Towards Strong AI
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Strong AI—artificial intelligence that is in all respects at least as intelligent as humans—is still out of reach. Current AI lacks common sense, that is, it is not able to infer, understand, or explain the hidden processes, forces, and causes behind data. Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic. In contrast, various sources of evidence from cognitive science suggest that human brains engage in the active development of compositional generative predictive models (CGPMs) from their self-generated sensorimotor experiences. Guided by evolutionarily-shaped inductive learning and information processing biases, they exhibit the tendency to organize the gathered experiences into event-predictive encodings. Meanwhile, they infer and optimize behavior and attention by means of both epistemic- and homeostasis-oriented drives. I argue that AI research should set a stronger focus on learning CGPMs of the hidden causes that lead to the registered observations. Endowed with suitable information-processing biases, AI may develop that will be able to explain the reality it is confronted with, reason about it, and find adaptive solutions, making it Strong AI. Seeing that such Strong AI can be equipped with a mental capacity and computational resources that exceed those of humans, the resulting system may have the potential to guide our knowledge, technology, and policies into sustainable directions. Clearly, though, Strong AI may also be used to manipulate us even more. Thus, it will be on us to put good, far-reaching and long-term, homeostasis-oriented purpose into these machines.
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When discussants realise they are discussing the strictly philosophical topic of strong AI, the problem is virtually nonexistent.
“When the discussants realise that they are discussing the strictly philosophical topic of strong AI (Butz, 2021) 2 , the problem is virtually nonexistent.”
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
James, W. (1891) · Henry Holt and Company eBooks · doi:10.1037/11059-000 · Cited by 25,799 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Lakoff, G. P., & Johnson, M. L. (2003) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226470993.001.0001 · Cited by 25,252 · see citing works
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237 · see citing works
(1976) · Cited by 10,423 · see citing works
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · see citing works
Searle, J. R. (1980) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00005756 · Cited by 6,954 · see citing works
Barsalou, L. W. (1999) · Behavioral and Brain Sciences · doi:10.1017/s0140525x99002149 · Cited by 6,887 · see citing works
Computing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120 · see citing works
Conflict, arousal, and curiosity.
Berlyne, D. E. (1960) · McGraw-Hill Book Company · doi:10.1037/11164-000 · Cited by 6,018 · see citing works
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924 · see citing works
Autopoiesis and Cognition: The Realization of the Living
Maturana, H. R., & Varela, F. J. (1980) · Boston Studies in the Philosophy and History of Science · doi:10.1007/978-94-009-8947-4 · Cited by 5,815 · see citing works
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · see citing works
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,824 · see citing works
Autopoiesis and Cognition: The Realization of the Living
Maturana, H. R., Varela, F. J., & Beer, S. (1980) · Cited by 4,667 · see citing works
Jackendoff, R. S. (2002) · doi:10.1093/acprof:oso/9780198270126.001.0001 · Cited by 4,472 · see citing works
Gigerenzer, G., & Gaissmaier, W. (2011) · Annual Review of Psychology · doi:10.1146/annurev-psych-120709-145346 · Cited by 4,037 · see citing works
Simple Heuristics That Make Us Smart
Gigerenzer, G., & Todd, P. M. (1999) · Cited by 3,562 · see citing works
Boguslaw, R., & Skinner, B. F. (1972) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063899 · Cited by 3,556 · see citing works
Chomsky (1959) · doi:10.2307/411334 · Cited by 3,425 · see citing works
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119 · see citing works
The Theory of Event Coding (TEC): A framework for perception and action planning
Hommel, B., Müsseler, J., Aschersleben, G., & Prinz, W. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000103 · Cited by 3,082 · see citing works
Causality: models, reasoning, and inference
(2000) · Choice Reviews Online · doi:10.5860/choice.38-1494a · Cited by 2,969 · see citing works
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817 · see citing works
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Purposive Behavior in Animals and Men
Rodhom, C., & Tolman, E. C. (1950) · The American Journal of Psychology · doi:10.2307/1418946 · Cited by 2,455 · see citing works
(2016) · Cited by 2,334 · see citing works
The Cambridge handbook of thinking and reasoning
Holyoak, K. J., & Morrison, B. (2005) · Cited by 2,133 · see citing works
Purposive Behavior in Animals and Men
Irwin, F. W. (1932) · Europe PMC (PubMed Central) · Cited by 1,867 · see citing works
Foundations of Language: Brain, Meaning, Grammar, Evolution
Jackendoff, R. S. (2002) · Cited by 1,858 · see citing works
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831 · see citing works
The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations
Rizzolatti, G., & Sinigaglia, C. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2805 · Cited by 1,680 · see citing works
Event structure in perception and conception.
Zacks, J. M., & Tversky, B. (2001) · Psychological Bulletin · doi:10.1037/0033-2909.127.1.3 · Cited by 1,208 · see citing works
Event perception: a mind-brain perspective
Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.2.273 · Cited by 1,148 · see citing works
Intrinsic Motivation Systems for Autonomous Mental Development
Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.890271 · Cited by 1,126 · see citing works
MIT encyclopedia of cognitive sciences
(1999) · Choice Reviews Online · doi:10.5860/choice.36-4243 · Cited by 1,021 · see citing works
Action understanding as inverse planning
Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009) · Cognition · doi:10.1016/j.cognition.2009.07.005 · Cited by 920 · see citing works
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892 · see citing works
Reward, Motivation, and Reinforcement Learning
Dayan, P., & Balleine, B. W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00963-7 · Cited by 855 · see citing works
Deep Learning: A Critical Appraisal
Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853 · see citing works
Active inference and epistemic value
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731 · see citing works
Learning and inference in the brain
Friston, K. (2003) · Neural Networks · doi:10.1016/j.neunet.2003.06.005 · Cited by 724 · see citing works
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
Minsky, M. (2006) · Choice Reviews Online · doi:10.5860/choice.44-5688 · Cited by 683 · see citing works
A possibility for implementing curiosity and boredom in model-building neural controllers
Schmidhuber, J. (1991) · The MIT Press eBooks · doi:10.7551/mitpress/3115.003.0030 · Cited by 669 · see citing works
Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory.
Alison Gopnik, C., & Wellman, H. M. (2012) · Psychological Bulletin · doi:10.1037/a0028044 · Cited by 660 · see citing works
Prediction, cognition and the brain
Bubić, A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00025 · Cited by 658 · see citing works
Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective
Botvinick, M. M., Niv, Y., & Barto, A. G. (2009) · Cognition · doi:10.1016/j.cognition.2008.08.011 · Cited by 646 · see citing works
Developmental robotics: a survey
Lungarella, M., Giorgio Metta, B., Pfeifer, R., & Sandini, G. (2003) · Connection Science · doi:10.1080/09540090310001655110 · Cited by 626 · see citing works
Visual memory and motor planning in a natural task
Hayhoe, M., Shrivastava, A., Mruczek, R. E. B., & Pelz, J. B. (2003) · Journal of Vision · doi:10.1167/3.1.6 · Cited by 585 · see citing works
Viewpoint: when will AI exceed human performance? evidence from AI experts
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11222 · Cited by 579 · see citing works
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0064 · Cited by 515 · see citing works
Commonsense reasoning and commonsense knowledge in artificial intelligence
Davis, E., & Marcus, G. (2015) · Communications of the ACM · doi:10.1145/2701413 · Cited by 434 · see citing works
Cooperative Inverse Reinforcement Learning
Hadfield-Menell, D., Dragan, A., Abbeel, P., & Russell, S. (2016) · arXiv · doi:10.48550/arxiv.1606.03137 · Cited by 413 · see citing works
Monte-Carlo tree search and rapid action value estimation in computer Go
Gelly, S., & Silver, D. (2011) · Artificial Intelligence · doi:10.1016/j.artint.2011.03.007 · Cited by 377 · see citing works
Gärdenfors, P. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9629.001.0001 · Cited by 372 · see citing works
The Geometry of Meaning: Semantics Based on Conceptual Spaces
Gärdenfors, P. (2014) · Cited by 332 · see citing works
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
Rao, R. P. N., & Ballard, D. H. (1997) · Neural Computation · doi:10.1162/neco.1997.9.4.721 · Cited by 290 · see citing works
Radvansky, G. A., & Zacks, J. M. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199898138.001.0001 · Cited by 212 · see citing works
Constructing Experience: Event Models from Perception to Action
Richmond, L. L., & Zacks, J. M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.08.005 · Cited by 194 · see citing works
Structured Event Memory: A neuro-symbolic model of event cognition.
Franklin, N. T., Norman, K. A., Ranganath, C., Zacks, J. M., & Gershman, S. J. (2020) · Psychological Review · doi:10.1037/rev0000177 · Cited by 177 · see citing works
Common Sense, the Turing Test, and the Quest for Real AI
Levesque, H. J. (2017) · The MIT Press eBooks · doi:10.7551/mitpress/10909.001.0001 · Cited by 158 · see citing works
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Zhu, Y., Gao, T., Fan, L., Huang, S., Edmonds, M., Liu, H., Gao, F., Zhang, C., Qi, S., Wu, Y. N., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.01.011 · Cited by 135 · see citing works
From Event Representation to Linguistic Meaning
Ünal, E., Ji, Y., & Anna Papafragou, A. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12475 · Cited by 125 · see citing works
Structuring Memory Through Inference‐Based Event Segmentation
Shin, Y. S., & DuBrow, S. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12505 · Cited by 121 · see citing works
The Omniglot challenge: a 3-year progress reportPreprint
Tenenbaum, J. B. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 115 · see citing works
Event Representations and Predictive Processing: The Role of the Midline Default Network Core
Stawarczyk, D., Bezdek, M. A., & Zacks, J. M. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12450 · Cited by 106 · see citing works
Compositional generalization through meta sequence-to-sequence learningPreprint
Lake, B. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05381 · Cited by 104 · see citing works
Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension
Kuperberg, G. R. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12518 · Cited by 79 · see citing works
Sensorimotor Cognition and Natural Language Syntax
Knott, A. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/8938.001.0001 · Cited by 76 · see citing works
Elman, J. L., & McRae, K. (2019) · Psychological Review · doi:10.1037/rev0000133 · Cited by 75 · see citing works
How Does the Mind Render Streaming Experience as Events?
Baldwin, D. A., & Kosie, J. E. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12502 · Cited by 68 · see citing works
Knauff, M. (2013) · The MIT Press eBooks · doi:10.7551/mitpress/9780262018654.001.0001 · Cited by 67 · see citing works
Generative character of perception: a neural architecture for sensorimotor anticipation
Gross, H.-M., Heinze, A., Seiler, T., & Stephan, V. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00047-7 · Cited by 60 · see citing works
Space to reason: a spatial theory of human thought
(2013) · Choice Reviews Online · doi:10.5860/choice.51-2653 · Cited by 53 · see citing works
Butz, M. V., & Kutter, E. F. (2016) · Cited by 52 · see citing works
Self-citationToward a Unified Sub-symbolic Computational Theory of Cognition
Butz, M. V. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00925 · Cited by 51 · see citing works
Self-citationLearning, planning, and control in a monolithic neural event inference architecture
Butz, M. V., Bilkey, D. K., Humaidan, D., Knott, A., & Otte, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.05.001 · Cited by 48 · see citing works
Self-citationCan Machine Intelligence be Measured in the Same Way as Human intelligence?
Besold, T. R., Hernández‐Orallo, J., & Schmid, U. (2015) · Künstliche Intell. · doi:10.1007/s13218-015-0361-4 · Cited by 43 · see citing works
How and Why the Brain Lays the Foundations for a Conscious Self
Butz, M. V. (2008) · Constructivist Foundations · Cited by 37 · see citing works
Self-citationWhat's in a Concept? Analog versus Parametric Concepts in LCCM Theory
Evans, V. (2015) · The MIT Press eBooks · doi:10.7551/mitpress/9383.003.0018 · Cited by 35 · see citing works
Searle, J. R. (2009) · Scholarpedia · doi:10.4249/scholarpedia.3100 · Cited by 34 · see citing works
Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces
Schillaci, G., Villalpando, A. P., Hafner, V. V., Hanappe, P., Colliaux, D., & Wintz, T. (2020) · Adaptive Behavior · doi:10.1177/1059712320922916 · Cited by 32 · see citing works
Event‐Predictive Cognition: A Root for Conceptual Human Thought
Butz, M. V., Achimova, A., Bilkey, D. K., & Knott, A. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12522 · Cited by 31 · see citing works
Self-citationButz, M. V., & Kutter, E. F. (2017) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198739692.001.0001 · Cited by 30 · see citing works
Self-citationSelf-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Butz, M. V., Shirinov, E., & Reif, K. L. (2010) · Adaptive Behavior · doi:10.1177/1059712310376842 · Cited by 28 · see citing works
Self-citationThe Dynamics of Neural Populations Capture the Laws of the Mind
Schöner, G. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12453 · Cited by 27 · see citing works
Knott, A., & Takac, M. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12497 · Cited by 27 · see citing works
Prediction‐Based Learning and Processing of Event Knowledge
McRae, K., Brown, K., & Elman, J. L. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12482 · Cited by 23 · see citing works
Simultaneously emerging Braitenberg codes and compositionality
Sugita, Y., Tani, J., & Butz, M. V. (2011) · Adaptive Behavior · doi:10.1177/1059712311416871 · Cited by 18 · see citing works
Self-citationInferring Adaptive Goal-Directed Behavior Within Recurrent Neural Networks
Otte, S., Schmitt, T., Friston, K. J., & Butz, M. V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68600-4_27 · Cited by 17 · see citing works
Self-citationAnticipation for learning, cognition and education
Butz, M. V. (2004) · On the Horizon The International Journal of Learning Futures · doi:10.1108/10748120410555359 · Cited by 16 · see citing works
Self-citationAutonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Gumbsch, C., Butz, M. V., & Martius, G. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2925890 · Cited by 15 · see citing works
Self-citationAn interdisciplinary overview of developmental indices and behavioral measures of the minimal self
Georgie, Y., Schillaci, G., & Hafner, V. (2019) · Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics · doi:10.1109/devlrn.2019.8850703 · Cited by 15 · see citing works
Just Imagine! Learning to Emulate and Infer Actions with a Stochastic Generative Architecture
Schrodt, F., & Butz, M. V. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00005 · Cited by 14 · see citing works
Self-citationHands Ahead in Mind and Motion: Active Inference in Peripersonal Hand Space
Lohmann, J., Belardinelli, A., & Butz, M. V. (2019) · Vision · doi:10.3390/vision3020015 · Cited by 13 · see citing works
Self-citationA Computational Model for the Dynamical Learning of Event Taxonomies.
Gumbsch, C., Otte, S., & Butz, M. V. (2017) · eScholarship (California Digital Library) · Cited by 9 · see citing works
Self-citationIntegrative Collision Avoidance Within RNN-Driven Many-Joint Robot Arms
Otte, S., Hofmaier, L., & Butz, M. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01424-7_73 · Cited by 8 · see citing works
Self-citationWhich Structures Are Out There
Butz, M. V. (2017) · doi:10.15502/9783958573093 · Cited by 8 · see citing works
Self-citationSpeaker-specific adaptation to variable use of uncertainty expressions.
Schuster, S., & Degen, J. (2019) · Cognitive Science · Cited by 7 · see citing works
Generating new concepts with hybrid neuro-symbolic modelsPreprint
Feinman, R., & Lake, B. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08978 · Cited by 7 · see citing works
Perception: The Binding Problem and the Coherence of Perception
Schmidt, T. (2009) · Elsevier eBooks · doi:10.1016/b978-012373873-8.00059-1 · Cited by 6 · see citing works
Investigating Efficient Learning and Compositionality in Generative LSTM Networks
Fabi, S., Otte, S., Wiese, J. G., & Butz, M. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61609-0_12 · Cited by 6 · see citing works
Self-citationFostering Event Compression Using Gated Surprise
Humaidan, D., Otte, S., & Butz, M. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61609-0_13 · Cited by 4 · see citing works
Self-citationBinding and Perspective Taking as Inference in a Generative Neural Network Model
Sadeghi, M., Schrodt, F., Otte, S., & Butz, M. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86365-4_1 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 9
- Bahdanau D, Cho K, Bengio Y (2015) Neural machine transla- tion by jointly learning to align and translate. In: 3rd interna- tional conference on learning representations, ICLR 2015 (2015). ArXiv: 1409. 0473
- Johnson-Laird PN (1983) Mental models: towards a cognitive science of language, inference, and consciousness. Cambridge University Press and Harvard University Press, Cambridge
- Pearl J (2020) The limitations of opaque learning machines. In: Brockman J (ed) Possible minds: 25 ways of looking at AI, chap. 2. Penguin Press, New York, pp 13-19
- Russell S (2020) The purpose put into the machine. In: Brockman J (ed) Possible minds: 25 ways of looking at AI, chap. 3. Penguin Press, New York, pp 20-32
- Schmidhuber J (1991) A possibility for implementing curiosity and boredom in model-building neural controllers. In: Proceed- ings of the first international conference on simulation of adap- tive behavior: from animals to animats, pp 222-227
- Sutton R (2019) The bitter lesson (2019). https:// doi. org/ 10. 1111/ tops. 12502 106. · DOI
- Sutton RS, Barto AG (2018) Reinforcement learning: an intro- duction, second, edition edn. MIT Press, Cambridge 107.
- Tani J (2017) Exploring robotic minds. Oxford University Press, Oxford 110.
- Vinyals O, Babuschkin I, Chung J, Mathieu M, Jaderberg M, Czarnecki W, Dudzik A, Huang A, Georgiev P, Powell R, Ewalds T, Horgan D, Kroiss M, Danihelka I, Agapiou J, Oh J, Dalibard V, Choi D, Sifre L, Sulsky Y, Vezhnevets S, Molloy J, Cai T, Budden D, Paine T, Gulcehre C, Wang Z, Pfaff T, Pohlen T, Yogatama D, Cohen J, McKinney K, Smith O, Schaul T, Lil- licrap T, Apps C, Kavukcuoglu K, Hassabis D, Silver D (2019) Alphastar: Mastering the real-time strategy game StarCraft II. https:// doi. org/ 10. 1007/ s13218-015-0361-4 114. · DOI
Cited by
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There is no such thing as conscious artificial intelligence
Porębski, A., & Figura, J. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05868-8 · Cited by 20 · see citing works
“When the discussants realise that they are discussing the strictly philosophical topic of strong AI (Butz, 2021) 2 , the problem is virtually nonexistent.”
- Authors
- Butz, M. V.
- Affiliations
- University of Tübingen (DE)
- Corresponding author
- Butz, Martin V.
- ORCID
- Butz, Martin V.
- Venue
- KI - Künstliche Intelligenz
- Published
- 2021-02-26
- Volume
- 35
- Issue
- 1
- Pages
- 91-101
- ISSN
- 0933-1875 (ISSN-L)
- 1610-1987
- OpenAlex
- W3134077149
- Cited by
- 42
- References
- 116
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 13 · 2023: 5 · 2022: 7 · 2021: 4
- FWCI
- 3.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Künstliche Intell. (publishedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (198647426, BU 1335/11-1, 390727645)
- Alexander von Humboldt-Stiftung (Feodor Lynen)
- Eberhard Karls Universität Tübingen
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Keywords
- artificial general intelligence, common sense, hidden causes, causal inference, information processing biases, inductive learning, homeostasis, deep neural network, AI ethics, manipulation
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