其他
NeuroAI:迈向下一代人工智能
导语
NeuroAI 是神经科学和人工智能的交叉点,这一新兴领域假定,对神经计算的更好理解将帮助催化人工智能的下一次革命。神经科学是否推动了人工智能?未来的人工智能是否需要神经科学?神经科学和人工智能领域的多位著名学者近日在 arXiv 上发表 NeuroAI 白皮书认为,神经科学长期以来一直是推动人工智能(AI)发展的重要驱动力,NeuroAI 领域的基础研究将推动下一代人工智能的进程。文章发表后引发热议,以下是对争论的主角——NeuroAI 白皮书的全文翻译。
本着促进神经科学、计算机科学、认知科学和脑科学等不同领域的学术工作者的交流与合作,集智俱乐部联合北京师范大学柳昀哲、北京大学鲍平磊和昌平实验室吕柄江三位研究员共同发起了 「NeuroAI」读书会,聚焦在视觉、语言和学习领域中神经科学与人工智能的相关研究,期待能够架起神经科学与人工智能领域的合作桥梁,激发跨学科的学术火花。欢迎感兴趣的朋友参加。
关键词:NeuroAI,人工智能,神经科学
Anthony Zador, Blake Richards, Bence Ölveczky等 | 来源
赵凯 | 译者
邓一雪 | 编辑
论文题目:Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution论文链接:https://arxiv.org/abs/2210.08340
NeuroAI面对的大型挑战:具身图灵测试
NeuroAI面对的大型挑战:具身图灵测试
解决具身图灵测试的路线图
解决具身图灵测试的路线图
我们需要什么?
我们需要什么?
结论
结论
参考文献
Akos, Zsuzsa, Máté Nagy, Severin Leven, and Tamás Vicsek. 2010. “Thermal Soaring Flight of Birds and Unmanned Aerial Vehicles.” Bioinspiration & Biomimetics 5 (4): 045003. Attwell, D., and S. B. Laughlin. 2001. “An Energy Budget for Signaling in the Grey Matter of the Brain.”Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism 21 (10): 1133–45. Bommasani, Rishi, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, et al. 2021. “On the Opportunities and Risks of Foundation Models.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/2108.07258. Brown, Tom, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, and Amanda Askell. 2020. “Language Models Are Few-Shot Learners.” Advances in Neural Information Processing Systems 33: 1877–1901. Campbell, Murray, A. Joseph Hoane, and Feng-Hsiung Hsu. 2002. “Deep Blue.” Artificial Intelligence 134 (1): 57–83. Cisek, Paul, and Benjamin Y. Hayden. 2022. “Neuroscience Needs Evolution.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 377 (1844): 20200518. Culick, Fred. 2001. “What the Wright Brothers Did and Did Not Understand about Flight Mechanics - In Modern Terms.” In 37th Joint Propulsion Conference and Exhibit. Joint Propulsion Conferences. American Institute of Aeronautics and Astronautics. Davies, Mike, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, et al. 2018. “Loihi: A Neuromorphic Manycore Processor with On-Chip Learning.” IEEE Micro 38 (1): 82–99. DeBole, Michael V., Brian Taba, Arnon Amir, Filipp Akopyan, Alexander Andreopoulos, William P. Risk, Jeff Kusnitz, et al. 2019. “TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years.” Computer 52 (5): 20–29. Dobrunz, L. E., and C. F. Stevens. 1997. “Heterogeneity of Release Probability, Facilitation, and Depletion at Central Synapses.” Neuron 18 (6): 995–1008. Gupta, Agrim, Silvio Savarese, Surya Ganguli, and Li Fei-Fei. 2021. “Embodied Intelligence via Learning and Evolution.” Nature Communications 12 (1): 5721. Hassabis, Demis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick. 2017.“Neuroscience-Inspired Artificial Intelligence.” Neuron 95 (2): 245–58. Huang, Sandy, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel. 2017. “Adversarial Attacks on Neural Network Policies.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/1702.02284. Hubel, D. H., and T. N. Wiesel. 1962. “Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex.” The Journal of Physiology. https://doi.org/10.1113/jphysiol.1962.sp006837. Itti, L., C. Koch, and E. Niebur. 1998. “A Model of Saliency-Based Visual Attention for Rapid Scene Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (11): 1254–59. Kosoy, Eliza, Adrian Liu, Jasmine L. Collins, David Chan, Jessica B. Hamrick, Nan Rosemary Ke, Sandy Huang, Bryanna Kaufmann, John Canny, and Alison Gopnik. 11--13 Apr 2022. “Learning Causal Overhypotheses through Exploration in Children and Computational Models.” In Proceedings of the First Conference on Causal Learning and Reasoning, edited by Bernhard Schölkopf, Caroline Uhler, and Kun Zhang, 177:390–406. Proceedings of Machine Learning Research. PMLR. Koulakov, Alexei, Sergey Shuvaev, Divyansha Lachi, and Anthony Zador. 2022. “Encoding Innate Ability through a Genomic Bottleneck.” bioRxiv. https://doi.org/10.1101/2021.03.16.435261. Larochelle, Hugo, and Geoffrey Hinton. 2010. “Learning to Combine Foveal Glimpses with a Third-Order Boltzmann Machine.” Advances in Neural Information Processing Systems 23. https://proceedings.neurips.cc/paper/2010/hash/677e09724f0e2df9b6c000b75b5da10d-Abstract.html . LeCun, Yann, and Yoshua Bengio. 1995. “Convolutional Networks for Images, Speech, and Time Series.” The Handbook of Brain Theory and Neural 3361 (10). http://www.iro.umontreal.ca/~lisa/pointeurs/handbook-convo.pdf. Lennie, Peter. 2003. “The Cost of Cortical Computation.” Current Biology: CB 13 (6): 493–97. Lilienthal, Otto. 1911. Birdflight as the Basis of Aviation: A Contribution Towards a System of Aviation, Compiled from the Results of Numerous Experiments Made by O. and G. Lilienthal. Longmans, Green. Liu, Siqi, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, et al. 2021. “From Motor Control to Team Play in Simulated Humanoid Football.” arXiv [cs.AI]. arXiv. http://arxiv.org/abs/2105.12196. Ma, Wei Ji, Jeffrey M. Beck, Peter E. Latham, and Alexandre Pouget. 2006. “Bayesian Inference with Probabilistic Population Codes.” Nature Neuroscience 9 (11): 1432–38. McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” The Bulletin of Mathematical Biophysics 5 (4): 115–33. Merel, Josh, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, and Bence Ölveczky. 2019. “Deep Neuroethology of a Virtual Rodent.” arXiv [q-bio.NC]. arXiv. http://arxiv.org/abs/1911.09451. Merel, Josh, Matthew Botvinick, and Greg Wayne. 2019. “Hierarchical Motor Control in Mammals and Machines.” Nature Communications 10 (1): 5489. Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, et al. 2015. “Human-Level Control through Deep Reinforcement Learning.” Nature 518 (7540): 529–33. Moravec, Hans. 1988. Mind Children: The Future of Robot and Human Intelligence. Harvard University Press. Patterson, David, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2021. “Carbon Emissions and Large Neural Network Training.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/2104.10350. Pehlevan, Cengiz, and Dmitri B. Chklovskii. 2019. “Neuroscience-Inspired Online Unsupervised Learning Algorithms: Artificial Neural Networks.” IEEE Signal Processing Magazine 36 (6): 88–96. Rescorla, R. A. 1972. “A Theory of Pavlovian Conditioning : Variations in the Effectiveness of Reinforcement and Nonreinforcement.” Current Research and Theory, 64–99. Roy, Kaushik, Akhilesh Jaiswal, and Priyadarshini Panda. 2019. “Towards Spike-Based Machine Intelligence with Neuromorphic Computing.” Nature 575 (7784): 607–17. Schultz, W., P. Dayan, and P. R. Montague. 1997. “A Neural Substrate of Prediction and Reward.” Science 275 (5306): 1593–99. Sejnowski, Terrence. 2022. “Large Language Models and the Reverse Turing Test.” arXiv [cs.CL]. arXiv. http://arxiv.org/abs/2207.14382. Shyy, Wei, Yongsheng Lian, Jian Tang, Dragos Viieru, and Hao Liu. 2008. Aerodynamics of Low Reynolds Number Flyers. Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. Sinz, Fabian H., Xaq Pitkow, Jacob Reimer, Matthias Bethge, and Andreas S. Tolias. 2019. “Engineering a Less Artificial Intelligence.” Neuron 103 (6): 967–79. Sokoloff, Louis. 1960. “The Metabolism of the Central Nervous System in Vivo.” Handbook of Physiology, Section I, Neurophysiology 3: 1843–64. Stanley, Kenneth O., Jeff Clune, Joel Lehman, and Risto Miikkulainen. 2019. “Designing Neural Networks through Neuroevolution.” Nature Machine Intelligence 1 (1): 24–35. Stöckl, Christoph, Dominik Lang, and Wolfgang Maass. 2022. “Structure Induces Computational Function in Networks with Diverse Types of Spiking Neurons.” bioRxiv. https://doi.org/10.1101/2021.05.18.444689. Thorndike, L., and Darryl Bruce. 2017. Animal Intelligence: Experimental Studies. Routledge. Turing, A. M. 1950. “I.—COMPUTING MACHINERY AND INTELLIGENCE.” Mind; a Quarterly Review of Psychology and Philosophy LIX (236): 433–60. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” Advances in Neural Information Processing Systems 30. https://proceedings.neurips.cc/paper/7181-attention-is-all-you-need. Von Neumann, John. 2012. The Computer and the Brain. 3rd ed. Yale University Press. https://doi.org/10.12987/9780300188080. Xu, K., J. Ba, R. Kiros, K. Cho, and A. Courville. 2015. “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention.” International. https://proceedings.mlr.press/v37/xuc15.html. Zador, Anthony M. 2019. “A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains.” Nature Communications 10 (1): 3770.
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