Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.
Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. Adv Neur Inform Proc Syst. 2007;153–160.
Hinton GE. Learning multiple layers of representation. Trends Cogn Sci. 2007;11:428–34.
Ciresan D, Meier U, Schmidhuber J. Multi-column deep neural networks for image classification, Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012;3642–3649. doi:https://doi.org/10.1109/cvpr.2012.6248110.
Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Adv Neur Inform Proc Syst. 2012.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. Adv Neur Inform Proc Syst 2017. arXiv:1706.03762.
Sejnowski TJ. The deep learning revolution. MIT press. 2018.
Pearl J, McKenzie D. The book of why: the new science of cause and effect. Basic Books; 2018.
Harnett K. To build truly intelligent machines, teach them cause and effect. Quanta. 2018. https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/.
Marcus G, Davis E. Rebooting AI: building artificial intelligence we can trust. Pantheon. 2019.
Heaven D. Why deep-learning AIs are so easy to fool. Nature. 2019;574:163–6. https://doi.org/10.1038/d41586-019-03013-5.
Mitchell M. Artificial intelligence: a guide for thinking humans. Strauss and Giroux: Farrar; 2019.
Brooks RA. The cul-de-sac of the computational metaphor: a talk by Rodney Brooks. Edge. 2019. https://www.edge.org/conversation/rodney_a_brooks-the-cul-de-sac-of-the-computational-metaphor.
Marcus G, Davis E, Aaronson S. A very preliminary analysis of DALL-E 2. 2022. arXiv:2204.13807 [cs.CV].
Minai AA, Braha D, Bar-Yam Y. Complex systems engineering: a new paradigm, in complex engineered systems: science meets technology, D. Braha, A.A. Minai, and Y. Bar-Yam (Eds.). Springer Verlag. 2006;1–22.
Raff RA. The shape of life: genes, development, and the evolution of animal form. University of Chicago Press. 1996.
Schlosser G, Wagner GP (eds.). Modularity in development and evolution. Univer Chic Press. 2004.
Carroll SB. Endless forms most beautiful: the new science of evo-devo and the making of the animal kingdom. WW Norton & Company. 2005.
Wagner A. The origins of evolutionary innovations. Oxford: Oxford University Press; 2011.
Meunier D, Lambiotte R, Bullmore E. Modular and hierarchically modular organization of brain networks. Front Neurosci. 2010;4. https://doi.org/10.3389/fnins.2010.00200.
Grossberg S. The complementary brain: Unifying brain dynamics and modularity. Trends Cogn Sci. 2000;4:233–46. https://doi.org/10.1016/S1364-6613(00)01464-9.
Grossberg S. Conscious mind, resonant brain: how each brain makes a mind. Oxford University Press; 2021.
d’Avella A, Pai DK. Modularity for sensorimotor control: evidence and a new prediction. J Mot Behav. 2010;42:361–9.
Geary DC. The origin of mind: evolution of brain, cognition, and general intelligence. Am Psychol Assoc. 2005.
Thelen E, Smith LB. A dynamic systems approach to the development of cognition and action. MIT Press; 1994.
Kelso JAS. Dynamic patterns: the self-organization of brain and behavior. Bradford Books; 1995.
Goldfield EC. Emergent forms: origins and early development of human action and perception. Oxford University Press; 1995.
Nolfi S, Floreano D. Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT press. 2000.
Weng J, McClelland J, Pentland A, Sporns O, Stockman I, Sur M, Thelen E. Autonomous mental development by robots and animals. Science. 2001;291:599–600.
Jin Y, Meng Y. Morphogenetic robotics: a new emerging field in developmental robotics. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Reviews and Applications. 2011;41(2):145–60.
Weng J. Symbolic models and emergent models: a review. IEEE Trans Auton Ment Dev. 2011;4:29–54.
Cangelosi A, Schlesinger M. Developmental Robotics: from babies to robots. MIT Press. 2015.
Vujovic V, Rosendo A, Brodbeck L, Iida F. Evolutionary developmental robotics: Improving morphology and control of physical robots. Artificial Life. 2017;23(2):169–185. https://doi.org/10.1162/ARTL_a_00228.
Merel J, Botvinick M, Wayne G. Hierarchical motor control in mammals and machines. Nat Commun. 2019;10:5489. https://doi.org/10.1038/s41467-019-13239-6.
Botvinick M, Ritter S, Wang JX, Kurth-Nelson Z, Hassabis D. Reinforcement learning, fast and slow. Trends Cogn Sci. 2019;23:408–22. https://doi.org/10.1016/j.tics.2019.02.006.
Barretto A, Hou S, Borsa D, Silver D, Precup D. Fast reinforcement learning with generalized policy updates. PNAS. 2020;117:30079–87.
Spearman C. General intelligence, objectively determined and measured. Am J Psychol. 1904;15:201–93.
Cattell EB. Theory of fluid and crystallized intelligence: a critical experiment. J Educ Psychol. 1963;54:1–22.
Kahneman D. Thinking fast and slow. Straus and Giroux: Farrar; 2011.
Callebaut W, Rasskin-Gutman D (eds.). Modularity: understanding the development and evolution of natural complex systems. MIT Press. 2005.
Whitacre JM. Degeneracy: A link between evolvability, robustness and complexity in biological systems. Theor Biol Med Model. 2010;7:6. https://doi.org/10.1186/1742-4682-7-6.
Dawkins R. The evolution of evolvability, In Langton C. G. (Ed.), Artificial life: the proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems. Addison‐Wesley Publishing Co. 1988;201–220.
Kirschner M, Gerhart J. Evolvability. PNAS. 1998;95(15):8420–7. https://doi.org/10.1073/pnas.95.15.8420.
Wagner A. Robustness and evolvability in living systems. Princeton University Press; 2005.
Kerg G, Mittal S, Rolnick D, Bengio Y, Richards B, Lajoie G. On neural architecture inductive biases for relational tasks. 2022. arXiv:2206.05056 [cs.NE]. https://doi.org/10.48550/arXiv.2206.05056.
Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: can language models be too big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). 2021;610–623. https://doi.org/10.1145/3442188.3445922.
Chen MX, Firat O, Bapna A, Johnson M, Macherey W, Foster GF, Jones L, Parmar N, Schuster M, Chen Z, Wu Y, Hughes M. The best of both worlds: combining recent advances in neural machine translation, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia (Long Papers). 2018;76–86.
Liu X, Duh K, Liu L, Gao J. Very deep transformers for neural machine translation. 2020. arXiv:2008.07772 [cs.CL].
Heaven WD. OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless. MIT Technol Rev. 2020. https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/.
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, va den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529(7587):484–9. https://doi.org/10.1038/nature16961.
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y, Lillicrap T, Hui F, Sifre L, van den Driessche G, Graepel T, Hassabis D. Mastering the game of Go without human knowledge. Nature. 2017;550(7676):354–9. https://doi.org/10.1038/nature24270.
Girshick, R.B. (2015) Fast R-CNN, 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448.
OpenAI (2022) ChatGPT: Optimizing language models for dialogue. https://openai.com/blog/chatgpt/.
Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I. Zero-shot text-to-image generation. 2021. https://arxiv.org/abs/2102.12092v2.
Minai AA, Perdoor M, Byadarhaly KV, Vasa S, Iyer LR. A synergistic view of autonomous cognitive systems. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN’2010). 2010;498–505.
Braitenberg V. Vehicles: experiments in synthetic psychology. Cambridge, MA: MIT Press; 1984.
Carlson JM, Doyle J. Complexity and robustness. PNAS. 2002;99(supp. 1):2538–45.
Tanaka R, Doyle J. Scale-rich metabolic networks: background and introduction. 2004. https://arxiv.org/abs/q-bio/0410009.
Zador AM. A critique of pure learning and what artificial neural networks can learn from animal brains. Nat Commun. 2019;10:3770.
Latash ML. Understanding and synergy: a single concept at different levels of analysis?. Front Syst Neurosci. 2021;15. https://doi-org.uc.idm.oclc.org/10.3389/fnsys.2021.735406.
Latash ML. Motor synergies and the equilibrium-point hypothesis. Mot Control. 2010;14(3):294–322. https://doi.org/10.1123/mcj.14.3.294.
Article MathSciNet Google Scholar
Riley MA, Kuznetsov N, Bonnette S. State-, parameter-, and graph-dynamics: constraints and the distillation of postural control systems. Science & Motricité. 2011;74:5–18. https://doi.org/10.1051/sm/2011117.
Dobzhansky T. Nothing in biology makes sense except in the light of evolution. American Biology Teacher. 1973;35(3):125–9. https://doi.org/10.1093/icb/4.4.443.
Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol. 1962;160:106–54.
Hubel DH, Wiesel TN. Brain and visual perception. New York: Oxford Press; 2005.
Fogel LJ, Owens AJ, Walsh MJ. Artificial intelligence through simulated evolution. NY: John Wiley; 1966.
Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press; 1975.
Goldberg D. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Professional. 1989.
Stanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput. 2002;10(2):99–127. https://doi.org/10.1162/106365602320169811.
Stanley K, Miikkulainen R. A taxonomy for artificial embryogeny. Artif Life. 2003;9(2):93–130.
Clune J, Beckmann BE, Ofria C, Pennock RT. Evolving coordinated quadruped gaits with the HyperNEAT generative encoding. Proc IEEE Cong Evol Comp. 2009;2764–2771.
Sims K. Evolving virtual creatures. Proceedings of SIGGRAPH '94. 1994;15–22.
Sims K. Evolving 3D morphology and behavior by competition. Artif Life. 1994;1:353–72. https://doi.org/10.1162/artl.1994.1.4.353.
Rieffel J, Pollack J. An endosymbiotic model for modular acquisition in stochastic developmental systems. Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (ALIFE X). 2006.
Kirschner MW, Gerhart JC. The plausibility of life: resolving Darwin’s dilemma. Yale University Press; 2005.
Gerhart J, Kirschner M. The theory of facilitated variation. PNAS. 2007;104(Supp. 1):8582–9.
Kimura M. The neutral theory of molecular evolution. Press: Cambridge Univ; 1983.
Huneman P. Neutral spaces and topological explanations in evolutionary biology: lessons from some landscapes and mappings. Philosophy of Science. 2018;85(5):969–83. https://doi.org/10.1086/699759.
Kauffman SA. The origins of order: self-organization and selection in evolution. Oxford University Press; 1993.
Siebert BA, Hall CL, Gleeson JP, Asllani M. Role of modularity in self-organization dynamics in biological networks. Phys Rev E. 2020;102:052306. https://doi.org/10.1103/PhysRevE.102.052306.
留言 (0)