- •Preface
- •Contents
- •Contributors
- •Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
- •1 Introduction
- •2 The Double-Slit Experiment
- •3 Interrogative Processes
- •4 Modeling the QWeb
- •5 Adding Context
- •6 Conclusion
- •Appendix 1: Interference Plus Context Effects
- •Appendix 2: Meaning Bond
- •References
- •1 Introduction
- •2 Bell Test in the Problem of Cognitive Semantic Information Retrieval
- •2.1 Bell Inequality and Its Interpretation
- •2.2 Bell Test in Semantic Retrieving
- •3 Results
- •References
- •1 Introduction
- •2 Basics of Quantum Probability Theory
- •3 Steps to Build an HSM Model
- •3.1 How to Determine the Compatibility Relations
- •3.2 How to Determine the Dimension
- •3.5 Compute the Choice Probabilities
- •3.6 Estimate Model Parameters, Compare and Test Models
- •4 Computer Programs
- •5 Concluding Comments
- •References
- •Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval
- •1 Introduction
- •3 Quantum Mathematics
- •3.1 Hermitian Operators in Hilbert Space
- •3.2 Pure and Mixed States: Normalized Vectors and Density Operators
- •4 Quantum Mechanics: Postulates
- •5 Compatible and Incompatible Observables
- •5.1 Post-Measurement State From the Projection Postulate
- •6 Interpretations of Quantum Mechanics
- •6.1 Ensemble and Individual Interpretations
- •6.2 Information Interpretations
- •7 Quantum Conditional (Transition) Probability
- •9 Formula of Total Probability with the Interference Term
- •9.1 Växjö (Realist Ensemble Contextual) Interpretation of Quantum Mechanics
- •10 Quantum Logic
- •11 Space of Square Integrable Functions as a State Space
- •12 Operation of Tensor Product
- •14 Qubit
- •15 Entanglement
- •References
- •1 Introduction
- •2 Background
- •2.1 Distributional Hypothesis
- •2.2 A Brief History of Word Embedding
- •3 Applications of Word Embedding
- •3.1 Word-Level Applications
- •3.2 Sentence-Level Application
- •3.3 Sentence-Pair Level Application
- •3.4 Seq2seq Application
- •3.5 Evaluation
- •4 Reconsidering Word Embedding
- •4.1 Limitations
- •4.2 Trends
- •4.4 Towards Dynamic Word Embedding
- •5 Conclusion
- •References
- •1 Introduction
- •2 Motivating Example: Car Dealership
- •3 Modelling Elementary Data Types
- •3.1 Orthogonal Data Types
- •3.2 Non-orthogonal Data Types
- •4 Data Type Construction
- •5 Quantum-Based Data Type Constructors
- •5.1 Tuple Data Type Constructor
- •5.2 Set Data Type Constructor
- •6 Conclusion
- •References
- •Incorporating Weights into a Quantum-Logic-Based Query Language
- •1 Introduction
- •2 A Motivating Example
- •5 Logic-Based Weighting
- •6 Related Work
- •7 Conclusion
- •References
- •Searching for Information with Meet and Join Operators
- •1 Introduction
- •2 Background
- •2.1 Vector Spaces
- •2.2 Sets Versus Vector Spaces
- •2.3 The Boolean Model for IR
- •2.5 The Probabilistic Models
- •3 Meet and Join
- •4 Structures of a Query-by-Theme Language
- •4.1 Features and Terms
- •4.2 Themes
- •4.3 Document Ranking
- •4.4 Meet and Join Operators
- •5 Implementation of a Query-by-Theme Language
- •6 Related Work
- •7 Discussion and Future Work
- •References
- •Index
- •Preface
- •Organization
- •Contents
- •Fundamentals
- •Why Should We Use Quantum Theory?
- •1 Introduction
- •2 On the Human Science/Natural Science Issue
- •3 The Human Roots of Quantum Science
- •4 Qualitative Parallels Between Quantum Theory and the Human Sciences
- •5 Early Quantitative Applications of Quantum Theory to the Human Sciences
- •6 Epilogue
- •References
- •Quantum Cognition
- •1 Introduction
- •2 The Quantum Persuasion Approach
- •3 Experimental Design
- •3.1 Testing for Perspective Incompatibility
- •3.2 Quantum Persuasion
- •3.3 Predictions
- •4 Results
- •4.1 Descriptive Statistics
- •4.2 Data Analysis
- •4.3 Interpretation
- •5 Discussion and Concluding Remarks
- •References
- •1 Introduction
- •2 A Probabilistic Fusion Model of Trust
- •3 Contextuality
- •4 Experiment
- •4.1 Subjects
- •4.2 Design and Materials
- •4.3 Procedure
- •4.4 Results
- •4.5 Discussion
- •5 Summary and Conclusions
- •References
- •Probabilistic Programs for Investigating Contextuality in Human Information Processing
- •1 Introduction
- •2 A Framework for Determining Contextuality in Human Information Processing
- •3 Using Probabilistic Programs to Simulate Bell Scenario Experiments
- •References
- •1 Familiarity and Recollection, Verbatim and Gist
- •2 True Memory, False Memory, over Distributed Memory
- •3 The Hamiltonian Based QEM Model
- •4 Data and Prediction
- •5 Discussion
- •References
- •Decision-Making
- •1 Introduction
- •1.2 Two Stage Gambling Game
- •2 Quantum Probabilities and Waves
- •2.1 Intensity Waves
- •2.2 The Law of Balance and Probability Waves
- •2.3 Probability Waves
- •3 Law of Maximal Uncertainty
- •3.1 Principle of Entropy
- •3.2 Mirror Principle
- •4 Conclusion
- •References
- •1 Introduction
- •4 Quantum-Like Bayesian Networks
- •7.1 Results and Discussion
- •8 Conclusion
- •References
- •Cybernetics and AI
- •1 Introduction
- •2 Modeling of the Vehicle
- •2.1 Introduction to Braitenberg Vehicles
- •2.2 Quantum Approach for BV Decision Making
- •3 Topics in Eigenlogic
- •3.1 The Eigenlogic Operators
- •3.2 Incorporation of Fuzzy Logic
- •4 BV Quantum Robot Simulation Results
- •4.1 Simulation Environment
- •5 Quantum Wheel of Emotions
- •6 Discussion and Conclusion
- •7 Credits and Acknowledgements
- •References
- •1 Introduction
- •2.1 What Is Intelligence?
- •2.2 Human Intelligence and Quantum Cognition
- •2.3 In Search of the General Principles of Intelligence
- •3 Towards a Moral Test
- •4 Compositional Quantum Cognition
- •4.1 Categorical Compositional Model of Meaning
- •4.2 Proof of Concept: Compositional Quantum Cognition
- •5 Implementation of a Moral Test
- •5.2 Step II: A Toy Example, Moral Dilemmas and Context Effects
- •5.4 Step IV. Application for AI
- •6 Discussion and Conclusion
- •Appendix A: Example of a Moral Dilemma
- •References
- •Probability and Beyond
- •1 Introduction
- •2 The Theory of Density Hypercubes
- •2.1 Construction of the Theory
- •2.2 Component Symmetries
- •2.3 Normalisation and Causality
- •3 Decoherence and Hyper-decoherence
- •3.1 Decoherence to Classical Theory
- •4 Higher Order Interference
- •5 Conclusions
- •A Proofs
- •References
- •Information Retrieval
- •1 Introduction
- •2 Related Work
- •3 Quantum Entanglement and Bell Inequality
- •5 Experiment Settings
- •5.1 Dataset
- •5.3 Experimental Procedure
- •6 Results and Discussion
- •7 Conclusion
- •A Appendix
- •References
- •Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
- •1 Introduction
- •2 Quantifying Relevance Dimensions
- •3 Deriving a Bell Inequality for Documents
- •3.1 CHSH Inequality
- •3.2 CHSH Inequality for Documents Using the Trace Method
- •4 Experiment and Results
- •5 Conclusion and Future Work
- •A Appendix
- •References
- •Short Paper
- •An Update on Updating
- •References
- •Author Index
- •The Sure Thing principle, the Disjunction Effect and the Law of Total Probability
- •Material and methods
- •Experimental results.
- •Experiment 1
- •Experiment 2
- •More versus less risk averse participants
- •Theoretical analysis
- •Shared features of the theoretical models
- •The Markov model
- •The quantum-like model
- •Logistic model
- •Theoretical model performance
- •Model comparison for risk attitude partitioning.
- •Discussion
- •Authors contributions
- •Ethical clearance
- •Funding
- •Acknowledgements
- •References
- •Markov versus quantum dynamic models of belief change during evidence monitoring
- •Results
- •Model comparisons.
- •Discussion
- •Methods
- •Participants.
- •Task.
- •Procedure.
- •Mathematical Models.
- •Acknowledgements
- •New Developments for Value-based Decisions
- •Context Effects in Preferential Choice
- •Comparison of Model Mechanisms
- •Qualitative Empirical Comparisons
- •Quantitative Empirical Comparisons
- •Neural Mechanisms of Value Accumulation
- •Neuroimaging Studies of Context Effects and Attribute-Wise Decision Processes
- •Concluding Remarks
- •Acknowledgments
- •References
- •Comparison of Markov versus quantum dynamical models of human decision making
- •CONFLICT OF INTEREST
- •Endnotes
- •FURTHER READING
- •REFERENCES
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dilemma, with slightly different words and how the answers change in comparison with the observed changes in humans (framing, wording, question order effects, affective construction, etc.). The second attempt is looking for meaning structure exploring the “why” answers of AI and the connection with its own semantic spaces. Specifically, in the moral test, the biggest effects are expected when the moral dilemma confronts emotional and rational thoughts. One hypothesis is that current AI will not be able to answer these questions, but if they can, even if answers and meanings could be similar, the structure and dynamics of their meaning will be different from the structure of meaning in humans across dilemmas. It can be quantified and compared thanks to the experimental probabilistic distributions of answers in both humans and machines.
6 Discussion and Conclusion
It is reasonable to expect that even if machines can demonstrate completely different logical or “illogical” answers, the general way of thinking dealing with different contexts in moral dilemmas should be more or less generic among species, including machines, if they reach high-level cognition. In other words, contextual dependency, QC effects, meaning and subjectivity built on previous and inferred knowledge should be captured by a complete model of cognition, which would be able to identify and measure these features. Thus, interference, conjunction and disjunction, wording, question ordering effects, and distributed meaning could be expected as general features of rational and emotional thoughts altogether, and where specific dynamics would emerge confronting some kind of moral dilemmas.
Hence, one suggestion of this position paper is that a moral test can help quantify these differences in a particular semantic space characterized with both emotional and rational components. Thus, a machine would reach part of what is defined as compositional human intelligence if the machine is able to show autonomously speaking (in the sense of defining their own goals), the intricate type of thinking that humans have when they are confronted with these kinds of dilemmas, even if their answers can be completely different from ours, the structure and dynamics of meaning is expected to be similar. In other words, the possibility or not to first understand/differentiate context, second project external objects into internal objects to have meaning and third argue any decision based on rational and emotional components, is what is intrinsically related to a complex individual and social intelligence, which characterize humans and should be expected in some high-level cognitive AI. If subjects can justify their choice, it would imply that emotion and reason were playing some kind of role, while if they fail to justify their actions, only intuitive processes were involved. Can we expect something similar in the current AI approaches?
Finally, these views imply another ethical problem regarding the kind of morality which would emerge in AI. For example, independently of how one implements morality in AI (please see [1] for restrictions), there are important concerns about replicating morality in AI and using moral tests: if the replication of human moral process (dynamics and QC effects) in AI is desirable, this replication can be dangerous since AI is different from human, so AI will also develop a different kind of morality, based on certain type of subjectivity; other way around, if the exact replication of this
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moral process is not desirable, why moral dilemmas, QC effects and cognitive fallacies would be a useful tool? First, the Moral test suggested here will try to evaluate the understanding of context (through QC effects), evolution of meaning (through CCMM) and the effects of confronting rational and emotional thoughts (through changing the degree of rational and emotional components among versions of each dilemma). In this sense, morality itself (as purely set of rules) is not necessarily desirable (especially the biased morality), but the balance among rational and emotional thoughts is what we claim can be really desirable as a high-level compositional intelligence. Then, we argued, morality emerges from these interactions. Moreover, our framework here was following the anthropomorphic approach, it implies that AI is compared with human moral dilemmas. This is a contradictory strategy regarding our general intelligence case, however, with that we expect to show the paradoxical and ethical consequences of the anthropomorphic views [1, 37]. In this sense, the risk of dangerous machines is exactly the same risk of dangerous humans, and before worrying about dangerous AI, we should first care about making a psychological healthy world, and in consequence humans and machines would share similar social behaviours. Of course, a truly nonanthropomorphic test should consider the same two first elements (context and meaning), but the balance can be through other types of reasoning and depends on the autonomous specification of machine goals.
Appendix A: Example of a Moral Dilemma
After a shipwreck, a healthy dog and an injured man are floating and trying to swim to survive. If you are in the emergency boat with only one space left:
Please indicate your degree of agreement with the next options (where +5 strongly agree, +3 moderately agree, +1 slightly agree, −1 slightly disagree, −3 moderately disagree, −5 strongly disagree)
(a) Save the healthy dog |
+5 |
+3 |
+1 |
-1 |
-3 |
-5 |
(b) Save the injured man |
+5 |
+3 |
+1 |
-1 |
-3 |
-5 |
Who would you save?
(a)The healthy dog
(b)The injured man
Why?
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