1. Janusz Starzyk School of Electrical Engineering and Computer Science, Ohio University, USA www.ent.ohiou.edu/~starzyk Photo: https://www.adbusters.org/magazine/87/philosophy-zero-point.html http://www.geneang.com/Presence_Healing,_LLC/Neuroscience_of_Consciousness.html Machine Consciousness - A Computational Model Dilip Prasad School of Computer Engineering Nanyang Technological University, Singapore

2.  Consciousness  Scientific perspective  Philosophers’ perspective  Emergence of consciousness  Evolution and consciousness  Our approach for machine consciousness  Consciousness: functional requirements  Definition of machine consciousness  Computational model  Computational model: implications Outline Photo: http://tsvetankapetrova.wordpress.com/2009/06/30/5-setbacks-that-stop-you-big-time/

3. Description of Consciousness  The quality or state of being aware especially of something within oneself from Merriam Webster Dictionary  Nobody has a slightest idea of how anything material can be conscious – J.A Fodor  …our subjective experience or conscious state involving awareness, attention, and self reference - Jeanette Norden.  Anything that we are aware of at a given moment forms part of our consciousness, making conscious experience at once the most familiar and most mysterious aspect of our lives - Velmans Photo: http://www.amazon.com/Great-Courses-Jeanette-Norden/dp/159803362X/ref=pd_rhf_p_t_2

4. Scientific perspective  It may be pointless trying to define consciousness, its evolution or function as they may have many different interpretations, similar to other big words like perception, learning, knowledge, attention, etc – Sloman  Consciousness refers to focusing attention, mental rehearsal, thinking, decision making, awareness, alerted state of mind, voluntary actions and subliminal priming, concept of self and internal talk – Calvin & Ojemann  Consciousness is a combination of self awareness and qualia and memory plays an important role in it – Jeff Hawkins  Consciousness is a dynamic process and it changes with development of brain. Further, at macro-level there is no consciousness centre and at micro-level there are no committed neurons or genes dedicated to consciousness – Susan Greenfield

5. Philosophers’ perspective  Phenomenally conscious states are those states that possess fine-grained intentional contents of which the subject is aware, being the target or potential target of some sort of higher-order representation – Rosenthal (Higher Order Theory)  Consciousness is accomplished by a distributed society of specialists that is equipped with working memory, called a global workspace, whose contents can be broadcast to the system as a whole – Baars  …various events of content-fixation occurring in various places at various times in the brain... there is no single place in brain for consciousness – Dennett  Nisargadatta states that awareness is not a part (subset) of consciousness but instead it is its superset

6. Emergence of Consciousness WeekHuman Fetus brain development 6Cortical cells come at the correct position 20Cortical region is insulated with myelin sheath 25Development of local connections between neurons 30Fetus’ brain generates electrical wave patterns Conclusion : Emergence of consciousness is a gradual process Photos: http://daymix.com/Fetus-Brain-Development/ http://www.humanillnesses.com/Behavioral-Health-A-Br/The-Brain-and-Nervous-System.html?Comments[do]=mod&Comments[id]=1

7. Evolution and consciousness – appearance and evolution of consciousness Living BeingEvolutionary traits Analogous feasibility in machines Human Beings  Fully developed cross-modal representation  Sensory capabilities: auditory, taste, touch, vision, etc.  Bi-frontal cortex: planning, thought, motivation Impossible at present Hedgehog (earliest mammals)  Cross-modal representation  Sensory capabilities: auditory, touch, vision (less developed), etc.  Small frontal cortex Impossible at present Birds  Primitive cross-modal representation  Sensory capabilities: auditory, touch, vision, olfactory.  Primitive associative memory Associative memories Photos: http://images.google.com/

8. Evolution and consciousness –absence of consciousness Living BeingEvolutionary traits Analogous feasibility in machines Reptiles *  Olfactory system  Primitive vision Computer vision (nascent) Hagfish (early vertebrate)  Primitive olfactory system  Primitive nervous system Artificial neural networks Lower level animals (hydra, sponge, etc.)  Sensory motor units  Point to point nervous system Mechanical and/or electronic control systems * inconclusive\consciousness in transition Exceptional cases -> Octopus(memory & learning skill), Circadian sleep wake cycle of insects (crude state of consciousness), etc. Photos: http://images.google.com/

9. Our approach for machine consciousness  Define consciousness in functional terms  Identify minimum functional requirements  Identify minimum functional blocks, their individual roles, their inter- relationship  A computational model Photo: http://www.theglobalintelligencer.com/aug2007/fringe

10. Consciousness: functional requirements  Intelligence Mechanism to acquire and represent Knowledge Knowledge is a result of learning  Attention and attention Switching  Cognitive perception and related action Semantic memory Associative sensory-motor memory Episodic memory – not necessary  Cognitive awareness  Central executive Photo: http://eduspaces.net/csessums/weblog/11712.html

11. Computational Models of Intelligence  Not necessary alive  Consciousness requires –Intelligence (ability) –Awareness (state)  How to define and compute intelligence?

12. Embodied Intelligence –Mechanism: biological, mechanical or virtual agent with embodied sensors and actuators –EI acts on environment and perceives its actions –Environment hostility is persistent and stimulates EI to act –Hostility: direct aggression, pain, scarce resources, etc –EI learns so it must have associative self-organizing memory –Knowledge is acquired by EI Definition  Embodied Intelligence (EI) is a mechanism that learns how to minimize hostility of its environment

13. Motivated Learning  Various pains and external signals compete for attention.  Attention switching results from competition.  Cognitive perception is aided by winner of competition.  Definition: Motivated learning (ML) is pain based motivation, goal creation and learning in embodied agent.  Machine creates abstract goals based on the primitive pain signals.  It receives internal rewards for satisfying its goals (both primitive and abstract).  ML applies to EI working in a hostile environment.

14. Attention  Selective process of  cognitive perception/action  other cognitive experiences like thoughts, action planning, expectations, dreams  Result of attention switching  needed to have cognitive experience  leads to a sequence of cognitive experiences Comic: http://lonewolflibrarian.wordpress.com/2009/08/05/attention-and-distraction-what-are-you-paying-attention-to-08-05-09/

15. Attention Switching !!!  Dynamic process resulting from competition between representations related to motivations sensory inputs internal thoughts including spurious signals (like noise).  May be a result of deliberate cognitive experience (and thus fully conscious signal) subconscious process (stimulated by internal or external signals) Thus, while paying attention is a conscious experience, switching attention does not have to be. Photo: http://lonewolflibrarian.wordpress.com/2009/10/page/3/

16. Central Executive  Operates no matter whether machine is conscious or not  Platform for the emergence, control, and manifestation of consciousness  Control its conscious and subconscious processes  Driven by  learning mechanism  creation and selection of motivations and goals Central executive, by relating cognitive experience to internal motivations and plans, creates self-awareness and conscious state of mind.

17. Definition of Machine Consciousness A machine is conscious IF besides the required components for perception, action, and associative memory, it has a central executive that controls all the processes (conscious or subconscious) of the machine; The central executive is driven by the machine’s attention switching, motivation goal selection, and learning mechanism, and uses cognitive perception and understanding of motivations, thoughts, or plans. Photo: www.spectrum.ieee.org/.../biorobot11f-thumb.jpg

18. Computational Model of Machine Consciousness Semantic memory Sensory processors Data encoders/ decoders Sensory units Motor skills Motor processors Data encoders/ decoders Motor units Emotions, rewards, and sub-cortical processing Attention switching Action monitoring Motivation and goal processor Planning and thinking Episodic memory Queuing and organization of episodes Episodic Memory & Learning Central Executive Sensory-motor Inspiration: human brain Photo (brain): http://www.scholarpedia.org/article/Neuronal_correlates_of_consciousness

19. Sensory- Motor Block Semantic memory Sensory processors Data encoders/ decoders Sensory units Motor skills Motor processors Data encoders/ decoders Motor units Emotions, rewards, and sub-cortical processing Sensory-motor  sensory processors integrated with semantic memory  motor processors integrated with motor skills  sub-cortical processor integrated with emotions and rewards  Multiple processors, parallel processing, multiple individual outputs

20. Central Executive Attention switching Action monitoring Motivation and goal processor Planning and thinking Central Executive  interacts with other units for o performing its tasks o gathering data o giving directions to other units  no clearly identified decision center  decision influenced by o competing signals representing motivations, pains, desires, and attention switching need not be cognitive or consciously realized o competition can be interrupted by attention switching signal

21. Central Executive Attention switching Action monitoring Motivation and goal processor Planning and thinking Central Executive  Tasks o cognitive perception o attention o attention switching o motivation o goal creation and selection o thoughts o planning o learning, etc.  Requires o capability to dynamically select and directly execute programs o capability to activate semantic memory and control emotions

22. Computational Model: Implications  The motivations for actions are physically distributed o competing signals are generated in various parts of machine’s mind  Before a winner is selected, machine does not interpret the meaning of competing signals  Cognitive processing is predominantly sequential o winner of the internal competition serves as an instantaneous director of the cognitive thought process, before it is replaced by another winner  Top down activation for perception, planning, internal thought or motor functions o results in conscious experience decision of what is observed planning how to respond o a continuous train of such experiences constitutes consciousness Photo: http://www.prlog.org/10313829-homeless-man-earns-250000-after-viewing-prosperity-consciousness-video-subliminal-mind- training.html

23. References  J. A. Fodor, "The big idea: can there be science of the mind," Times Literary Supplement, pp. 5-7, July 1992.  J. Norden, Understanding the brain, Video lecture series.  M. Velmans, "Where experiences are: Dualist, physicalist, enactive and reflexive accounts of phenomenal consciousness," Phenomenology and the Cognitive Sciences, vol. 6, pp. 547-563, 2007  A. Sloman, "Developing concept of consciousness," Behavioral and Brain Sciences, vol. 14 (4), pp. 694-695, Dec 1991.  W. H. Calvin and G. A. Ojemann, Conversation with Neil's brain: the neural nature of thought and language: Addison-Wesley, 1994.  J. Hawkins and S. Blakeslee, On intelligence. New York: Henry Holt & Company, LLC., 2004.  S. Greenfield, The private life of the brain. New York: John Wiley & Sons, Inc., 2000.  Nisargadatta, I am that. Bombay: Chetana Publishing, 1973.  D. C. Dennett, Consciousness Explained, Penguin Press,1993.  D. M. Rosenthal, The nature of Mind, Oxford University Press, 1991.  B. J. Baars “A cognitive theory of consciousness,” Cambridge University Press, 1998. Photo: http://s121.photobucket.com/albums/o209/TiTekty/?action=view&current=hist_sci_image1.jpg

24. Questions ?? Photo: http://bajan.wordpress.com/2010/03/03/dont-blame-life-blame-the-way-how-you-live-it/

25. Embodiment of a Mind  Embodiment is a part of the environment that EI controls to interact with the rest of the environment  It contains intelligence core and sensory motor interfaces under its control  Necessary for development of intelligence  Not necessarily constant or in the form of a physical body  Boundary transforms modifying brain’s self- determination

26.  Brain learns own body’s dynamic  Self-awareness is a result of identification with own embodiment  Embodiment can be extended by using tools and machines  Successful operation is a function of correct perception of environment and own embodiment Embodiment of a Mind

27. Pain-center and Goal Creation  Simple Mechanism  Creates hierarchy of values  Motivation is to reduce the primitive pain level  Leads to formulation of complex goals  Reinforcement : Pain increase Pain decrease  Forces exploration + - Environment Sensor Motor Pain level Dual pain level Pain increase Pain decrease (-) (+) Motivation (-) (+) (-) Goal

28. Primitive Goal Creation -+ Pain Dry soil Primitive level open tank sit on garbage refill faucet w. can water Dual pain  Reinforcing a proper action Wall-E’s goal is to keep his plants from dying

29. Abstract Goal Hierarchy  Abstract goals are created to reduce abstract pains and to satisfy the primitive goals  A hierarchy of abstract goals is created to satisfy the lower level goals Activation Stimulation Inhibition Reinforcement Echo Need Expectation -+ + Dry soil Primitive Level Level I Level II faucet - w. can open water + Sensory pathway (perception, sense) Motor pathway (action, reaction) Level III tank - refill

30. Reinforcement Learning Motivated Learning  Single value function  Measurable rewards  Can be optimized  Predictable  Objectives set by designer  Maximizes the reward  Potentially unstable  Learning effort increases with complexity  Always active  Multiple value functions  One for each goal  Internal rewards  Cannot be optimized  Unpredictable  Sets its own objectives  Solves minimax problem  Always stable  Learns better in complex environment than RL  Acts when needed http://www.bradfordvts.co.uk/images/goal.jpg