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Explore how servo stacks can serve coffee, navigate rooms, and perform complex tasks by building a sensory-motor abstraction stack. Learn how each servo functions as an interpolating associative memory, storing trajectories of signals and generalizing them to directed surfaces in vector space. Discover the power of feedback controllers in processing sensory-motor reflexes and how to apply them to achieve artificial general intelligence.
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J Storrs Hall President The Foresight Institute The Robotics Path to AGI using Servo Stacks
A sensory-motor abstraction stack Serve Coffee walk to dining room locate coffee table entering dining room three steps, turn right choose foot placement doorjamb location lift right foot, extend left kinaesthesia proprioception contract particular muscles footsole pressure pattern
Crosstalk at all levels reflex efference copy sensory motor reflex efference copy reflex efference copy reflex efference copy
reinterpret as a stack of feedback controllers (servos) serving coffee navigating rooms walking (A working brain is a complex network, not a linear chain!)
Each servo is an interpolating associative memory • It stores trajectories of the signals on its connections • Generalizes them to directed surfaces in the vector space • CBR-like learning • Can perform or recognize trajectories • each trajectory associated with a condensed-signature “name” in signals to/from higher level servos
Memory = {(S, P, D, F), ...} Setpoint Plant Differential Feedback S P D F Inputs and outputs have dynamically- settable strengths to determine which fields are “address” and which are “data”
Interpolation and extrapolation: Analogical Quadrature Desired/predicted result Recognized current situation action to take D used for chaining S Best match in memory P result in memory action taken F EVERYTHING in the system is represented as simple numeric vectors
Connectivity determines function Minsky 1954 sequencer S P D F There is also Recognize mode Homeostat S P D F simulator S P D F
Parallel similarity match spreading activation model beliefs freeze police believes please fleece pleas Fleaspeck
Holonic feedback from context blocks world sentence command beliefs freeze police believes please fleece pleas
Active Production Networks (Mark Jones, Bell Labs, 80's) sentence command question W-word VP NP [please] VP NP statement NP VP Please pick up the green sphere
Holonic feedback causes priming sentence command [please] VP NP det adj N Please pick up the green sphere
“Please pick up the greensphere” red (round thing) S brown box blue block red block CMD green (round thing) blue cylinder (grayish color) rod brown cone red cylinder VP NP orange block det adj N ? green
The meaning of a phrase is • The configuration of servos that can • recognize a thing, situation, or action • simulate a process (including actions) • plan an action leading to a given situation • perform an action • Understanding the phrase consists of building that working machine • Active Subnet Configuration -- ASC
ASCs look like k-lines in Minsky's Society of Mind model green sphere Picker-upper Finder Reacher Grasper Lifter proprioception eyes touch This is the same structure as the APN, run in reverse. It can recognize actions; the APN can generate sentences.
Learning on three levels • Lower-level adaptive learning as the associative memories fill with better pictures of the space • this happens automatically and continually • Medium-level conceptual learning as new servos and connection geometries are created • automatic mining of all signals for correlations and clustering, pruned by predictive skill • High-level learning as learned, conscious action • existing servos can create new ones and manipulate ASCs with explicit actions