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This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements.
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Deep learning
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A review of relational machine learning for knowledge graphs
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Neural-symbolic learning and reasoning: contributions and challenges
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Knowledge graph embedding: A survey of approaches and applications
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Attention is all you need
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Reinforcement learning: An introduction
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Neural approaches to conversational ai
Neurosymbolic ai: The 3 rd wave
Artur d’Avila Garcez and Luis C Lamb · 2023
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The semantic web: A new form of web content that is meaningful to computers will unleash a revolution of new possibilities
Tim Berners-Lee, James Hendler, and Ora Lassila · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Gemini: a family of highly capable multimodal models
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Palm: Scaling language modeling with pathways
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Jianfeng Gao, Michel Galley, and Lihong Li · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Cooperative heterogeneous multi-robot systems: A survey
Yara Rizk, Mariette Awad, and Edward W Tunstel · 2019
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Deep learning for system 2 processing
Yoshua Bengio · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Agentbench: Evaluating llms as agents
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
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Halueval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen · 2023
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The rise and potential of large language model based agents: A survey
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
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Creating large language model applications utilizing langchain: A primer on developing llm apps fast
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su · 2023
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Neurosymbolic ai for reasoning over knowledge graphs: A survey
Lauren Nicole DeLong, Ramon Fernández Mir, and Jacques D Fleuriot · 2023
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Knowledge graphs: Opportunities and challenges
Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, and Francesco Osborne · 2023
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A neuro-vector-symbolic architecture for solving raven’s progressive matrices
Michael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian, and Abbas Rahimi · 2023
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Testing theory of mind in large language models and humans
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Are emergent abilities of large language models a mirage?
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A survey on large language model based autonomous agents
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