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This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts (MoE), multimodal learning, and the speculated advancements towards Artificial General Intelligence (AGI).
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M. Afshar, B. Sharma, D. Dligach, M. Oguss, R. Brown, N. Chhabra, H. M. Thompson, T. Markossian, C. Joyce, M. M. Churpek et al. , “Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (smart-ai): a retrospective deep learning study,” The Lancet Digital Health , vol. 4, no. 6, pp. e426–e435, 2022
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H. Alwahaby, M. Cukurova, Z. Papamitsiou, and M. Giannakos, “The evidence of impact and ethical considerations of multimodal learning analytics: A systematic literature review,” The Multimodal Learning Analytics Handbook , pp. 289–325, 2022
2022
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Y. Rong, “Roadmap of alphago to alphastar: Problems and challenges,” in 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022) , vol. 12348. SPIE, 2022, pp. 904–914
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2023
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Y. Fan, F. Jiang, P. Li, and H. Li, “Grammargpt: Exploring open-source llms for native chinese grammatical error correction with supervised fine-tuning,” in CCF International Conference on Natural Language Processing and Chinese Computing . Springer, 2023, pp. 69–80
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D. Liga and L. Robaldo, “Fine-tuning gpt-3 for legal rule classification,” Computer Law & Security Review , vol. 51, p. 105864, 2023
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F. Faal, K. Schmitt, and J. Y. Yu, “Reward modeling for mitigating toxicity in transformer-based language models,” Applied Intelligence , vol. 53, no. 7, pp. 8421–8435, 2023
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J. Peng, K. Zhou, R. Zhou, T. Hartvigsen, Y. Zhang, Z. Wang, and T. Chen, “Sparse moe as a new treatment: Addressing forgetting, fitting, learning issues in multi-modal multi-task learning,” in Conference on Parsimony and Learning (Recent Spotlight Track) , 2023
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W. Huang, H. Zhang, P. Peng, and H. Wang, “Multi-gate mixture-of-expert combined with synthetic minority over-sampling technique for multimode imbalanced fault diagnosis,” in 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD) . IEEE, 2023, pp. 456–461
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M. Agbese, R. Mohanani, A. Khan, and P. Abrahamsson, “Implementing ai ethics: Making sense of the ethical requirements,” in Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering , 2023, pp. 62–71
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N. Guha, C. Lawrence, L. A. Gailmard, K. Rodolfa, F. Surani, R. Bommasani, I. Raji, M.-F. Cuéllar, C. Honigsberg, P. Liang et al. , “Ai regulation has its own alignment problem: The technical and institutional feasibility of disclosure, registration, licensing, and auditing,” George Washington Law Review, Forthcoming , 2023
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Gemini Team, Google, “Gemini: A family of highly capable multimodal models,” 2023, accessed: 17 December 2023. [Online]. Available: https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf
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S. Qi, Z. Cao, J. Rao, L. Wang, J. Xiao, and X. Wang, “What is the limitation of multimodal llms? a deeper look into multimodal llms through prompt probing,” Information Processing & Management , vol. 60, no. 6, p. 103510, 2023
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M. S. Rahman, S. Bag, M. A. Hossain, F. A. M. A. Fattah, M. O. Gani, and N. P. Rana, “The new wave of ai-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement,” Journal of Retailing and Consumer Services , vol. 72, p. 103273, 2023
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R. Wolfe, Y. Yang, B. Howe, and A. Caliskan, “Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency , 2023, pp. 1174–1185
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Q. Miao, W. Zheng, Y. Lv, M. Huang, W. Ding, and F.-Y. Wang, “Dao to hanoi via desci: Ai paradigm shifts from alphago to chatgpt,” IEEE/CAA Journal of Automatica Sinica , vol. 10, no. 4, pp. 877–897, 2023
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W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4195–4205
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H. Cai, J. Li, M. Hu, C. Gan, and S. Han, “Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 302–17 313
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X. Liu, H. Peng, N. Zheng, Y. Yang, H. Hu, and Y. Yuan, “Efficientvit: Memory efficient vision transformer with cascaded group attention,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 420–14 430
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Y. Li, Q. Fan, H. Huang, Z. Han, and Q. Gu, “A modified yolov8 detection network for uav aerial image recognition,” Drones , vol. 7, no. 5, p. 304, 2023
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F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on yolo-v8 for smart cities,” Neural Computing and Applications , vol. 35, no. 28, pp. 20 939–20 954, 2023
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Z. Guo, Y. Tang, R. Zhang, D. Wang, Z. Wang, B. Zhao, and X. Li, “Viewrefer: Grasp the multi-view knowledge for 3d visual grounding,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 372–15 383
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C. Pan, Y. He, J. Peng, Q. Zhang, W. Sui, and Z. Zhang, “Baeformer: Bi-directional and early interaction transformers for bird’s eye view semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9590–9599
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P. Xu, X. Zhu, and D. A. Clifton, “Multimodal learning with transformers: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
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I. Molenaar, S. de Mooij, R. Azevedo, M. Bannert, S. Järvelä, and D. Gašević, “Measuring self-regulated learning and the role of ai: Five years of research using multimodal multichannel data,” Computers in Human Behavior , vol. 139, p. 107540, 2023
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S. Steyaert, M. Pizurica, D. Nagaraj, P. Khandelwal, T. Hernandez-Boussard, A. J. Gentles, and O. Gevaert, “Multimodal data fusion for cancer biomarker discovery with deep learning,” Nature Machine Intelligence , vol. 5, no. 4, pp. 351–362, 2023
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V. Rani, S. T. Nabi, M. Kumar, A. Mittal, and K. Kumar, “Self-supervised learning: A succinct review,” Archives of Computational Methods in Engineering , vol. 30, no. 4, pp. 2761–2775, 2023
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M. C. Schiappa, Y. S. Rawat, and M. Shah, “Self-supervised learning for videos: A survey,” ACM Computing Surveys , vol. 55, no. 13s, pp. 1–37, 2023
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J. Yu, H. Yin, X. Xia, T. Chen, J. Li, and Z. Huang, “Self-supervised learning for recommender systems: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2023
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V. Bharti, A. Kumar, V. Purohit, R. Singh, A. K. Singh, and S. K. Singh, “A label efficient semi self-supervised learning framework for iot devices in industrial process,” IEEE Transactions on Industrial Informatics , 2023
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D. Sam and J. Z. Kolter, “Losses over labels: Weakly supervised learning via direct loss construction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 8, 2023, pp. 9695–9703
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M. Wang, P. Xie, Y. Du, and X. Hu, “T5-based model for abstractive summarization: A semi-supervised learning approach with consistency loss functions,” Applied Sciences , vol. 13, no. 12, p. 7111, 2023
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S. Yan, H. Shao, Y. Xiao, B. Liu, and J. Wan, “Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises,” Robotics and Computer-Integrated Manufacturing , vol. 79, p. 102441, 2023
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N.-R. Zhou, T.-F. Zhang, X.-W. Xie, and J.-Y. Wu, “Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution,” Signal Processing: Image Communication , vol. 110, p. 116891, 2023
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A. Hafiz, M. Hassaballah, A. Alqahtani, S. Alsubai, and M. A. Hameed, “Reinforcement learning with an ensemble of binary action deep q-networks.” Computer Systems Science & Engineering , vol. 46, no. 3, 2023
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B. Lin, “Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook,” Expert Systems with Applications , p. 122254, 2023
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B. Luo, Z. Wu, F. Zhou, and B.-C. Wang, “Human-in-the-loop reinforcement learning in continuous-action space,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
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H. Liu, J. Liu, L. Cui, Z. Teng, N. Duan, M. Zhou, and Y. Zhang, “Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2023
2023
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S. Ajmal, A. A. I. Ahmed, and C. Jalota, “Natural language processing in improving information retrieval and knowledge discovery in healthcare conversational agents,” Journal of Artificial Intelligence and Machine Learning in Management , vol. 7, no. 1, pp. 34–47, 2023
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W. Peng, D. Xu, T. Xu, J. Zhang, and E. Chen, “Are gpt embeddings useful for ads and recommendation?” in International Conference on Knowledge Science, Engineering and Management . Springer, 2023, pp. 151–162
2023
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H. Rashkin, V. Nikolaev, M. Lamm, L. Aroyo, M. Collins, D. Das, S. Petrov, G. S. Tomar, I. Turc, and D. Reitter, “Measuring attribution in natural language generation models,” Computational Linguistics , pp. 1–64, 2023
2023
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A. K. Pandey and S. S. Roy, “Natural language generation using sequential models: A survey,” Neural Processing Letters , pp. 1–34, 2023
2023
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Y. K. Dwivedi, N. Kshetri, L. Hughes, E. L. Slade, A. Jeyaraj, A. K. Kar, A. M. Baabdullah, A. Koohang, V. Raghavan, M. Ahuja et al. , ““so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy,” International Journal of Information Management , vol. 71, p. 102642, 2023
2023
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H. Ji, I. Han, and Y. Ko, “A systematic review of conversational ai in language education: Focusing on the collaboration with human teachers,” Journal of Research on Technology in Education , vol. 55, no. 1, pp. 48–63, 2023
2023
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Y. Wan, W. Wang, P. He, J. Gu, H. Bai, and M. R. Lyu, “Biasasker: Measuring the bias in conversational ai system,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 515–527
2023
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Z. Xiao, “Seeing us through machines: designing and building conversational ai to understand humans,” Ph.D. dissertation, University of Illinois at Urbana-Champaign, 2023
2023
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H.-K. Ko, G. Park, H. Jeon, J. Jo, J. Kim, and J. Seo, “Large-scale text-to-image generation models for visual artists’ creative works,” in Proceedings of the 28th International Conference on Intelligent User Interfaces , 2023, pp. 919–933
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A. Pearson, “The rise of crealtives: Using ai to enable and speed up the creative process,” Journal of AI, Robotics & Workplace Automation , vol. 2, no. 2, pp. 101–114, 2023
2023
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J. Rezwana and M. L. Maher, “Designing creative ai partners with cofi: A framework for modeling interaction in human-ai co-creative systems,” ACM Transactions on Computer-Human Interaction , vol. 30, no. 5, pp. 1–28, 2023
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S. Sharma and S. Bvuma, “Generative adversarial networks (gans) for creative applications: Exploring art and music generation,” International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068 , vol. 2, no. 4, pp. 29–33, 2023
2023
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B. Attard-Frost, A. De los Ríos, and D. R. Walters, “The ethics of ai business practices: a review of 47 ai ethics guidelines,” AI and Ethics , vol. 3, no. 2, pp. 389–406, 2023
2023
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J. Schuett, “Three lines of defense against risks from ai,” AI & SOCIETY , pp. 1–15, 2023
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T. McIntosh, A. Kayes, Y.-P. P. Chen, A. Ng, and P. Watters, “Applying staged event-driven access control to combat ransomware,” Computers & Security , vol. 128, p. 103160, 2023
2023
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C. Ma, J. Li, K. Wei, B. Liu, M. Ding, L. Yuan, Z. Han, and H. V. Poor, “Trusted ai in multiagent systems: An overview of privacy and security for distributed learning,” Proceedings of the IEEE , vol. 111, no. 9, pp. 1097–1132, 2023
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M. Nguyen, K. Kishan, T. Nguyen, A. Chadha, and T. Vu, “Efficient fine-tuning large language models for knowledge-aware response planning,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2023, pp. 593–611
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S. Nyholm, “Responsibility gaps, value alignment, and meaningful human control over artificial intelligence,” in Risk and responsibility in context . Routledge, 2023, pp. 191–213
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L. Che, J. Wang, Y. Zhou, and F. Ma, “Multimodal federated learning: A survey,” Sensors , vol. 23, no. 15, p. 6986, 2023
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C. Hwang, W. Cui, Y. Xiong, Z. Yang, Z. Liu, H. Hu, Z. Wang, R. Salas, J. Jose, P. Ram et al. , “Tutel: Adaptive mixture-of-experts at scale,” Proceedings of Machine Learning and Systems , vol. 5, 2023
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N. Dikkala, N. Ghosh, R. Meka, R. Panigrahy, N. Vyas, and X. Wang, “On the benefits of learning to route in mixture-of-experts models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 9376–9396
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J. Li, Y. Jiang, Y. Zhu, C. Wang, and H. Xu, “Accelerating distributed { \{ MoE } \} training and inference with lina,” in 2023 USENIX Annual Technical Conference (USENIX ATC 23) , 2023, pp. 945–959
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M. H. Jarrahi, D. Askay, A. Eshraghi, and P. Smith, “Artificial intelligence and knowledge management: A partnership between human and ai,” Business Horizons , vol. 66, no. 1, pp. 87–99, 2023
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S. Friederich, “Symbiosis, not alignment, as the goal for liberal democracies in the transition to artificial general intelligence,” AI and Ethics , pp. 1–10, 2023
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S. Pal, K. Kumari, S. Kadam, and A. Saha, “The ai revolution,” IARA Publication , 2023
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P. Budhwar, S. Chowdhury, G. Wood, H. Aguinis, G. J. Bamber, J. R. Beltran, P. Boselie, F. Lee Cooke, S. Decker, A. DeNisi et al. , “Human resource management in the age of generative artificial intelligence: Perspectives and research directions on chatgpt,” Human Resource Management Journal , vol. 33, no. 3, pp. 606–659, 2023
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S. A. Bin-Nashwan, M. Sadallah, and M. Bouteraa, “Use of chatgpt in academia: Academic integrity hangs in the balance,” Technology in Society , vol. 75, p. 102370, 2023
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N. Liu, A. Brown et al. , “Ai increases the pressure to overhaul the scientific peer review process. comment on “artificial intelligence can generate fraudulent but authentic-looking scientific medical articles: Pandora’s box has been opened”,” J Med Internet Res , vol. 25, p. e50591, 2023
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