Human-Like Machine Intelligence
- Length: 544 pages
- Edition: 1
- Language: English
- Publisher: Oxford University Press
- Publication Date: 2021-09-20
- ISBN-10: 0198862539
- ISBN-13: 9780198862536
- Sales Rank: #0 (See Top 100 Books)
In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning
and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s-2030s), and is being actively investigated in the US, Europe and China.
The chapters of this book have been authored by a mixture of UK and other international specialists. Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3)
learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.
Cover Human-Like Machine Intelligence Copyright Preface Acknowledgements Contents Part 1: Human-like Machine Intelligence 1: Human-Compatible Artificial Intelligence 1.1 Introduction 1.2 Artificial Intelligence 1.3 1001 Reasons to Pay No Attention 1.4 Solutions 1.4.1 Assistance games 1.4.2 The off-switch game 1.4.3 Acting with unknown preferences 1.5 Reasons for Optimism 1.6 Obstacles 1.7 Looking Further Ahead 1.8 Conclusion References 2: Alan Turing and Human-Like Intelligence 2.1 The Background to Turing’s 1936 Paper 2.2 Introducing Turing Machines 2.3 The Fundamental Ideas of Turing’s 1936 Paper 2.4 Justifying the Turing Machine 2.5 Was the Turing Machine Inspired by Human Computation? 2.6 From 1936 to 1950 2.7 Introducing the Imitation Game 2.8 Understanding the Turing Test 2.9 Does Turing’s “Intelligence” have to be Human-Like? 2.10 Reconsidering Standard Objections to the Turing Test References 3: Spontaneous Communicative Conventions through Virtual Bargaining 3.1 The Spontaneous Creation of Conventions 3.2 Communication through Virtual Bargaining 3.3 The Richness and Flexibility of Signal-Meaning Mappings 3.4 The Role of Cooperation in Communication 3.5 The Nature of the Communicative Act 3.6 Conclusions and Future Directions Acknowledgements References 4: Modelling Virtual Bargaining using Logical Representation Change 4.1 Introduction—Virtual Bargaining 4.2 What’s in the Box? 4.3 Datalog Theories 4.3.1 Clausal form 4.3.2 Datalog properties 4.3.3 Application 1: Game rules as a logic theory 4.3.4 Application 2: Signalling convention as a logic theory 4.4 SL Resolution 4.4.1 SL refutation 4.4.2 Executing the strategy 4.5 Repairing Datalog Theories 4.5.1 Fault diagnosis and repair 4.5.2 Example: The black swan 4.6 Adapting the Signalling Convention 4.6.1 ‘Avoid’ condition 4.6.2 Extended vocabulary 4.6.3 Private knowledge 4.7 Conclusion Acknowledgements References Part 2: Human-like Social Cooperation 5: Mining Property-driven Graphical Explanations for Data-centric AI from Argumentation Frameworks 5.1 Introduction 5.2 Preliminaries 5.2.1 Background: argumentation frameworks 5.2.2 Application domain 5.3 Explanations 5.4 Reasoning and Explaining with BFs Mined from Text 5.4.1 Mining BFs from text 5.4.2 Reasoning 5.4.3 Explaining 5.5 Reasoning and Explaining with AFs Mined from Labelled Examples 5.5.1 Mining AFs from examples 5.5.2 Reasoning 5.5.3 Explaining 5.6 Reasoning and Explaining with QBFs Mined from Recommender Systems 5.6.1 Mining QBFs from recommender systems 5.6.2 Explaining 5.7 Conclusions Acknowledgements References 6: Explanation in AI systems 6.1 Machine-generated Explanation 6.1.1 Bayesian belief networks: a brief introduction 6.1.2 Bayesian belief networks: explaining evidence 6.1.3 Bayesian belief networks: explaining reasoning processes 6.2 Good Explanation 6.2.1 A brief overview of models of explanation 6.2.2 Explanatory virtues 6.2.3 Implications 6.2.4 A brief case study on human-generated explanation 6.3 Bringing in the user: bi-directional relationships 6.3.1 Explanations are communicative acts 6.3.2 Explanations and trust 6.3.3 Trust and fidelity 6.3.4 Further research avenues 6.4 Conclusions Acknowledgements References 7: Human-like Communication 7.1 Introduction 7.2 Face-to-face Conversation 7.2.1 Facial expressions 7.2.2 Gesture 7.2.3 Voice 7.3 Coordinating Understanding 7.3.1 Standard average understanding 7.3.2 Misunderstandings 7.4 Real-time Adaptive Communication 7.5 Conclusion References 8: Too Many cooks: Bayesian inference for coordinating Multi-agent Collaboration 8.1 Introduction 8.2 Multi-Agent MDPs with Sub-Tasks 8.2.1 Coordination Test Suite 8.3 Bayesian Delegation 8.4 Results 8.4.1 Self-play 8.4.2 Ad-hoc 8.5 Discussion Acknowledgements References 9: Teaching and Explanation: Aligning Priors between Machines and Humans 9.1 Introduction 9.2 Teaching Size: Learner and Teacher Algorithms 9.2.1 Uniform-prior teaching size 9.2.2 Simplicity-prior teaching size 9.3 Teaching and Explanations 9.3.1 Interpretability 9.3.2 Exemplar-based explanation 9.3.3 Machine teaching for explanations 9.4 Teaching with Exceptions 9.5 Universal Case 9.5.1 Example 1: Non-iterative concept 9.5.2 Example 2: Iterative concept 9.6 Feature-value Case 9.6.1 Example 1: Concept with nominal attributes only 9.6.2 Example 2: Concept with numeric attributes 9.7 Discussion Acknowledgements References Part 3: Human-like Perception and Language 10: Human-like Computer Vision 10.1 Introduction 10.2 Related Work 10.3 Logical Vision 10.3.1 Learning geometric concepts from synthetic images 10.3.2 One-shot learning from real images 10.4 Learning Low-level Perception through Logical Abduction 10.5 Conclusion and Future Work References 11: Apperception 11.1 Introduction 11.2 Method 11.2.1 Making sense of unambiguous symbolic input 11.2.2 The Apperception Engine 11.2.3 Making sense of disjunctive symbolic input 11.2.4 Making sense of raw input 11.2.5 Applying the Apperception Engine to raw input 11.3 Experiment: Sokoban 11.3.1 The data 11.3.2 The model 11.3.3 Understanding the interpretations 11.3.4 The baseline 11.4 Related Work 11.5 Discussion 11.6 Conclusion References 12: Human–Machine Perception of Complex Signal Data 12.1 Introduction 12.1.1 Interpreting the QT interval on an ECG 12.1.2 Human–machine perception 12.2 Human–Machine Perception of ECG Data 12.2.1 Using pseudo-colour to support human interpretation Pseudo-colouring method 12.2.2 Automated human-like QT-prolongation detection 12.3 Human–Machine Perception: Differences, Benefits, and Opportunities 12.3.1 Future work References 13: The Shared-Workspace Framework for Dialogue and Other Cooperative Joint Activities 13.1 Introduction 13.2 The Shared Workspace Framework 13.3 Applying the Framework to Dialogue 13.4 Bringing Together Cooperative Joint Activity and Communication 13.5 Relevance to Human-like Machine Intelligence 13.5.1 Communication via an augmented workspace 13.5.2 Making an intelligent artificial interlocutor 13.6 Conclusion References 14: Beyond Robotic Speech: Mutual Benefits to Cognitive Psychology and Artificial Intelligence from the Study of Multimodal Communic 14.1 Introduction 14.2 The Use of Multimodal Cues in Human Face-to-face Communication 14.3 How Humans React to Embodied Agents that Use Multimodal Cues 14.4 Can Embodied Agents Recognize Multimodal Cues Produced by Humans? 14.5 Can Embodied Agents Produce Multimodal Cues? 14.6 Summary and Way Forward: Mutual Benefits from Studies on Multimodal Communication 302 14.6.1 Development and coding of shared corpora 14.6.2 Toward a mechanistic understanding of multimodal communication 14.6.3 Studying human communication with embodied agents Acknowledgements References Part 4: Human-like Representation and Learning 15: Human–Machine Scientific Discovery 15.1 Introduction 15.2 Scientific Problem and Dataset: Farm Scale Evaluations (FSEs) of GMHT Crops 15.3 The Knowledge Gap for Modelling Agro-ecosystems: Ecological Networks 15.4 Automated Discovery of Ecological Networks from FSE Data and Ecological Background Knowledge 15.5 Evaluation of the Results and Subsequent Discoveries 15.6 Conclusions References 16: Fast and Slow Learning in Human-Like Intelligence 16.1 Do Humans Learn Quickly and Is This Uniquely Human? 16.1.1 Evidence of rapid learning in infants, children, and adults 16.1.2 Does fast learning require a specific mechanism? 16.1.3 Slow learning in infants, children, and adults 16.1.4 Beyond word and concept learning 16.1.5 Evidence of rapid learning in non-human animals 16.2 What Makes for Rapid Learning? 16.3 Reward Prediction Error as the Gateway to Fast and Slow Learning 16.4 Conclusion Acknowledgements References 17: Interactive Learning with Mutual Explanations in Relational Domains 17.1 Introduction 17.2 The Case for Interpretable and Interactive Learning 17.3 Types of Explanations—There is No One-Size Fits All 17.4 Interactive Learning with ILP 17.5 Learning to Delete with Mutual Explanations 17.6 Conclusions and Future Work Acknowledgements References 18: Endowing machines with the expert human ability to select representations: why and how 18.1 Introduction 18.2 Example of selecting a representation 18.3 Benefits of switching representations 18.3.1 Epistemic benefits of switching representations 18.3.2 Cognitive benefits of switching representations 18.4 Why selecting a good representation is hard 18.4.1 Representational and cognitive complexity 18.4.2 Cognitive framework 18.5 Describing representations: rep2rep 18.5.1 A description language for representations 18.5.2 Importance 18.5.3 Correspondences 18.5.4 Formal properties for assessing informational suitability 18.5.5 Cognitive properties for assessing cognitive cost 18.6 Automated analysis and ranking of representations 18.7 Applications and future directions Acknowledgements References 19: Human–Machine Collaboration for Democratizing Data Science 19.1 Introduction 19.2 Motivation 19.2.1 Spreadsheets 19.2.2 A motivating example: Ice cream sales 19.3 Data Science Sketches 19.3.1 Data wrangling 19.3.2 Data selection Processing the data Relational rule learning Implementation choices 19.3.3 Clustering Problem setting Finding a cluster assignment 19.3.4 Sketches for inductive models Prediction Learning constraints and formulas Auto-completion Solving predictive auto-completion under constraints. PSYCHE acquires candidate Integrating the sketches. Let us now consider the effect of coloured sketches. So far, 19.4 Related Work 19.4.1 Visual analytics 19.4.2 Interactive machine learning 19.4.3 Machine learning in spreadsheets 19.4.4 Auto-completion and missing value Imputation 19.5 Conclusion Acknowledgements References Part 5: Evaluating Human-like Reasoning 20: Automated Common-sense Spatial Reasoning: Still a Huge Challenge 20.1 Introduction 20.2 Common-sense Reasoning 20.2.1 The nature of common-sense reasoning 20.2.2 Computational simulation of commonsense spatial reasoning 20.2.3 But natural language is still a promising route to common-sense 20.3 Fundamental Ontology of Space 20.3.1 Defining the spatial extent of material entities 20.4 Establishing a Formal Representation and its Vocabulary 20.4.1 Semantic form 20.4.2 Specifying a suitable vocabulary 20.4.3 The potentially infinite distinctions among spatial relations 20.5 Formalizing Ambiguous and Vague Spatial Vocabulary 20.5.1 Crossing 20.5.2 Position relative to ‘vertical’ 20.5.3 Sense resolution 20.6 Implicit and Background Knowledge 20.7 Default Reasoning 20.8 Computational Complexity 20.9 Progress towards Common-sense Spatial Reasoning 20.10 Conclusions Acknowledgements References 21: Sampling as the Human Approximation to Probabilistic Inference 21.1 A Sense of Location in the Human Sampling Algorithm 21.2 Key Properties of Cognitive Time Series 21.3 Sampling Algorithms to Explain Cognitive Time Series 21.3.1 Going beyond individuals to markets 21.4 Making the Sampling Algorithm more Bayesian 21.4.1 Efficient accumulation of samples explains perceptual biases 21.5 Conclusions Acknowledgements References 22: What Can the Conjunction Fallacy Tell Us about Human Reasoning? 22.1 The Conjunction Fallacy 22.2 Fallacy or No Fallacy? 22.3 Explaining the Fallacy 22.4 The Pre-eminence of Impact Assessment over Probability Judgements 22.5 Implications for Effective Human-like Computing 22.6 Conclusion References 23: Logic-based Robotics 23.1 Introduction 23.2 Relational Learning in Robot Vision 23.3 Learning to Act 23.3.1 Learning action models Trace recording Segmentation of states Matching the segments with existing action models Learning by experimentation Experimentation in simulation and real world 23.3.2 Tool creation Tool generalizer 23.3.3 Learning to plan with qualitative models Planning with qualitative models Learning a qualitative model Refining actions by reinforcement learning Closed-loop learning and experiments 23.4 Conclusion Acknowledgements References 24: Predicting Problem Difficulty in Chess 24.1 Introduction 24.2 Experimental Data 24.3 Analysis 24.3.1 Relations between player rating, problem rating, and success 24.3.2 Relations between player’s rating and estimation of difficulty 24.3.3 Experiment in automated prediction of difficulty 24.4 More Subtle Sources of Difficulty 24.4.1 Invisible moves 24.4.2 Seemingly good moves and the ‘Einstellung’ effect 24.5 Conclusions Acknowledgements References Index
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