (e.g., brain representations are globally smooth), (17) the motivation problem (e.g., including reinforcements and various emotions), (18) the global optimality problem (e.g., avoid catastrophic memory loss and Post-Selections), (19) the auto-programming for general purposes (APFGP) problem, (20) the brain-thinking problem. The paper discusses also why the proposed holistic solution of conscious learning [1], [2] solves each.