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Archive for March, 2011

Don’t have Stress At Computer

Posted by sailjamehra on March 1, 2011

  • Don’t have  Stress At Computer Why Because, Brain has

 

  • 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

 

  • Description of Consciousness

 

  • The quality or state of being aware especially of something within oneself .
  • Nobody has a slightest idea of how anything material can be conscious .
  • …our subjective experience or conscious state involving awareness, attention, and self reference.
  • 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.

 

  • 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 .
  • 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 .
  • Consciousness is a combination of self awareness and qualia and memory plays an important role in it .
  • 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 .

 

  • 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. 
  • 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 . 
  • …various events of content-fixation occurring in various places at various times in the brain … there is no single place in brain for consciousness . 

Nisargadatta states that awareness is not a part (subset) of consciousness but instead it is its superset

  • – appearance and evolution of consciousness
    • 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
    • Evolution and consciousness
      –absence of consciousness
       
    • To avoid Our approach consciousness
    • Define consciousness in functional terms
    • Identify minimum functional requirements
    • Identify minimum functional blocks, their individual roles, their inter-relationship
    • A computational model
    • 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
    • Not necessary alive
    • Consciousness requires
    • Intelligence (ability)
    • Awareness (state)
    • Embodied Intelligence
    • Definition
      • Embodied Intelligence (EI) is a mechanism that learns how to minimize hostility of its environment
        • 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
    • Motivated Learning
    • Definition: Motivated learning (ML) is pain based motivation, goal creation and learning in embodied agent.
    •  
      •  
        • Various pains and external signals compete for attention.
        • Attention switching results from competition.
        • Cognitive perception is aided by winner of competition.
    • 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
    • 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.
    • Central Executive
    • Central executive, by relating cognitive experience to internal motivations and plans, creates self-awareness and conscious state of mind.
    • Computational Model of Machine Consciousness
    • Central Executive
    • Tasks
      •  
        • cognitive perception
        • attention
        • attention switching
        • motivation
        • goal creation and selection
        • thoughts
        • planning
        • learning, etc.
    • Requires
      •  
        • capability to dynamically select and directly execute programs
        • capability to activate semantic memory and control emotions
  • Living Being
    • Evolutionary traits
    • Analogous feasibility in machines
     
      

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    •  

     

           
      

    •  

     

         

        

     

     

     

    • Living Being
    • Evolutionary 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

     

     

     

     

     

     

     

     

     

     

 

  • 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. 
  • 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 . 
  • …various events of content-fixation occurring in various places at various times in the brain … there is no single place in brain for consciousness . 

Nisargadatta states that awareness is not a part (subset) of consciousness but instead it is its superset

Emergence of Consciousness

Evolution and consciousness
– appearance and evolution of consciousness

 

  • Computational Model: Implications

 

  • The motivations for actions are physically distributed
    •  
      • 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
    •  
      • 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
    •  
      • results in conscious experience
        •  
          • decision of what is observed
          • planning how to respond
      • a continuous train of such experiences constitutes consciousness

 

  • so set  Goal like :

  • Embodied Intelligence

 

  • Definition
    • Embodied Intelligence (EI) is a mechanism that learns how to minimize hostility of its environment
      • 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

 

  • Motivated Learning

 

 

  • Definition: Motivated learning (ML) is pain based motivation, goal creation and learning in embodied agent.

 

  •  
    •  
      • Various pains and external signals compete for attention.
      • Attention switching results from competition.
      • Cognitive perception is aided by winner of competition.

 

  • 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

 

  • 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.

 

  • Central Executive

 

  • Central executive, by relating cognitive experience to internal motivations and plans, creates self-awareness and conscious state of mind.

 

  • Computational Model of Machine Consciousness

 

  • Central Executive

 

  • Tasks
    •  
      • cognitive perception
      • attention
      • attention switching
      • motivation
      • goal creation and selection
      • thoughts
      • planning
      • learning, etc.

 

  • Requires
    •  
      • capability to dynamically select and directly execute programs
      • capability to activate semantic memory and control emotions

 

  • Computational Model: Implications

 

  • The motivations for actions are physically distributed
    •  
      • 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
    •  
      • 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
    •  
      • results in conscious experience
        •  
          • decision of what is observed
          • planning how to respond
      • a continuous train of such experiences constitutes consciousness

 

  • so set  Goal like :

 

  • Reinforcement 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

 

  • Motivated Learning

 

  • 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

 

 finally reach goal like this…:

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