An Implementable Architecture for Conscious Machines

1. An Implementable Architecture for Conscious Machines Dr. Pentti O A Haikonen, Principal Scientist, Cognitive technology Nokia Research Center

2. Introduction The aim of my research is to develop a conscious machine; not one with a “flea’s consciousness” but a robot brain with the cognitive abilities and hallmarks of the human conscious mind. This machine would have to posses: -Awareness of its environment, time and place -Self-consciousness; awareness of its existence as an independent entity, awareness of its mental content like inner speech and inner imagery and the recognition of these as its own -Apparent subjective immateriality of the mental content -Cognitive functions that parallel those of the human brain; symbol processing in the human sense, natural language -Emotions, emotional significance and judgement

3. The General Model of Cognition “mental loop” “external loop”

4. Basic Specifications I -Processing with meaning and significance -Distributed neural signal array representation -Associative soft symbol processing -System reactions; pain, pleasure, good, bad, emotions -Match/mismatch/novelty detection -Distributed attention controlled by significance -Modulation domain operation

5. Basic Specifications II -Perception process, incl. attention, priming and prediction -Learning, also learning by imitation, “mirror neurons” -Inner speech, natural language -Inner imagery -Introspection, judgement of own thoughts -Imagination, planning (involves the imagery of “self” executing imagined actions) -True “immaterial” consciousness; self-awareness

6. The Outline of a Conscious Machine

7. Neuron groups for Inner Processes Main input signal array Output signal array “Neuron group” Associative signal array inputs -Can associatively connect large arrays of neural signals to each other -Can associatively connect neural signal array sequences to each other -Can associatively evoke arrays of neural signals and neural signal array sequences even by limited length and incomplete cues -Sensitive to signal intensity; significance control -Can determine match/mismatch/novelty conditions between main input signal array and associative signal arrays -Finds “best fit” by Winner-Takes-All threshold operation

8. The General Architecture

9. The Visual Subsystem

10. The General Architecture II -Each modality works on its own and produces streams of percepts about environment and internal states. -Modalities are associatively cross-connected, therefore the activity of one modality may be reflected in the other modalities; percepts may be named and labeled, names may evoke corresponding percepts…the activity of one modality may be memorized and reported in terms of other modalities, etc. -Attention determines which percepts are accepted for further action. Attention is controlled by signal intensity and thresholds, these are controlled by e.g. emotional significance. -Pain and pleasure are system reactions that affect attention.

11. Why Would This Machine be Conscious? We are conscious when we are able to report to ourselves what we are experiencing and are able to make memories of that. This machine is able to produce these reports; it can make a verbal note e.g. about what is being seen and it can also make an episodic memory of that. The machine will accumulate a personal history. A self-conscious being must be able to perceive the difference between the percepts caused by external entities and percepts of its own inner activity. There are several ways to achieve this within this architecture. The machine will, in principle, be able to report having a flow of inner speech and inner imagery and will claim the ownership of these. The “mental activity” takes place in modulation domain, hence the mental content appears as immaterial.

12. NRC associative neuron group integrated circuit development work The purpose of the work is to develop associative neuron group microchips that -Are suitable for cognitive and conscious neural system architectures -Can learn; associatively connect very large arrays of neural signals to each other -Can associatively connect neural signal array sequences to each other -Can associatively evoke arrays of neural signals and neural signal array sequences by limited length and incomplete cues (soft symbol processing) -Can accommodate importance -Can determine match/mismatch/novelty conditions between neural signal arrays

13. Experiments with the neuron microchip ver. 2001 Audio oscillator bank Interval detector Associative neuron groups and perception-response loops Microphone + one octave filter bank This test system could learn associatively short melodies and mimic them when few notes were played as a cue. The system learned the pitches of the notes as well as their duration; also the duration of any silent(!) interval.

14. Associative neuron group integrated circuit v. 2002 An Experimental Integrated Circuit for Conscious Machines -24 neurons, 24 x 24 synapses in 8 individual groups (total 4608) - Fully parallel internal operation, serial external communication -Contains the main features of a future practical large scale microchip

15. Associative neuron group integrated circuit ver. 2002

16. Associative neuron group integrated circuit ver. 2002 -Original circuitry and specifications developed by the author at NRC -Actual implementation on silicon executed by VTT Microelectronics Neuron chip ver. 2002 installed in test circuit board (square chip in the middle)

17. Conclusions The author has developed an architecture for conscious machines. More detailed description and discussion can be found in the book: The Cognitive Approach to Conscious Machines, Imprint Academic 2003 Experimental integrated circuits are being developed for the actual implementation. More work will be needed in the development of suitable input/output sensors and devices and especially in the sensory signal preprocessing area.

18. An Implementable Architecture for Conscious Machines Thank You for Your Attention Dr. Pentti O A Haikonen, Principal Scientist, Cognitive Technology Nokia Research Center

19. -end-