SlideShare una empresa de Scribd logo
1 de 38
Descargar para leer sin conexión
Illinois Genetic Algorithms Laboratory
     Department of General Engineering
     University of Illinois at Urbana-Champaign
     Urbana, IL 61801.




Searle, Intentionality, and the
Future of Classifier Systems
                    David E. Goldberg
     Illinois Genetic Algorithms Laboratory
   University of Illinois at Urbana-Champaign
                Urbana, IL 61801
                  deg@uiuc.edu
1980 v. Now
    Remember thinking how cool LCSs


    were.
    Just apply them to gas pipelines


    and voila, all AI problems of
    Western Civilization would be
    solved.
    Started to ask John for examples


    of successful application.
    Found out that I was in the


    middle of an interesting idea, not
    a working computer program.
                                         John H. Holland (b. 1929)
Roadmap
    Are we happy with LCSs?


    What’s Searle got to do with it.


    Revisiting the Chinese room.


    Art Burkes had it right.


    Designing a conscious computer.


    Searlean program for LCSs:


        Computational consciousness not impossible.
    –
        From consciousness to intentionality.
    –
        Intentionality and beyond.
    –

    What are we missing?


    What should we do?

Are We Happy With LCSs?
    Have made progress:


        Increasingly competent, solve hard problems
    –
        quickly reliably and accurately.
        Principled manner.
    –

    But don’t seem very intelligent:


        Do what we tell them.
    –

        Not autonomous in any serious sense.
    –

        Our discussions are largely technical.
    –

        Are we focused on right problems?
    –
What’s Searle Got to Do With It?
     Mill Prof of Philosophy of Berkeley.
 
     Philosopher of language and mind.
 

     Early work took off from Austin’s work on
 
     speech acts.
     Searle is Darth Vader of artificial
 
     intelligence.
     Notorious Chinese Room argument has
 
     always puzzled me.
     In many ways, Searle is high philosophical
 
     priest of emergence.
                                                  John R. Searle (b. 1932)
     Rejects dualism & materialism.
 
     Couldn’t understand how he could miss
 
     possibility of more than mere systactical
     translation.
The Chinese Room Argument

    Strong AI is not possible on a computer.


    Monolingual English speaker in a room with


        Chinese writing (story)
    –

        2nd Chinese symbols (questions)
    –

        Instructions in English for relating first set of symbols
    –
        to second.
        3rd set of Chinese symbols (answers)
    –

    English speaker does not understand Chinese even


    if answers are indistinguishable from those of
    Chinese speaker.
Cracks in the Chinese Room

    Mind, Language & Society,

    Basic Books, 1998, p. 53.
    “When I say that the brain

    is a biological organ and
    consciousness a biological
    process, I do not, of course,
    say or imply that it would
    be impossible to produce an
    artificial brain out of
    nonbiological materials.”
More Searle

    “The heart is also a biological organ, and the


    pumping of blood a biological process, but it is
    possible to build an artificial heart that pumps
    blood. There is no reason, in principle, why we
    could not similarly make an artificial brain that
    causes consciousness.”
    Searle was complaining about direct approach to


    intelligence.
    Without consciousness and intentionality there


    cannot be intelligence.
    How do we create an intelligent, conscious being?

Arthur Burks Had Interesting Take

      Robots and Free
  
      Minds, University of
      Michigan, 1986.
      “Tonight I will
  
      advocate the thesis: A
      FINITE
      DETERMINISTIC
      AUTOMATON CAN
      PERFORM ALL
      NATURAL HUMAN
      FUNCTIONS.”
Chapter 5: Evolution and Intentionality

      “The course of biological evolution from cells
  
      to Homo sapiens has been a gradual
      development of intentional systems from
      direct-response systems.”
      “The [intentional] system contains a model of
  
      its present status in relation to its goal and
      regularly updates that model on the basis of
      the information it receives. Finally, it decides
      what to do after consulting a strategy, which
      has value assessments attached in to various
      alternative courses of action.”
CS-1 Had Bio/Psycho Roots
    CS-1 had reservoirs for

    hunger and thirst (Holland
    & Reitman, 1978).
    Schemata processors

    paper had reservoirs, too
    (Holland, 1971).
    CS-1 worked in maze

    running task.
    But design was Lockean.


    Tabula rasa for everything

    except rule firing,
    apportionment of credit,
    and rule discovery.
    Is this enough?

    Thesis: Can’t take shortcut

    around consciousness and
    intentionality.
So You Want a Conscious Computer

      What does this mean?
  

      Consciousness is complex, emergent
  
      phenomenon.
      How can we design it?
  

      Don’t throw pieces together and hope for
  
      the best.
      My experience: Emergent phenomena
  
      emerge when (a) key elements are present
      and (b) system tuned properly.
      Consider more Searle.
  
Shooting for C Not Crazy

    Shooting for GA competence was crazy.


    Have accomplished.


    How:


        Considered essential elements.
    –

        Built qual/quant theories of how they worked.
    –

        Designed until limits of performance achieved.
    –

    Can do the same for

    consciousness/intentionality!!
Searle’s Greatest Hits

    Mind as biological phenomenon.


    Function of consciousness.


    Features of consciousness.


    How the mind works: Intentionality.


    The good stuff comes from intentionality:

    Language & other institutional fact.
    What are we missing?

Mind as Biology
    Consciousness is the primary feature of

    minds.
    3 features of consciousness:

        Inner: in body and in sequence of events.
    –
        Qualitative: certain way they feel.
    –
        Subjective: first person ontology (does not
    –
        preclude objective epistemology).
    Enormous variety of consciousness: smell a

    rose, worry about income taxes, sudden
    rage about driver, etc.
Functions of Consciousness
    What does it do? What is survival value?


    What doesn’t it do for our species?


    Consciousness is central to our survival.


    All actions a result of conscious thought

    followed by action.
Consciousness, Intentionality, & Causation

      Represent world, and act on representations.
  

      Intentional causation: Not billiard ball causation.
  

      Not all consciousness intentionally causal, but much
  
      is.
      Should be best understood; are we not in touch
  
      with it always? Descartes’s error.
      Yet difficult to describe: Can describe objects,
  
      moods, thoughts, but not C itself.
      Problems:
  

          Not itself an object of observation (consciousness
      –
          observes but is not observed).
          Tradition of separating mind/body: dualism.
      –
Features of C
      Ontological subjectivity.
1.

      C comes in unified form. Thinking and feeling go on
2.

      at same time in same field of C: Vertical & horizontal.
      C connects us to world (Tie to intentionality).
3.

      C states come in moods.
4.

      Always structured.
5.

      Varying degrees of attention.
6.

      C is situated.
7.

      Varying degrees of familiarity.
8.

      Refer to other things
9.

      Always pleasurable or unpleasurable
10.
How the Mind Works: Intentionality

      Primary evolutionary role of C is to relate
  
      us to environment.
      Cannot eliminate intentionality of mind by
  
      appealing to language; already
      intentionality of the mind.
      Searle: Urge to reduce it to something else
  
      is faulty.
      DEG: As designers we need to reduce it to
  
      something and then find conditions of
      emergence among those things.
Intentionality as Biology

    Thirst, hunger as basic, causing desire to

    drink or eat.
    Once this granted, camel nose under the

    tent, intentions based on other sensory.
    Isn’t reality “confirmed” by our “success” in

    achieving intentional goals over and over
    again.
Structure of Intentional States

    Intentionality as way mental states are directed at

    objects & states of affairs.
    Can be directed at things that don’t exist?


    How can this be?


    Distinguish between type of intentional state and

    content.
    Content: rain; Types: hope, believe, fear rain.


    Structural features:


        Direction of fit
    –
        Conditions of satisfaction
    –
Direction of Fit
    Term: from Austin, foreshadowed by Wittgenstein,


    examples Anscombe.
    Anscombe’s lists:


        Shopping list: Beer, butter, bacon. Husband matches
    –
        world to list.
        Detective’s list: Follows shopper, “beer, butter,
    –
        bacon,” matches list to world.
    Not all intentional states like this: e.g. when you


    are sorry, assume match between mind and world.
    Intentional state is null.
Conditions of Satisfaction
    Beliefs can be true or false.


    Goals can be achieved or not.


    Easier to understand in terms of speech acts.


    Have 5 illocutionary points or types:


         Assertive: commit to the truth.
     –

         Directive: direct hearer to do something.
     –

         Commissive: speaker promises to do something.
     –

         Expressive: speaker expresses opinion about state of
     –
         the world.
         Declarations: speaker creates something with
     –
         utterance.
Intentional Causation
    Intend to move body  body moves:

    Example of intentional causation.
    Differs from billiard ball or Humean causation.


    Self-referential: intend to move body, body moves

    because I intended  then intentional causation.
    Critical to distinguishing natural versus social

    sciences.
    Intentional explanations not deterministic: Could

    have done otherwise  gap is free will.
Good Stuff from Intentionality

    Searle goes on to talk about language and

    institutional facts (money, college degrees,
    etc.).
    Disappointment with LCS is it can’t get to

    the good stuff.
    Can’t do language.


    Can’t form contracts.


    Can’t create new institutional fact.

Construction of Social Reality

    Need to clarify observer-independent &

    observer-dependent features of the world.
    Need 3 new elements:


        Collective intentionality.
    –

        Assignment of function.
    –

        Constitutive rules
    –
Observer Independent v. Dependent

       Many features of the world independent of
   
       our observations of them: observer
       independence.
       Many observer dependent: Something a
   
       characteristic because of observer
       judgment, but not relative to others.
       OI vs. OD more important than mind-body.
   

       DEG aside: Isn’t it dualism in the back door
   
       though?
Collective Intentionality
    Need the notion of “we intend together.”


    Attempts to reduce to individual intention are

    complex.
    Existence of biological organisms with collective

    intentionality suggests CI is a primitive.
    DEG aside: Are social insects intentional in Searlean

    sense? Could be that social affiliation is primitive,
    certain behaviors hard wired. Then, CI results from
    (a) naming the group, (b) attributing intention to it
    (as-if intentionality), and (c) treating the as-if as
    real.
Assignment of Function

    Use of objects as tools:


        Monkey uses stick to get banana.
    –

        Man sits on rock.
    –

    Physical existence facilitates function, but

    function is observer relative.
    All function assignment is observer relative.

Constitutive Rules

    How to distinguish between brute facts and


    institutional facts.
    Types of rules:


        Some rules regulate: “Drive on right side of road.”
    –

        Some rules regulate and constitute: Rules of chess
    –
        both regulate conduct of game and create it.
    Constitutive rules have form: X counts as Y in C.


    “Move two and over one” counts as a knight’s


    move in Chess.”
Simple Model of Construction of Social Reality

        Strong thesis: All institutional reality explained by 3
    
        things:
            Collective intentionality.
        –
            Assigned function  wall keeps people out
        –
            physically, but low fence or boundary marker keeps
            people out by convention.
            Constitutive rules.
        –
        Money example: Evolution from valuable
    
        commodity to fiat currency.
        Institutional reality powerful: X counts as Y in C can
    
        be iterated and stacked forming powerful network
        of institutional facts.
What Are We Missing?

    Do not have C-machines.


    Searle’s 10:


          Ontological subjectivity.
    1.
          C comes in unified form.
    2.
          C connects us to world.
    3.
          C states come in moods.
    4.
          Always structured.
    5.
          Varying degrees of attention.
    6.
          C is situated.
    7.
          Varying degrees of familiarity.
    8.
          Refer to other things
    9.
          Always pleasurable or unpleasurable
    10.
Unity Missing

    Can argue that we have vertical unity in

    message board.
    Do not have horizontal unity.


    My first proposal recommended

    modifications to permit time series.
    Modifications to rules.


    Modification to the boards.

Moods & Pleasant/Unpleasant Missing

      This is big.
  

      Emotions are “engagement with the world”
  
      (Solomon).
      Necessary for judgment and values.
  

      Don’t want a simulation.
  

      Emotions:
  

          Physiological component
      –

          Judgmental component
      –
Other Things Missing

    Attention


    Gestalt structure


    Situatedness & familiarity


    Refer to other things (may have this)

What Should We Do?

    Stuff we’ve gotten right: Sensors, association,

    models (anticipation), learning
    Can’t continue to work on same thing.


    No serious architectural changes proposed to LCS.

    Why?
    Need:


        Emotions: As judgments, source of values, and
    –
        arbiter of attention.
        Multiple boards: As source of difference and
    –
        similarity. Main hope of quality of consciousness &
        unity.
        Center of intention rooted in “biological needs.”
    –
How Do We Break This Down?

    Tough problem.


    If C is complex building block,

    what are minimal essential
    elements to achieve.
    How do we know we’ve achieved

    it (first person ontology, third
    person epistomology)?
    Sets of tests and experiments.


    What theory needed to set

    parameters of C?
    Not unlike approach that cracked

    innovation
Summary & Conclusions

    Have accomplished quite a bit in classifier

    systems.
    Many of our questions are technical.


    Deeper questions about whether we’re

    attacking the right questions.
    Need consciousness and intention to get

    the “good stuff” of intelligent behavior.
    Wrestling with Searle’s categories not a bad

    place to start.

Más contenido relacionado

Similar a Searle, Intentionality, and the Future of Classifier Systems

Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxChapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
robertad6
 
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxChapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
mccormicknadine86
 
Critical Thinking Chesapeake Energy
Critical Thinking Chesapeake EnergyCritical Thinking Chesapeake Energy
Critical Thinking Chesapeake Energy
ProfessorUrich
 
introduction to cognition
introduction to cognitionintroduction to cognition
introduction to cognition
Anju Gautam
 
The Subtle Art of Persuasion
The Subtle Art of PersuasionThe Subtle Art of Persuasion
The Subtle Art of Persuasion
James Box
 
Sensation and perception_2012
Sensation and perception_2012Sensation and perception_2012
Sensation and perception_2012
laylay27
 

Similar a Searle, Intentionality, and the Future of Classifier Systems (20)

Mind Models (Minsky, Pinker, Hawkins)
Mind Models (Minsky, Pinker, Hawkins)Mind Models (Minsky, Pinker, Hawkins)
Mind Models (Minsky, Pinker, Hawkins)
 
13 anderson
13 anderson13 anderson
13 anderson
 
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxChapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
 
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxChapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docx
 
Can we understand consciousness
Can we understand consciousnessCan we understand consciousness
Can we understand consciousness
 
Foundations Of Knowledge
Foundations Of KnowledgeFoundations Of Knowledge
Foundations Of Knowledge
 
Creative Energy workbook
Creative Energy workbookCreative Energy workbook
Creative Energy workbook
 
Critical Thinking Chesapeake Energy
Critical Thinking Chesapeake EnergyCritical Thinking Chesapeake Energy
Critical Thinking Chesapeake Energy
 
Introduction
IntroductionIntroduction
Introduction
 
Why Right-Brainers Will Rule The Future
Why Right-Brainers Will Rule The FutureWhy Right-Brainers Will Rule The Future
Why Right-Brainers Will Rule The Future
 
Ability of Creativity
Ability of CreativityAbility of Creativity
Ability of Creativity
 
introduction to cognition
introduction to cognitionintroduction to cognition
introduction to cognition
 
1810.mid1043.07
1810.mid1043.071810.mid1043.07
1810.mid1043.07
 
The Subtle Art of Persuasion
The Subtle Art of PersuasionThe Subtle Art of Persuasion
The Subtle Art of Persuasion
 
Effective Decision Making
Effective Decision MakingEffective Decision Making
Effective Decision Making
 
Persuasive Technology 2009 Summary
Persuasive Technology 2009 SummaryPersuasive Technology 2009 Summary
Persuasive Technology 2009 Summary
 
The Science and Practice of Brain Fitness
The Science and Practice of Brain FitnessThe Science and Practice of Brain Fitness
The Science and Practice of Brain Fitness
 
Sensation and perception_2012
Sensation and perception_2012Sensation and perception_2012
Sensation and perception_2012
 
Yates kluge
Yates klugeYates kluge
Yates kluge
 
Social Thinking
Social ThinkingSocial Thinking
Social Thinking
 

Más de Xavier Llorà

Large Scale Data Mining using Genetics-Based Machine Learning
Large Scale Data Mining using   Genetics-Based Machine LearningLarge Scale Data Mining using   Genetics-Based Machine Learning
Large Scale Data Mining using Genetics-Based Machine Learning
Xavier Llorà
 

Más de Xavier Llorà (20)

Meandre 2.0 Alpha Preview
Meandre 2.0 Alpha PreviewMeandre 2.0 Alpha Preview
Meandre 2.0 Alpha Preview
 
Soaring the Clouds with Meandre
Soaring the Clouds with MeandreSoaring the Clouds with Meandre
Soaring the Clouds with Meandre
 
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
 
Large Scale Data Mining using Genetics-Based Machine Learning
Large Scale Data Mining using   Genetics-Based Machine LearningLarge Scale Data Mining using   Genetics-Based Machine Learning
Large Scale Data Mining using Genetics-Based Machine Learning
 
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Data-Intensive Computing for  Competent Genetic Algorithms:  A Pilot Study us...Data-Intensive Computing for  Competent Genetic Algorithms:  A Pilot Study us...
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
 
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new TrendsScalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
 
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Pittsburgh Learning Classifier Systems for Protein  Structure Prediction: Sca...Pittsburgh Learning Classifier Systems for Protein  Structure Prediction: Sca...
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
 
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Towards a Theoretical  Towards a Theoretical  Framework for LCS  Framework fo...Towards a Theoretical  Towards a Theoretical  Framework for LCS  Framework fo...
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
 
Learning Classifier Systems for Class Imbalance Problems
Learning Classifier Systems  for Class Imbalance  ProblemsLearning Classifier Systems  for Class Imbalance  Problems
Learning Classifier Systems for Class Imbalance Problems
 
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
A Retrospective Look at  A Retrospective Look at  Classifier System ResearchCl...A Retrospective Look at  A Retrospective Look at  Classifier System ResearchCl...
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
 
XCS: Current capabilities and future challenges
XCS: Current capabilities and future  challengesXCS: Current capabilities and future  challenges
XCS: Current capabilities and future challenges
 
Negative Selection for Algorithm for Anomaly Detection
Negative Selection for Algorithm for Anomaly DetectionNegative Selection for Algorithm for Anomaly Detection
Negative Selection for Algorithm for Anomaly Detection
 
Computed Prediction: So far, so good. What now?
Computed Prediction:  So far, so good. What now?Computed Prediction:  So far, so good. What now?
Computed Prediction: So far, so good. What now?
 
NIGEL 2006 welcome
NIGEL 2006 welcomeNIGEL 2006 welcome
NIGEL 2006 welcome
 
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Linkage Learning for Pittsburgh LCS: Making Problems TractableLinkage Learning for Pittsburgh LCS: Making Problems Tractable
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
 
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Meandre: Semantic-Driven Data-Intensive Flows in the CloudsMeandre: Semantic-Driven Data-Intensive Flows in the Clouds
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
 
ZigZag: The Meandring Language
ZigZag: The Meandring LanguageZigZag: The Meandring Language
ZigZag: The Meandring Language
 
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
 
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
 
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
 

Último

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Último (20)

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 

Searle, Intentionality, and the Future of Classifier Systems

  • 1. Illinois Genetic Algorithms Laboratory Department of General Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801. Searle, Intentionality, and the Future of Classifier Systems David E. Goldberg Illinois Genetic Algorithms Laboratory University of Illinois at Urbana-Champaign Urbana, IL 61801 deg@uiuc.edu
  • 2. 1980 v. Now Remember thinking how cool LCSs  were. Just apply them to gas pipelines  and voila, all AI problems of Western Civilization would be solved. Started to ask John for examples  of successful application. Found out that I was in the  middle of an interesting idea, not a working computer program. John H. Holland (b. 1929)
  • 3. Roadmap Are we happy with LCSs?  What’s Searle got to do with it.  Revisiting the Chinese room.  Art Burkes had it right.  Designing a conscious computer.  Searlean program for LCSs:  Computational consciousness not impossible. – From consciousness to intentionality. – Intentionality and beyond. – What are we missing?  What should we do? 
  • 4. Are We Happy With LCSs? Have made progress:  Increasingly competent, solve hard problems – quickly reliably and accurately. Principled manner. – But don’t seem very intelligent:  Do what we tell them. – Not autonomous in any serious sense. – Our discussions are largely technical. – Are we focused on right problems? –
  • 5. What’s Searle Got to Do With It? Mill Prof of Philosophy of Berkeley.  Philosopher of language and mind.  Early work took off from Austin’s work on  speech acts. Searle is Darth Vader of artificial  intelligence. Notorious Chinese Room argument has  always puzzled me. In many ways, Searle is high philosophical  priest of emergence. John R. Searle (b. 1932) Rejects dualism & materialism.  Couldn’t understand how he could miss  possibility of more than mere systactical translation.
  • 6. The Chinese Room Argument Strong AI is not possible on a computer.  Monolingual English speaker in a room with  Chinese writing (story) – 2nd Chinese symbols (questions) – Instructions in English for relating first set of symbols – to second. 3rd set of Chinese symbols (answers) – English speaker does not understand Chinese even  if answers are indistinguishable from those of Chinese speaker.
  • 7. Cracks in the Chinese Room Mind, Language & Society,  Basic Books, 1998, p. 53. “When I say that the brain  is a biological organ and consciousness a biological process, I do not, of course, say or imply that it would be impossible to produce an artificial brain out of nonbiological materials.”
  • 8. More Searle “The heart is also a biological organ, and the  pumping of blood a biological process, but it is possible to build an artificial heart that pumps blood. There is no reason, in principle, why we could not similarly make an artificial brain that causes consciousness.” Searle was complaining about direct approach to  intelligence. Without consciousness and intentionality there  cannot be intelligence. How do we create an intelligent, conscious being? 
  • 9. Arthur Burks Had Interesting Take Robots and Free  Minds, University of Michigan, 1986. “Tonight I will  advocate the thesis: A FINITE DETERMINISTIC AUTOMATON CAN PERFORM ALL NATURAL HUMAN FUNCTIONS.”
  • 10. Chapter 5: Evolution and Intentionality “The course of biological evolution from cells  to Homo sapiens has been a gradual development of intentional systems from direct-response systems.” “The [intentional] system contains a model of  its present status in relation to its goal and regularly updates that model on the basis of the information it receives. Finally, it decides what to do after consulting a strategy, which has value assessments attached in to various alternative courses of action.”
  • 11. CS-1 Had Bio/Psycho Roots CS-1 had reservoirs for  hunger and thirst (Holland & Reitman, 1978). Schemata processors  paper had reservoirs, too (Holland, 1971). CS-1 worked in maze  running task. But design was Lockean.  Tabula rasa for everything  except rule firing, apportionment of credit, and rule discovery. Is this enough?  Thesis: Can’t take shortcut  around consciousness and intentionality.
  • 12. So You Want a Conscious Computer What does this mean?  Consciousness is complex, emergent  phenomenon. How can we design it?  Don’t throw pieces together and hope for  the best. My experience: Emergent phenomena  emerge when (a) key elements are present and (b) system tuned properly. Consider more Searle. 
  • 13. Shooting for C Not Crazy Shooting for GA competence was crazy.  Have accomplished.  How:  Considered essential elements. – Built qual/quant theories of how they worked. – Designed until limits of performance achieved. – Can do the same for  consciousness/intentionality!!
  • 14. Searle’s Greatest Hits Mind as biological phenomenon.  Function of consciousness.  Features of consciousness.  How the mind works: Intentionality.  The good stuff comes from intentionality:  Language & other institutional fact. What are we missing? 
  • 15. Mind as Biology Consciousness is the primary feature of  minds. 3 features of consciousness:  Inner: in body and in sequence of events. – Qualitative: certain way they feel. – Subjective: first person ontology (does not – preclude objective epistemology). Enormous variety of consciousness: smell a  rose, worry about income taxes, sudden rage about driver, etc.
  • 16. Functions of Consciousness What does it do? What is survival value?  What doesn’t it do for our species?  Consciousness is central to our survival.  All actions a result of conscious thought  followed by action.
  • 17. Consciousness, Intentionality, & Causation Represent world, and act on representations.  Intentional causation: Not billiard ball causation.  Not all consciousness intentionally causal, but much  is. Should be best understood; are we not in touch  with it always? Descartes’s error. Yet difficult to describe: Can describe objects,  moods, thoughts, but not C itself. Problems:  Not itself an object of observation (consciousness – observes but is not observed). Tradition of separating mind/body: dualism. –
  • 18. Features of C Ontological subjectivity. 1. C comes in unified form. Thinking and feeling go on 2. at same time in same field of C: Vertical & horizontal. C connects us to world (Tie to intentionality). 3. C states come in moods. 4. Always structured. 5. Varying degrees of attention. 6. C is situated. 7. Varying degrees of familiarity. 8. Refer to other things 9. Always pleasurable or unpleasurable 10.
  • 19. How the Mind Works: Intentionality Primary evolutionary role of C is to relate  us to environment. Cannot eliminate intentionality of mind by  appealing to language; already intentionality of the mind. Searle: Urge to reduce it to something else  is faulty. DEG: As designers we need to reduce it to  something and then find conditions of emergence among those things.
  • 20. Intentionality as Biology Thirst, hunger as basic, causing desire to  drink or eat. Once this granted, camel nose under the  tent, intentions based on other sensory. Isn’t reality “confirmed” by our “success” in  achieving intentional goals over and over again.
  • 21. Structure of Intentional States Intentionality as way mental states are directed at  objects & states of affairs. Can be directed at things that don’t exist?  How can this be?  Distinguish between type of intentional state and  content. Content: rain; Types: hope, believe, fear rain.  Structural features:  Direction of fit – Conditions of satisfaction –
  • 22. Direction of Fit Term: from Austin, foreshadowed by Wittgenstein,  examples Anscombe. Anscombe’s lists:  Shopping list: Beer, butter, bacon. Husband matches – world to list. Detective’s list: Follows shopper, “beer, butter, – bacon,” matches list to world. Not all intentional states like this: e.g. when you  are sorry, assume match between mind and world. Intentional state is null.
  • 23. Conditions of Satisfaction Beliefs can be true or false.  Goals can be achieved or not.  Easier to understand in terms of speech acts.  Have 5 illocutionary points or types:  Assertive: commit to the truth. – Directive: direct hearer to do something. – Commissive: speaker promises to do something. – Expressive: speaker expresses opinion about state of – the world. Declarations: speaker creates something with – utterance.
  • 24. Intentional Causation Intend to move body  body moves:  Example of intentional causation. Differs from billiard ball or Humean causation.  Self-referential: intend to move body, body moves  because I intended  then intentional causation. Critical to distinguishing natural versus social  sciences. Intentional explanations not deterministic: Could  have done otherwise  gap is free will.
  • 25. Good Stuff from Intentionality Searle goes on to talk about language and  institutional facts (money, college degrees, etc.). Disappointment with LCS is it can’t get to  the good stuff. Can’t do language.  Can’t form contracts.  Can’t create new institutional fact. 
  • 26. Construction of Social Reality Need to clarify observer-independent &  observer-dependent features of the world. Need 3 new elements:  Collective intentionality. – Assignment of function. – Constitutive rules –
  • 27. Observer Independent v. Dependent Many features of the world independent of  our observations of them: observer independence. Many observer dependent: Something a  characteristic because of observer judgment, but not relative to others. OI vs. OD more important than mind-body.  DEG aside: Isn’t it dualism in the back door  though?
  • 28. Collective Intentionality Need the notion of “we intend together.”  Attempts to reduce to individual intention are  complex. Existence of biological organisms with collective  intentionality suggests CI is a primitive. DEG aside: Are social insects intentional in Searlean  sense? Could be that social affiliation is primitive, certain behaviors hard wired. Then, CI results from (a) naming the group, (b) attributing intention to it (as-if intentionality), and (c) treating the as-if as real.
  • 29. Assignment of Function Use of objects as tools:  Monkey uses stick to get banana. – Man sits on rock. – Physical existence facilitates function, but  function is observer relative. All function assignment is observer relative. 
  • 30. Constitutive Rules How to distinguish between brute facts and  institutional facts. Types of rules:  Some rules regulate: “Drive on right side of road.” – Some rules regulate and constitute: Rules of chess – both regulate conduct of game and create it. Constitutive rules have form: X counts as Y in C.  “Move two and over one” counts as a knight’s  move in Chess.”
  • 31. Simple Model of Construction of Social Reality Strong thesis: All institutional reality explained by 3  things: Collective intentionality. – Assigned function  wall keeps people out – physically, but low fence or boundary marker keeps people out by convention. Constitutive rules. – Money example: Evolution from valuable  commodity to fiat currency. Institutional reality powerful: X counts as Y in C can  be iterated and stacked forming powerful network of institutional facts.
  • 32. What Are We Missing? Do not have C-machines.  Searle’s 10:  Ontological subjectivity. 1. C comes in unified form. 2. C connects us to world. 3. C states come in moods. 4. Always structured. 5. Varying degrees of attention. 6. C is situated. 7. Varying degrees of familiarity. 8. Refer to other things 9. Always pleasurable or unpleasurable 10.
  • 33. Unity Missing Can argue that we have vertical unity in  message board. Do not have horizontal unity.  My first proposal recommended  modifications to permit time series. Modifications to rules.  Modification to the boards. 
  • 34. Moods & Pleasant/Unpleasant Missing This is big.  Emotions are “engagement with the world”  (Solomon). Necessary for judgment and values.  Don’t want a simulation.  Emotions:  Physiological component – Judgmental component –
  • 35. Other Things Missing Attention  Gestalt structure  Situatedness & familiarity  Refer to other things (may have this) 
  • 36. What Should We Do? Stuff we’ve gotten right: Sensors, association,  models (anticipation), learning Can’t continue to work on same thing.  No serious architectural changes proposed to LCS.  Why? Need:  Emotions: As judgments, source of values, and – arbiter of attention. Multiple boards: As source of difference and – similarity. Main hope of quality of consciousness & unity. Center of intention rooted in “biological needs.” –
  • 37. How Do We Break This Down? Tough problem.  If C is complex building block,  what are minimal essential elements to achieve. How do we know we’ve achieved  it (first person ontology, third person epistomology)? Sets of tests and experiments.  What theory needed to set  parameters of C? Not unlike approach that cracked  innovation
  • 38. Summary & Conclusions Have accomplished quite a bit in classifier  systems. Many of our questions are technical.  Deeper questions about whether we’re  attacking the right questions. Need consciousness and intention to get  the “good stuff” of intelligent behavior. Wrestling with Searle’s categories not a bad  place to start.