Understanding the Chinese room
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John Searle's Chinese room

Summary
John Searle's influential thought experiment implied to him that computers cannot understand.  Complex adaptive system (
This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
CAS
) theory indicates that this is not the case. 
Introduction
The philosopher John Searle developed a thought experiment to critique the possibility that digital computers can understand the data they process.  The experiment was an extension of a scenario created by Alan Turing that used a room to shield the identity of a man and woman.  In Turing's version a third person outside the room was asked to identify which of the two was the man just by asking the occupants questions.  Turing required that the woman respond truthfully, while the man must lie.  The twist in this scenario was that Turing then pointed out that a computer programmed to respond as the man or woman if successful in fooling the third person was surely intelligent. 

Searle also presents an analogy of the way a digital computer can be provided with a set of instructions which it can act on to manipulate strings of characters.  The analogy again replaces the computer by a person.

The experiment, The Chinese Room, is a story about an English speaking person sitting in a room with a book of rules about manipulations to perform on Chinese language character strings.  The English speaker does not understand Chinese language.  The rules are written in English.  To assist the person in following and executing the rules he has lots of paper, pencils and patience. 

A Chinese person passes a set of questions written in Chinese through a slot into the room.  The English speaker uses the rule book to act on the Chinese characters.  Eventually by following the rules a new string of Chinese characters is created and these the English person passes back through the slit to the Chinese speaker. 

The Chinese speaker reads the new document and is delighted to find that the questions they submitted through the slot have all been answered correctly and insightfully.  The Chinese speaker concludes that the person in the room understood the questions. 

Searle on the contrary points out that the English speaker clearly did not understand the questions, and the rule book is just a book so it did not either.  He concluded that no understanding occurred. 

When using
This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
complex adaptive system (CAS) theory
to analyze Searle's thought experiment it appears:
  1. Searle suggests that the English speaker does not understand Chinese.  However, a CAS
    Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
    It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
    The participation of agents in flows is introduced and some implications of this are outlined. 
    agent
    , using
    Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
    signals
    and a
    Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions.  The component architecture and structure of the plans is reviewed. 
    plan
    , can select appropriate actions, as the English speaker does.  We would contend that a CAS agent, under the influence of
    This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
    evolution
    , can generate plans and structures which support the agent's selection of
    The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
    models
    and actions that we equate with understanding. 
  2. Searle is ignoring key properties of the book.  The book contains rules which are, according to Searle, an effective recipe for generating insightful Chinese responses to Chinese questions.  The rules are written in English, and structured to allow the English speaker to find and execute the appropriate rule at each point. The rules have been devised by some process, which is able to specify a recipe to construct insightful responses to written Chinese questions.  When these properties of the book are included in the experiment the book can be seen to represent more than 'just a book'. 
The English speaker is a CAS
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agent
.  The book is a
Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions.  The component architecture and structure of the plans is reviewed. 
schematic plan
including a set of
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
that provide the agent with context sensitive ways to associate each particular
The complex adaptive system (CAS) nature of a value delivery system is first introduced.  It's a network of agents acting as relays. 

The critical nature of hub agents and the difficulty of altering an aligned network is reviewed. 

The nature of and exceptional opportunities created by platforms are discussed. 

Finally an example of aligning a VDS is presented. 
environmental
This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
state
with a valuable action.  The agent, rule book and its developers, paper, pencils and Chinese questions are an adaptive system. 

We would argue that the agent enables the 'system' to process the Chinese questions into effective answers because the plan and models in the book, and the representations of these in the neuron network, a network of interconnected neurons which perform signalling, modeling and control functions.  In Cajal's basic neural circuits the signalling is unidirectional.  He identified three classes of neurons in the circuits:
  • Sensory, Interneurons, Motor; which are biochemically distinct and suffer different disease states. 
of the agent have allowed the system to survive in a competitive
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
world, based on the benefits of Chinese conversation.  Once a
Richard Dawkin's explores how nature has created implementations of designs, without any need for planning or design, through the accumulation of small advantageous changes. 
mutable
schematic plan and adaptive agents have emerged only time, competition and appropriate
This page discusses the impact of random events which once they occur encourage a particular direction forward for a complex adaptive system (CAS). 
frozen accidents
are required. 

We contend that the English speaker, while provided by evolution with a powerful
This page uses an example to illustrate how:
  • A business can gain focus from targeting key customers,
  • Business planning activities performed by the whole organization can build awareness, empowerment and coherence. 
  • A program approach can ensure strategic alignment. 
perception and representation processor
, could be replaced by a machine agent, as long as the rules were appropriately transformed to provide the machine with an equivalent machine compatible recipe. 

The confusion in the thought experiment is in the apparent static nature of the book, the room, and lack of rewards and risks to the English speaking agent.  The emergence of an agent able to perform Searle's actions would have necessitated the presence of competitive advantage that the models and actions in the book provided to the agent.   The book's models and actions must have been developed over time based on the competitive advantage that they provided at each point to the developers. 

Terrence Deacon points out in 'Incomplete Nature' that a computer applying Bayesian is an iterative form of statistics invented by Thomas Bayes.  It uses a 'prior' statistic to represent the prior situation and then performs a calculation that integrates the probability of new events occurring into a 'posterior' probability.  This posterior becomes the prior for the next iteration with the application of the Bayesian identity xpost = xprior*y/(xprior*y + z(1-xprior)).  The magic in Bayesian statistics is in accurately generating the prior xprior and the current event probabilities y and z.  R. A. Fischer was so skeptical of the legitimacy of the prior that he advocated an alternative statistical framework and experimental process.   statistics to a large set of the questions being asked in the thought experiment and typical human responses to them, can also generate answers which would appear intelligent, to the Chinese speaker. 

A computer appropriately programmed with adaptive agents and including
Plans change in complex adaptive systems (CAS) due to the action of genetic operations such as mutation, splitting and recombination.  The nature of the operations is described. 
genetic operators
driving the
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
of the schematic plan and models can leverage Turing's insight to produce an artificially intelligent system.  The Chinese room experiment fails as a test of understanding. 

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This page looks at schematic structures and their uses.  It discusses a number of examples:
  • Schematic ideas are recombined in creativity. 
  • Similarly designers take ideas and rules about materials and components and combine them. 
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