Surveying the Evolution and Future Trajectory of Generative AI - Conclusions, References

Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References XI. CONCLUSIONS This roadmap survey has embarked on an exploration of the transformative trends in generative AI research, particularly focusing on speculated advancements like Q* and the progressive strides towards AGI. Our analysis highlights a crucial paradigm shift, driven by innovations such as MoE, multimodal learning, and the pursuit of AGI. These advancements signal a future where AI systems could significantly extend their capabilities in reasoning, contextual understanding, and creative problem-solving. This study reflects on AI’s dual potential to either contribute to or impede global equity and justice. The equitable distribution of AI benefits and its role in decision-making processes raise crucial questions about fairness and inclusivity. It is imperative to thoughtfully integrate AI into societal structures to enhance justice and reduce disparities. Despite these advancements, several open questions and research gaps remain. These include ensuring the ethical alignment of advanced AI systems with human values and societal norms, a challenge compounded by their increasing autonomy. The safety and robustness of AGI systems in diverse environments also remain a significant research gap. Addressing these challenges requires a multidisciplinary approach, incorporating ethical, social, and philosophical perspectives. Our survey has highlighted key areas for future interdisciplinary research in AI, emphasizing the integration of ethical, sociological, and technical perspectives. This approach will foster collaborative research, bridging the gap between technological advancement and societal needs, ensuring that AI development is aligned with human values and global welfare. The roles of MoE, multimodal, and AGI in reshaping generative AI have been identified as significant, as their advancements can enhance model performance and versatility, and pave the way for future research in areas like ethical AI alignment and AGI. As we forge ahead, the balance between AI advancements and human creativity is not just a goal but a necessity, ensuring AI’s role as a complementary force that amplifies our capacity to innovate and solve complex challenges. Our responsibility is to guide these advancements towards enriching the human experience, aligning technological progress with ethical standards and societal well-being. DISCLAIMER The authors hereby declare no conflict of interest. ABBREVIATIONS AGI Artificial General Intelligence AI Artificial Intelligence AIGC AI-generated content BERT Bidirectional Encoder Representations from Transformers CCPA California Consumer Privacy Act DQN Deep Q-Networks EU European Union GAN Generative Adversarial Network GDPR General Data Protection Regulation GPT Generative Pre-trained Transformers GPU Graphics Processing Unit LIDAR Light Detection and Ranging LLM Large Language Model LSTM Long Short-Term Memory MCTS Monte Carlo Tree Search ML Machine Learning MoE Mixture of Experts NLG Natural Language Generation NLP Natural Language Processing NLU Natural Language Understanding NN Neural Network PPO Proximal Policy Optimization RNNs Recurrent Neural Networks VNN Value Neural Network VRAM Video Random Access Memory REFERENCES [1] A. Turing, “Computing machinery and intelligence,” Mind, vol. 59, no. 236, p. 433, 1950. [2] D. McDermott, “Artificial intelligence meets natural stupidity,” Acm Sigart Bulletin, no. 57, pp. 4–9, 1976. [3] M. Minsky, “Steps toward artificial intelligence,” Proceedings of the IRE, vol. 49, no. 1, pp. 8–30, 1961. [4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015. [5] M. Minsky and S. Papert, “An introduction to computational geometry,” Cambridge tiass., HIT, vol. 479, no. 480, p. 104, 1969. [6] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986. [7] G.-G. Lee, L. Shi, E. Latif, Y. Gao, A. Bewersdorf, M. Nyaaba, S. Guo, Z. Wu, Z. Liu, H. Wang et al., “Multimodality of ai for education: Towards artificial general intelligence,” arXiv preprint arXiv:2312.06037, 2023. [8] P. Maddigan and T. Susnjak, “Chat2vis: Generating data visualisations via natural language using chatgpt, codex and gpt-3 large language models,” IEEE Access, 2023. [9] T. R. McIntosh, T. Liu, T. Susnjak, P. Watters, A. Ng, and M. N. Halgamuge, “A culturally sensitive test to evaluate nuanced gpt hallucination,” IEEE Transactions on Artificial Intelligence, vol. 1, no. 01, pp. 1–13, 2023. [10] M. R. Morris, J. Sohl-dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg, “Levels of agi: Operationalizing progress on the path to agi,” arXiv preprint arXiv:2311.02462, 2023. [11] J. Schuett, N. Dreksler, M. Anderljung, D. McCaffary, L. Heim, E. Bluemke, and B. Garfinkel, “Towards best practices in agi safety and governance: A survey of expert opinion,” arXiv preprint arXiv:2305.07153, 2023. [12] X. Shuai, J. Rollins, I. Moulinier, T. Custis, M. Edmunds, and F. Schilder, “A multidimensional investigation of the effects of publication retraction on scholarly impact,” Journal of the Association for Information Science and Technology, vol. 68, no. 9, pp. 2225–2236, 2017. [13] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017. [14] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al., “Improving language understanding by generative pre-training,” 2018. [15] C. Huang, Z. Zhang, B. Mao, and X. Yao, “An overview of artificial intelligence ethics,” IEEE Transactions on Artificial Intelligence, 2022. [16] L. Besanc¸on, N. Peiffer-Smadja, C. Segalas, H. Jiang, P. Masuzzo, C. Smout, E. Billy, M. Deforet, and C. Leyrat, “Open science saves lives: lessons from the covid-19 pandemic,” BMC Medical Research Methodology, vol. 21, no. 1, pp. 1–18, 2021. [17] C. R. Triggle, R. MacDonald, D. J. Triggle, and D. Grierson, “Requiem for impact factors and high publication charges,” Accountability in Research, vol. 29, no. 3, pp. 133–164, 2022. [18] T. McIntosh, A. Kayes, Y.-P. P. Chen, A. Ng, and P. Watters, “Ransomware mitigation in the modern era: A comprehensive review, research challenges, and future directions,” ACM Computing Surveys (CSUR), vol. 54, no. 9, pp. 1–36, 2021. [19] T. McIntosh, T. Liu, T. Susnjak, H. Alavizadeh, A. Ng, R. Nowrozy, and P. Watters, “Harnessing gpt-4 for generation of cybersecurity grc policies: A focus on ransomware attack mitigation,” Computers & Security, vol. 134, p. 103424, 2023. [20] H. Bao, W. Wang, L. Dong, Q. Liu, O. K. Mohammed, K. Aggarwal, S. Som, S. Piao, and F. Wei, “Vlmo: Unified vision-language pretraining with mixture-of-modality-experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 32 897–32 912, 2022. [21] N. Du, Y. Huang, A. M. Dai, S. Tong, D. Lepikhin, Y. Xu, M. Krikun, Y. Zhou, A. W. Yu, O. Firat et al., “Glam: Efficient scaling of language models with mixture-of-experts,” in International Conference on Machine Learning. PMLR, 2022, pp. 5547–5569. [22] S. Masoudnia and R. Ebrahimpour, “Mixture of experts: a literature survey,” Artificial Intelligence Review, vol. 42, pp. 275–293, 2014. [23] C. Riquelme, J. Puigcerver, B. Mustafa, M. Neumann, R. Jenatton, A. Susano Pinto, D. Keysers, and N. Houlsby, “Scaling vision with sparse mixture of experts,” Advances in Neural Information Processing Systems, vol. 34, pp. 8583–8595, 2021. [24] S. E. Yuksel, J. N. Wilson, and P. D. Gader, “Twenty years of mixture of experts,” IEEE transactions on neural networks and learning systems, vol. 23, no. 8, pp. 1177–1193, 2012. [25] L. Zhang, S. Huang, W. Liu, and D. Tao, “Learning a mixture of granularity-specific experts for fine-grained categorization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 8331–8340. [26] D. Martin, S. Malpica, D. Gutierrez, B. Masia, and A. Serrano, “Multimodality in vr: A survey,” ACM Computing Surveys (CSUR), vol. 54, no. 10s, pp. 1–36, 2022. [27] Q. Sun, Q. Yu, Y. Cui, F. Zhang, X. Zhang, Y. Wang, H. Gao, J. Liu, T. Huang, and X. Wang, “Generative pretraining in multimodality,” arXiv preprint arXiv:2307.05222, 2023. [28] L. Wei, L. Xie, W. Zhou, H. Li, and Q. Tian, “Mvp: Multimodalityguided visual pre-training,” in European Conference on Computer Vision. Springer, 2022, pp. 337–353. [29] J. Wu, W. Zhou, X. Qian, J. Lei, L. Yu, and T. Luo, “Menet: Lightweight multimodality enhancement network for detecting salient objects in rgb-thermal images,” Neurocomputing, vol. 527, pp. 119– 129, 2023. [30] Q. Ye, H. Xu, G. Xu, J. Ye, M. Yan, Y. Zhou, J. Wang, A. Hu, P. Shi, Y. Shi et al., “mplug-owl: Modularization empowers large language models with multimodality,” arXiv preprint arXiv:2304.14178, 2023. [31] K. LaGrandeur, “How safe is our reliance on ai, and should we regulate it?” AI and Ethics, vol. 1, pp. 93–99, 2021. [32] S. McLean, G. J. Read, J. Thompson, C. Baber, N. A. Stanton, and P. M. Salmon, “The risks associated with artificial general intelligence: A systematic review,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 35, no. 5, pp. 649–663, 2023. [33] Y. K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan, R. Dwivedi, J. Edwards, A. Eirug, V. Galanos, P. V. Ilavarasan, M. Janssen, P. Jones, A. K. Kar, H. Kizgin, B. Kronemann, B. Lal, B. Lucini, R. Medaglia, K. Le Meunier-FitzHugh, L. C. Le Meunier-FitzHugh, S. Misra, E. Mogaji, S. K. Sharma, J. B. Singh, V. Raghavan, R. Raman, N. P. Rana, S. Samothrakis, J. Spencer, K. Tamilmani, A. Tubadji, P. Walton, and M. D. Williams, “Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management, vol. 57, p. 101994, 2021. [34] I. Gabriel, “Artificial intelligence, values, and alignment,” Minds and Machines, vol. 30, pp. 411–437, 2020. [35] A. Shaban-Nejad, M. Michalowski, S. Bianco, J. S. Brownstein, D. L. Buckeridge, and R. L. Davis, “Applied artificial intelligence in healthcare: Listening to the winds of change in a post-covid-19 world,” pp. 1969–1971, 2022. [36] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023. [37] B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Computing Surveys, vol. 56, no. 2, pp. 1–40, 2023. [38] J. Li, X. Cheng, W. X. Zhao, J.-Y. Nie, and J.-R. Wen, “Halueval: A large-scale hallucination evaluation benchmark for large language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, pp. 6449–6464. [39] L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese, B. Balle, A. Kasirzadeh et al., “Ethical and social risks of harm from language models,” arXiv preprint arXiv:2112.04359, 2021. [40] X. Zhiheng, Z. Rui, and G. Tao, “Safety and ethical concerns of large language models,” in Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 4: Tutorial Abstracts), 2023, pp. 9–16. [41] P. F. Brown, V. J. Della Pietra, P. V. Desouza, J. C. Lai, and R. L. Mercer, “Class-based n-gram models of natural language,” Computational linguistics, vol. 18, no. 4, pp. 467–480, 1992. [42] S. Katz, “Estimation of probabilities from sparse data for the language model component of a speech recognizer,” IEEE transactions on acoustics, speech, and signal processing, vol. 35, no. 3, pp. 400–401, 1987. [43] R. Kneser and H. Ney, “Improved backing-off for m-gram language modeling,” in 1995 international conference on acoustics, speech, and signal processing, vol. 1. IEEE, 1995, pp. 181–184. [44] R. Kuhn and R. De Mori, “A cache-based natural language model for speech recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 12, no. 6, pp. 570–583, 1990. [45] H. Ney, U. Essen, and R. Kneser, “On structuring probabilistic dependences in stochastic language modelling,” Computer Speech & Language, vol. 8, no. 1, pp. 1–38, 1994. [46] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997. [47] M. K. Nammous and K. Saeed, “Natural language processing: speaker, language, and gender identification with lstm,” Advanced Computing and Systems for Security: Volume Eight, pp. 143–156, 2019. [48] D. Wei, B. Wang, G. Lin, D. Liu, Z. Dong, H. Liu, and Y. Liu, “Research on unstructured text data mining and fault classification based on rnn-lstm with malfunction inspection report,” Energies, vol. 10, no. 3, p. 406, 2017. [49] L. Yao and Y. Guan, “An improved lstm structure for natural language processing,” in 2018 IEEE International Conference of Safety Produce Informatization (IICSPI). IEEE, 2018, pp. 565–569. [50] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al., “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems, vol. 35, pp. 27 730–27 744, 2022. [51] T. Susnjak, “Beyond predictive learning analytics modelling and onto explainable artificial intelligence with prescriptive analytics and chatgpt,” International Journal of Artificial Intelligence in Education, pp. 1–31, 2023. [52] T. Susnjak, E. Griffin, M. McCutcheon, and K. Potter, “Towards clinical prediction with transparency: An explainable ai approach to survival modelling in residential aged care,” arXiv preprint arXiv:2312.00271, 2023. [53] R. Yang, T. F. Tan, W. Lu, A. J. Thirunavukarasu, D. S. W. Ting, and N. Liu, “Large language models in health care: Development, applications, and challenges,” Health Care Science, vol. 2, no. 4, pp. 255–263, 2023. [54] D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning,” Journal of AI, vol. 7, no. 1, pp. 52–62, 2023. [55] T. Susnjak, “Chatgpt: The end of online exam integrity?” arXiv preprint arXiv:2212.09292, 2022. [56] A. Tlili, B. Shehata, M. A. Adarkwah, A. Bozkurt, D. T. Hickey, R. Huang, and B. Agyemang, “What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education,” Smart Learning Environments, vol. 10, no. 1, p. 15, 2023. [57] M. A. AlAfnan, S. Dishari, M. Jovic, and K. Lomidze, “Chatgpt as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses,” Journal of Artificial Intelligence and Technology, vol. 3, no. 2, pp. 60–68, 2023. [58] A. S. George and A. H. George, “A review of chatgpt ai’s impact on several business sectors,” Partners Universal International Innovation Journal, vol. 1, no. 1, pp. 9–23, 2023. [59] G. K. Hadfield and J. Clark, “Regulatory markets: The future of ai governance,” arXiv preprint arXiv:2304.04914, 2023. [60] M. Bakker, M. Chadwick, H. Sheahan, M. Tessler, L. CampbellGillingham, J. Balaguer, N. McAleese, A. Glaese, J. Aslanides, M. Botvinick et al., “Fine-tuning language models to find agreement among humans with diverse preferences,” Advances in Neural Information Processing Systems, vol. 35, pp. 38 176–38 189, 2022. [61] Z. Hu, Y. Lan, L. Wang, W. Xu, E.-P. Lim, R. K.-W. Lee, L. Bing, and S. Poria, “Llm-adapters: An adapter family for parameter-efficient finetuning of large language models,” arXiv preprint arXiv:2304.01933, 2023. [62] H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal, and C. A. Raffel, “Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,” Advances in Neural Information Processing Systems, vol. 35, pp. 1950–1965, 2022. [63] H. Zheng, L. Shen, A. Tang, Y. Luo, H. Hu, B. Du, and D. Tao, “Learn from model beyond fine-tuning: A survey,” arXiv preprint arXiv:2310.08184, 2023. [64] P. Manakul, A. Liusie, and M. J. Gales, “Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models,” arXiv preprint arXiv:2303.08896, 2023. [65] A. Martino, M. Iannelli, and C. Truong, “Knowledge injection to counter large language model (llm) hallucination,” in European Semantic Web Conference. Springer, 2023, pp. 182–185. [66] J.-Y. Yao, K.-P. Ning, Z.-H. Liu, M.-N. Ning, and L. Yuan, “Llm lies: Hallucinations are not bugs, but features as adversarial examples,” arXiv preprint arXiv:2310.01469, 2023. [67] Y. Zhang, Y. Li, L. Cui, D. Cai, L. Liu, T. Fu, X. Huang, E. Zhao, Y. Zhang, Y. Chen et al., “Siren’s song in the ai ocean: A survey on hallucination in large language models,” arXiv preprint arXiv:2309.01219, 2023. [68] J. Ji, M. Liu, J. Dai, X. Pan, C. Zhang, C. Bian, R. Sun, Y. Wang, and Y. Yang, “Beavertails: Towards improved safety alignment of llm via a human-preference dataset,” arXiv preprint arXiv:2307.04657, 2023. [69] Y. Liu, Y. Yao, J.-F. Ton, X. Zhang, R. G. H. Cheng, Y. Klochkov, M. F. Taufiq, and H. Li, “Trustworthy llms: a survey and guideline for evaluating large language models’ alignment,” arXiv preprint arXiv:2308.05374, 2023. [70] Y. Wang, W. Zhong, L. Li, F. Mi, X. Zeng, W. Huang, L. Shang, X. Jiang, and Q. Liu, “Aligning large language models with human: A survey,” arXiv preprint arXiv:2307.12966, 2023. [71] Z. Sun, Y. Shen, Q. Zhou, H. Zhang, Z. Chen, D. Cox, Y. Yang, and C. Gan, “Principle-driven self-alignment of language models from scratch with minimal human supervision,” arXiv preprint arXiv:2305.03047, 2023. [72] Y. Wolf, N. Wies, Y. Levine, and A. Shashua, “Fundamental limitations of alignment in large language models,” arXiv preprint arXiv:2304.11082, 2023. [73] H. Dang, L. Mecke, F. Lehmann, S. Goller, and D. Buschek, “How to prompt? opportunities and challenges of zero-and few-shot learning for human-ai interaction in creative applications of generative models,” arXiv preprint arXiv:2209.01390, 2022. [74] R. Ma, X. Zhou, T. Gui, Y. Tan, L. Li, Q. Zhang, and X. Huang, “Template-free prompt tuning for few-shot ner,” arXiv preprint arXiv:2109.13532, 2021. [75] C. Qin and S. Joty, “Lfpt5: A unified framework for lifelong fewshot language learning based on prompt tuning of t5,” arXiv preprint arXiv:2110.07298, 2021. [76] S. Wang, L. Tang, A. Majety, J. F. Rousseau, G. Shih, Y. Ding, and Y. Peng, “Trustworthy assertion classification through prompting,” Journal of biomedical informatics, vol. 132, p. 104139, 2022. [77] 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. [78] D. Liga and L. Robaldo, “Fine-tuning gpt-3 for legal rule classification,” Computer Law & Security Review, vol. 51, p. 105864, 2023. [79] Y. Liu, A. Singh, C. D. Freeman, J. D. Co-Reyes, and P. J. Liu, “Improving large language model fine-tuning for solving math problems,” arXiv preprint arXiv:2310.10047, 2023. [80] Z. Talat, A. N´ev´eol, S. Biderman, M. Clinciu, M. Dey, S. Longpre, S. Luccioni, M. Masoud, M. Mitchell, D. Radev et al., “You reap what you sow: On the challenges of bias evaluation under multilingual settings,” in Proceedings of BigScience Episode# 5–Workshop on Challenges & Perspectives in Creating Large Language Models, 2022, pp. 26–41. [81] Y. Liu, S. Yu, and T. Lin, “Hessian regularization of deep neural networks: A novel approach based on stochastic estimators of hessian trace,” Neurocomputing, vol. 536, pp. 13–20, 2023. [82] Y. Lu, Y. Bo, and W. He, “Confidence adaptive regularization for deep learning with noisy labels,” arXiv preprint arXiv:2108.08212, 2021. [83] G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton, “Regularizing neural networks by penalizing confident output distributions,” arXiv preprint arXiv:1701.06548, 2017. [84] E. Chen, Z.-W. Hong, J. Pajarinen, and P. Agrawal, “Redeeming intrinsic rewards via constrained optimization,” Advances in Neural Information Processing Systems, vol. 35, pp. 4996–5008, 2022. [85] Y. Jiang, Z. Li, M. Tan, S. Wei, G. Zhang, Z. Guan, and B. Han, “A stable block adjustment method without ground control points using bound constrained optimization,” International Journal of Remote Sensing, vol. 43, no. 12, pp. 4708–4722, 2022. [86] M. Kachuee and S. Lee, “Constrained policy optimization for controlled self-learning in conversational ai systems,” arXiv preprint arXiv:2209.08429, 2022. [87] Z. Song, H. Wang, and Y. Jin, “A surrogate-assisted evolutionary framework with regions of interests-based data selection for expensive constrained optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023. [88] J. Yu, T. Xu, Y. Rong, J. Huang, and R. He, “Structure-aware conditional variational auto-encoder for constrained molecule optimization,” Pattern Recognition, vol. 126, p. 108581, 2022. [89] P. Butlin, “Ai alignment and human reward,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 437–445. [90] 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. [91] J. Leike, D. Krueger, T. Everitt, M. Martic, V. Maini, and S. Legg, “Scalable agent alignment via reward modeling: a research direction,” arXiv preprint arXiv:1811.07871, 2018. [92] L. Li, Y. Chai, S. Wang, Y. Sun, H. Tian, N. Zhang, and H. Wu, “Toolaugmented reward modeling,” arXiv preprint arXiv:2310.01045, 2023. [93] F. Barreto, L. Moharkar, M. Shirodkar, V. Sarode, S. Gonsalves, and A. Johns, “Generative artificial intelligence: Opportunities and challenges of large language models,” in International Conference on Intelligent Computing and Networking. Springer, 2023, pp. 545–553. [94] Z. Chen, Z. Wang, Z. Wang, H. Liu, Z. Yin, S. Liu, L. Sheng, W. Ouyang, Y. Qiao, and J. Shao, “Octavius: Mitigating task interference in mllms via moe,” arXiv preprint arXiv:2311.02684, 2023. [95] C. Dun, M. D. C. H. Garcia, G. Zheng, A. H. Awadallah, A. Kyrillidis, and R. Sim, “Sweeping heterogeneity with smart mops: Mixture of prompts for llm task adaptation,” arXiv preprint arXiv:2310.02842, 2023. [96] H. Naveed, A. U. Khan, S. Qiu, M. Saqib, S. Anwar, M. Usman, N. Barnes, and A. Mian, “A comprehensive overview of large language models,” arXiv preprint arXiv:2307.06435, 2023. [97] F. Xue, Y. Fu, W. Zhou, Z. Zheng, and Y. You, “To repeat or not to repeat: Insights from scaling llm under token-crisis,” arXiv preprint arXiv:2305.13230, 2023. [98] M. Nowaz Rabbani Chowdhury, S. Zhang, M. Wang, S. Liu, and P.-Y. Chen, “Patch-level routing in mixture-of-experts is provably sampleefficient for convolutional neural networks,” arXiv e-prints, pp. arXiv– 2306, 2023. [99] 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. [100] C. N. d. Santos, J. Lee-Thorp, I. Noble, C.-C. Chang, and D. Uthus, “Memory augmented language models through mixture of word experts,” arXiv preprint arXiv:2311.10768, 2023. [101] W. Wang, G. Ma, Y. Li, and B. Du, “Language-routing mixture of experts for multilingual and code-switching speech recognition,” arXiv preprint arXiv:2307.05956, 2023. [102] X. Zhao, X. Chen, Y. Cheng, and T. Chen, “Sparse moe with language guided routing for multilingual machine translation,” in Conference on Parsimony and Learning (Recent Spotlight Track), 2023. [103] W. Huang, H. Zhang, P. Peng, and H. Wang, “Multi-gate mixtureof-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. [104] B. Liu, L. Ding, L. Shen, K. Peng, Y. Cao, D. Cheng, and D. Tao, “Diversifying the mixture-of-experts representation for language models with orthogonal optimizer,” arXiv preprint arXiv:2310.09762, 2023. [105] W. Wang, Z. Lai, S. Li, W. Liu, K. Ge, Y. Liu, A. Shen, and D. Li, “Prophet: Fine-grained load balancing for parallel training of largescale moe models,” in 2023 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2023, pp. 82–94. [106] X. Yao, S. Liang, S. Han, and H. Huang, “Enhancing molecular property prediction via mixture of collaborative experts,” arXiv preprint arXiv:2312.03292, 2023. [107] Z. Xiao, Y. Jiang, G. Tang, L. Liu, S. Xu, Y. Xiao, and W. Yan, “Adversarial mixture of experts with category hierarchy soft constraint,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021, pp. 2453–2463. [108] 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. [109] Z. Chen, Y. Deng, Y. Wu, Q. Gu, and Y. Li, “Towards understanding the mixture-of-experts layer in deep learning,” Advances in neural information processing systems, vol. 35, pp. 23 049–23 062, 2022. [110] Y. Zhou, T. Lei, H. Liu, N. Du, Y. Huang, V. Zhao, A. M. Dai, Q. V. Le, J. Laudon et al., “Mixture-of-experts with expert choice routing,” Advances in Neural Information Processing Systems, vol. 35, pp. 7103– 7114, 2022. [111] N. Guha, C. Lawrence, L. A. Gailmard, K. Rodolfa, F. Surani, R. Bommasani, I. Raji, M.-F. Cu´ellar, 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. [112] 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 [113] J. N. Acosta, G. J. Falcone, P. Rajpurkar, and E. J. Topol, “Multimodal biomedical ai,” Nature Medicine, vol. 28, no. 9, pp. 1773–1784, 2022. [114] 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. [115] B. Xu, D. Kocyigit, R. Grimm, B. P. Griffin, and F. Cheng, “Applications of artificial intelligence in multimodality cardiovascular imaging: a state-of-the-art review,” Progress in cardiovascular diseases, vol. 63, no. 3, pp. 367–376, 2020. [116] A. Birhane, V. U. Prabhu, and E. Kahembwe, “Multimodal datasets: misogyny, pornography, and malignant stereotypes,” arXiv preprint arXiv:2110.01963, 2021. [117] Y. Li, W. Li, N. Li, X. Qiu, and K. B. Manokaran, “Multimodal information interaction and fusion for the parallel computing system using ai techniques,” International Journal of High Performance Systems Architecture, vol. 10, no. 3-4, pp. 185–196, 2021. [118] C. Zhang, Z. Yang, X. He, and L. Deng, “Multimodal intelligence: Representation learning, information fusion, and applications,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 3, pp. 478–493, 2020. [119] H. Qiao, V. Liu, and L. Chilton, “Initial images: using image prompts to improve subject representation in multimodal ai generated art,” in Proceedings of the 14th Conference on Creativity and Cognition, 2022, pp. 15–28. [120] A. E. Stewart, Z. Keirn, and S. K. D’Mello, “Multimodal modeling of collaborative problem-solving facets in triads,” User Modeling and User-Adapted Interaction, pp. 1–39, 2021. [121] L. Xue, N. Yu, S. Zhang, J. Li, R. Mart´ın-Mart´ın, J. Wu, C. Xiong, R. Xu, J. C. Niebles, and S. Savarese, “Ulip-2: Towards scalable multimodal pre-training for 3d understanding,” arXiv preprint arXiv:2305.08275, 2023. [122] L. Yan, L. Zhao, D. Gasevic, and R. Martinez-Maldonado, “Scalability, sustainability, and ethicality of multimodal learning analytics,” in LAK22: 12th international learning analytics and knowledge conference, 2022, pp. 13–23. [123] Y. Liu-Thompkins, S. Okazaki, and H. Li, “Artificial empathy in marketing interactions: Bridging the human-ai gap in affective and social customer experience,” Journal of the Academy of Marketing Science, vol. 50, no. 6, pp. 1198–1218, 2022. [124] 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. [125] E. Sachdeva, N. Agarwal, S. Chundi, S. Roelofs, J. Li, B. Dariush, C. Choi, and M. Kochenderfer, “Rank2tell: A multimodal driving dataset for joint importance ranking and reasoning,” arXiv preprint arXiv:2309.06597, 2023. [126] C. Cui, Y. Ma, X. Cao, W. Ye, Y. Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao et al., “A survey on multimodal large language models for autonomous driving,” arXiv preprint arXiv:2311.12320, 2023. [127] A. B. Temsamani, A. K. Chavali, W. Vervoort, T. Tuytelaars, G. Radevski, H. Van Hamme, K. Mets, M. Hutsebaut-Buysse, T. De Schepper, and S. Latr´e, “A multimodal ai approach for intuitively instructable autonomous systems: a case study of an autonomous off-highway vehicle,” in The Eighteenth International Conference on Autonomic and Autonomous Systems, ICAS 2022, May 22-26, 2022, Venice, Italy, 2022, pp. 31–39. [128] J. Lee and S. Y. Shin, “Something that they never said: Multimodal disinformation and source vividness in understanding the power of aienabled deepfake news,” Media Psychology, vol. 25, no. 4, pp. 531– 546, 2022. [129] S. Muppalla, S. Jia, and S. Lyu, “Integrating audio-visual features for multimodal deepfake detection,” arXiv preprint arXiv:2310.03827, 2023. [130] S. Kumar, M. K. Chaube, S. N. Nenavath, S. K. Gupta, and S. K. Tetarave, “Privacy preservation and security challenges: a new frontier multimodal machine learning research,” International Journal of Sensor Networks, vol. 39, no. 4, pp. 227–245, 2022. [131] J. Marchang and A. Di Nuovo, “Assistive multimodal robotic system (amrsys): security and privacy issues, challenges, and possible solutions,” Applied Sciences, vol. 12, no. 4, p. 2174, 2022. [132] A. Pe˜na, I. Serna, A. Morales, J. Fierrez, A. Ortega, A. Herrarte, M. Alcantara, and J. Ortega-Garcia, “Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment,” SN Computer Science, vol. 4, no. 5, p. 434, 2023. [133] R. Wolfe and A. Caliskan, “American== white in multimodal languageand-image ai,” in Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, pp. 800–812. [134] R. Wolfe, Y. Yang, B. Howe, and A. Caliskan, “Contrastive languagevision 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. [135] 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. [136] 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. [137] 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. [138] 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. [139] Y. Gao, M. Zhou, D. Liu, Z. Yan, S. Zhang, and D. N. Metaxas, “A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark,” arXiv preprint arXiv:2203.00131, 2022. [140] 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. [141] R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, J. Heek, K. Xiao, S. Agrawal, and J. Dean, “Efficiently scaling transformer inference,” Proceedings of Machine Learning and Systems, vol. 5, 2023. [142] Y. Ding and M. Jia, “Convolutional transformer: An enhanced attention mechanism architecture for remaining useful life estimation of bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022. [143] Y. Ding, M. Jia, Q. Miao, and Y. Cao, “A novel time–frequency transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings,” Mechanical Systems and Signal Processing, vol. 168, p. 108616, 2022. [144] G. Wang, Y. Zhao, C. Tang, C. Luo, and W. Zeng, “When shift operation meets vision transformer: An extremely simple alternative to attention mechanism,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 2, 2022, pp. 2423–2430. [145] 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. [146] 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. [147] 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. [148] 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. [149] S. Tamang, B. Sen, A. Pradhan, K. Sharma, and V. K. Singh, “Enhancing covid-19 safety: Exploring yolov8 object detection for accurate face mask classification,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2, pp. 892–897, 2023. [150] J. Lu, R. Xiong, J. Tian, C. Wang, C.-W. Hsu, N.-T. Tsou, F. Sun, and J. Li, “Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning,” Energy Storage Materials, vol. 50, pp. 139–151, 2022. [151] A. Onan, “Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 5, pp. 2098–2117, 2022. [152] F. Shan, X. He, D. J. Armaghani, P. Zhang, and D. Sheng, “Success and challenges in predicting tbm penetration rate using recurrent neural networks,” Tunnelling and Underground Space Technology, vol. 130, p. 104728, 2022. [153] C. Sridhar, P. K. Pareek, R. Kalidoss, S. S. Jamal, P. K. Shukla, S. J. Nuagah et al., “Optimal medical image size reduction model creation using recurrent neural network and genpsowvq,” Journal of Healthcare Engineering, vol. 2022, 2022. [154] J. Zhu, Q. Jiang, Y. Shen, C. Qian, F. Xu, and Q. Zhu, “Application of recurrent neural network to mechanical fault diagnosis: A review,” Journal of Mechanical Science and Technology, vol. 36, no. 2, pp. 527–542, 2022. [155] S. Lin, W. Lin, W. Wu, F. Zhao, R. Mo, and H. Zhang, “Segrnn: Segment recurrent neural network for long-term time series forecasting,” arXiv preprint arXiv:2308.11200, 2023. [156] Z. Wei, X. Zhang, and M. Sun, “Extracting weighted finite automata from recurrent neural networks for natural languages,” in International Conference on Formal Engineering Methods. Springer, 2022, pp. 370–385. [157] F. Bonassi, M. Farina, J. Xie, and R. Scattolini, “On recurrent neural networks for learning-based control: recent results and ideas for future developments,” Journal of Process Control, vol. 114, pp. 92–104, 2022. [158] 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. [159] 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. [160] P. Xu, X. Zhu, and D. A. Clifton, “Multimodal learning with transformers: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [161] I. Molenaar, S. de Mooij, R. Azevedo, M. Bannert, S. J¨arvel¨a, and D. Gaˇsevi´c, “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. [162] S. Steyaert, M. Pizurica, D. Nagaraj, P. Khandelwal, T. HernandezBoussard, 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. [163] V. Rani, S. T. Nabi, M. Kumar, A. Mittal, and K. Kumar, “Selfsupervised learning: A succinct review,” Archives of Computational Methods in Engineering, vol. 30, no. 4, pp. 2761–2775, 2023. [164] 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. [165] 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. [166] 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. [167] 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. [168] 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. [169] Q. Li, X. Peng, Y. Qiao, and Q. Hao, “Unsupervised person reidentification with multi-label learning guided self-paced clustering,” Pattern Recognition, vol. 125, p. 108521, 2022. [170] P. Nancy, H. Pallathadka, M. Naved, K. Kaliyaperumal, K. Arumugam, and V. Garchar, “Deep learning and machine learning based efficient framework for image based plant disease classification and detection,” in 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA). IEEE, 2022, pp. 1–6. [171] P. An, Z. Wang, and C. Zhang, “Ensemble unsupervised autoencoders and gaussian mixture model for cyberattack detection,” Information Processing & Management, vol. 59, no. 2, p. 102844, 2022. [172] 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. [173] E. Ayanoglu, K. Davaslioglu, and Y. E. Sagduyu, “Machine learning in nextg networks via generative adversarial networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 480–501, 2022. [174] K. Yan, X. Chen, X. Zhou, Z. Yan, and J. Ma, “Physical model informed fault detection and diagnosis of air handling units based on transformer generative adversarial network,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2192–2199, 2022. [175] 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. [176] P. Ladosz, L. Weng, M. Kim, and H. Oh, “Exploration in deep reinforcement learning: A survey,” Information Fusion, vol. 85, pp. 1–22, 2022. [177] Y. Matsuo, Y. LeCun, M. Sahani, D. Precup, D. Silver, M. Sugiyama, E. Uchibe, and J. Morimoto, “Deep learning, reinforcement learning, and world models,” Neural Networks, vol. 152, pp. 267–275, 2022. [178] D. Bertoin, A. Zouitine, M. Zouitine, and E. Rachelson, “Look where you look! saliency-guided q-networks for generalization in visual reinforcement learning,” Advances in Neural Information Processing Systems, vol. 35, pp. 30 693–30 706, 2022. [179] A. Hafiz, “A survey of deep q-networks used for reinforcement learning: State of the art,” Intelligent Communication Technologies and Virtual Mobile Networks: Proceedings of ICICV 2022, pp. 393–402, 2022. [180] A. Hafiz, M. Hassaballah, A. Alqahtani, S. Alsubai, and M. A. Hameed, “Reinforcement learning with an ensemble of binary action deep qnetworks.” Computer Systems Science & Engineering, vol. 46, no. 3, 2023. [181] A. Alagha, S. Singh, R. Mizouni, J. Bentahar, and H. Otrok, “Target localization using multi-agent deep reinforcement learning with proximal policy optimization,” Future Generation Computer Systems, vol. 136, pp. 342–357, 2022. [182] S. S. Hassan, Y. M. Park, Y. K. Tun, W. Saad, Z. Han, and C. S. Hong, “3to: Thz-enabled throughput and trajectory optimization of uavs in 6g networks by proximal policy optimization deep reinforcement learning,” in ICC 2022-IEEE International Conference on Communications. IEEE, 2022, pp. 5712–5718. [183] A. K. Jayant and S. Bhatnagar, “Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm,” Advances in Neural Information Processing Systems, vol. 35, pp. 24 432–24 445, 2022. [184] B. Lin, “Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook,” Expert Systems with Applications, p. 122254, 2023. [185] 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. [186] A. Raza, K. P. Tran, L. Koehl, and S. Li, “Designing ecg monitoring healthcare system with federated transfer learning and explainable ai,” Knowledge-Based Systems, vol. 236, p. 107763, 2022. [187] S. Siahpour, X. Li, and J. Lee, “A novel transfer learning approach in remaining useful life prediction for incomplete dataset,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–11, 2022. [188] Z. Guo, K. Lin, X. Chen, and C.-Y. Chit, “Transfer learning for angle of arrivals estimation in massive mimo system,” in 2022 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2022, pp. 506–511. [189] S. Liu, Y. Lu, P. Zheng, H. Shen, and J. Bao, “Adaptive reconstruction of digital twins for machining systems: A transfer learning approach,” Robotics and Computer-Integrated Manufacturing, vol. 78, p. 102390, 2022. [190] 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. [191] Y. Meng, J. Huang, Y. Zhang, and J. Han, “Generating training data with language models: Towards zero-shot language understanding,” Advances in Neural Information Processing Systems, vol. 35, pp. 462– 477, 2022. [192] R. M. Samant, M. R. Bachute, S. Gite, and K. Kotecha, “Framework for deep learning-based language models using multi-task learning in natural language understanding: A systematic literature review and future directions,” IEEE Access, vol. 10, pp. 17 078–17 097, 2022. [193] H. Weld, X. Huang, S. Long, J. Poon, and S. C. Han, “A survey of joint intent detection and slot filling models in natural language understanding,” ACM Computing Surveys, vol. 55, no. 8, pp. 1–38, 2022. [194] 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. [195] A. Montejo-R´aez and S. M. Jim´enez-Zafra, “Current approaches and applications in natural language processing,” Applied Sciences, vol. 12, no. 10, p. 4859, 2022. [196] K. Vijayan, O. Anand, and A. Sahaj, “Language-agnostic text processing for information extraction,” in CS & IT Conference Proceedings, vol. 12, no. 23. CS & IT Conference Proceedings, 2022. [197] C. D. Manning, “Human language understanding & reasoning,” Daedalus, vol. 151, no. 2, pp. 127–138, 2022. [198] 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. [199] E. Erdem, M. Kuyu, S. Yagcioglu, A. Frank, L. Parcalabescu, B. Plank, A. Babii, O. Turuta, A. Erdem, I. Calixto et al., “Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning,” Journal of Artificial Intelligence Research, vol. 73, pp. 1131–1207, 2022. [200] J. Qian, L. Dong, Y. Shen, F. Wei, and W. Chen, “Controllable natural language generation with contrastive prefixes,” arXiv preprint arXiv:2202.13257, 2022. [201] 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. [202] A. K. Pandey and S. S. Roy, “Natural language generation using sequential models: A survey,” Neural Processing Letters, pp. 1–34, 2023. [203] J. Y. Khan and G. Uddin, “Automatic code documentation generation using gpt-3,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022, pp. 1–6. [204] 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. [205] T. Fu, S. Gao, X. Zhao, J.-r. Wen, and R. Yan, “Learning towards conversational ai: A survey,” AI Open, vol. 3, pp. 14–28, 2022. [206] 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. [207] 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. [208] S. Kusal, S. Patil, J. Choudrie, K. Kotecha, S. Mishra, and A. Abraham, “Ai-based conversational agents: A scoping review from technologies to future directions,” IEEE Access, 2022. [209] Z. Xiao, “Seeing us through machines: designing and building conversational ai to understand humans,” Ph.D. dissertation, University of Illinois at Urbana-Champaign, 2023. [210] 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. [211] 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. [212] 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. [213] 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. [214] B. Attard-Frost, A. De los R´ıos, 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. [215] A. Gardner, A. L. Smith, A. Steventon, E. Coughlan, and M. Oldfield, “Ethical funding for trustworthy ai: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy ai practice,” AI and Ethics, pp. 1–15, 2022. [216] J. Schuett, “Three lines of defense against risks from ai,” AI & SOCIETY, pp. 1–15, 2023. [217] M. Sloane and J. Zakrzewski, “German ai start-ups and “ai ethics”: Using a social practice lens for assessing and implementing sociotechnical innovation,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 935–947. [218] M. Vasconcelos, C. Cardonha, and B. Gonc¸alves, “Modeling epistemological principles for bias mitigation in ai systems: an illustration in hiring decisions,” in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, pp. 323–329. [219] Y. Yang, A. Gupta, J. Feng, P. Singhal, V. Yadav, Y. Wu, P. Natarajan, V. Hedau, and J. Joo, “Enhancing fairness in face detection in computer vision systems by demographic bias mitigation,” in Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, pp. 813– 822. [220] R. Schwartz, A. Vassilev, K. Greene, L. Perine, A. Burt, P. Hall et al., “Towards a standard for identifying and managing bias in artificial intelligence,” NIST special publication, vol. 1270, no. 10.6028, 2022. [221] W. Guo and A. Caliskan, “Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 122–133. [222] Y. Kong, “Are “intersectionally fair” ai algorithms really fair to women of color? a philosophical analysis,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 485–494. [223] Y. C. Tan and L. E. Celis, “Assessing social and intersectional biases in contextualized word representations,” Advances in neural information processing systems, vol. 32, 2019. [224] L. Cheng, A. Mosallanezhad, P. Sheth, and H. Liu, “Causal learning for socially responsible ai,” arXiv preprint arXiv:2104.12278, 2021. [225] J. D. Correa, J. Tian, and E. Bareinboim, “Identification of causal effects in the presence of selection bias,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 2744– 2751. [226] B. Ghai and K. Mueller, “D-bias: a causality-based human-in-theloop system for tackling algorithmic bias,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 473–482, 2022. [227] J. N. Yan, Z. Gu, H. Lin, and J. M. Rzeszotarski, “Silva: Interactively assessing machine learning fairness using causality,” in Proceedings of the 2020 chi conference on human factors in computing systems, 2020, pp. 1–13. [228] E. Bertino, M. Kantarcioglu, C. G. Akcora, S. Samtani, S. Mittal, and M. Gupta, “Ai for security and security for ai,” in Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy, 2021, pp. 333–334. [229] H. Susanto, L. F. Yie, D. Rosiyadi, A. I. Basuki, and D. Setiana, “Data security for connected governments and organisations: Managing automation and artificial intelligence,” in Web 2.0 and cloud technologies for implementing connected government. IGI Global, 2021, pp. 229– 251. [230] S. Dilmaghani, M. R. Brust, G. Danoy, N. Cassagnes, J. Pecero, and P. Bouvry, “Privacy and security of big data in ai systems: A research and standards perspective,” in 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019, pp. 5737–5743. [231] T. McIntosh, “Intercepting ransomware attacks with staged eventdriven access control,” Ph.D. dissertation, La Trobe, 2022. [232] 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. [233] P. Hummel, M. Braun, M. Tretter, and P. Dabrock, “Data sovereignty: A review,” Big Data & Society, vol. 8, no. 1, p. 2053951720982012, 2021. [234] M. Lukings and A. Habibi Lashkari, “Data sovereignty,” in Understanding Cybersecurity Law in Data Sovereignty and Digital Governance: An Overview from a Legal Perspective. Springer, 2022, pp. 1–38. [235] M. Hickok, “Lessons learned from ai ethics principles for future actions,” AI and Ethics, vol. 1, no. 1, pp. 41–47, 2021. [236] J. Zhou and F. Chen, “Ai ethics: From principles to practice,” AI & SOCIETY, pp. 1–11, 2022. [237] J. A. Kroll, “Outlining traceability: A principle for operationalizing accountability in computing systems,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp. 758–771. [238] A. Oseni, N. Moustafa, H. Janicke, P. Liu, Z. Tari, and A. Vasilakos, “Security and privacy for artificial intelligence: Opportunities and challenges,” arXiv preprint arXiv:2102.04661, 2021. [239] B. C. Stahl and D. Wright, “Ethics and privacy in ai and big data: Implementing responsible research and innovation,” IEEE Security & Privacy, vol. 16, no. 3, pp. 26–33, 2018. [240] 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. [241] M. Song, Z. Wang, Z. Zhang, Y. Song, Q. Wang, J. Ren, and H. Qi, “Analyzing user-level privacy attack against federated learning,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2430–2444, 2020. [242] I. Misra and L. v. d. Maaten, “Self-supervised learning of pretextinvariant representations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 6707–6717. [243] X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, “S4l: Self-supervised semi-supervised learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 1476–1485. [244] T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby, “Self-supervised gans via auxiliary rotation loss,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 12 154–12 163. [245] S. Jenni and P. Favaro, “Self-supervised feature learning by learning to spot artifacts,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2733–2742. [246] P. Patel, N. Kumari, M. Singh, and B. Krishnamurthy, “Lt-gan: Selfsupervised gan with latent transformation detection,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2021, pp. 3189–3198. [247] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning. PMLR, 2020, pp. 1597–1607. [248] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9729–9738. [249] A. T. Liu, S.-W. Li, and H.-y. Lee, “Tera: Self-supervised learning of transformer encoder representation for speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 2351– 2366, 2021. [250] Y. Pang, W. Wang, F. E. Tay, W. Liu, Y. Tian, and L. Yuan, “Masked autoencoders for point cloud self-supervised learning,” in European conference on computer vision. Springer, 2022, pp. 604–621. [251] T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, “Metalearning in neural networks: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 9, pp. 5149–5169, 2021. [252] R. Vilalta and Y. Drissi, “A perspective view and survey of metalearning,” Artificial intelligence review, vol. 18, pp. 77–95, 2002. [253] M. Al-Shedivat, L. Li, E. Xing, and A. Talwalkar, “On data efficiency of meta-learning,” in International Conference on Artificial Intelligence and Statistics. PMLR, 2021, pp. 1369–1377. [254] Y. Hu, R. Liu, X. Li, D. Chen, and Q. Hu, “Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data,” IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 3894– 3904, 2021. [255] S. Baik, J. Choi, H. Kim, D. Cho, J. Min, and K. M. Lee, “Metalearning with task-adaptive loss function for few-shot learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9465–9474. [256] Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang, “Meta-baseline: Exploring simple meta-learning for few-shot learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9062–9071. [257] M. A. Jamal and G.-J. Qi, “Task agnostic meta-learning for few-shot learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11 719–11 727. [258] R. Behnia, M. R. Ebrahimi, J. Pacheco, and B. Padmanabhan, “Ewtune: A framework for privately fine-tuning large language models with differential privacy,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2022, pp. 560–566. [259] J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le, “Finetuned language models are zero-shot learners,” arXiv preprint arXiv:2109.01652, 2021. [260] W. Kuang, B. Qian, Z. Li, D. Chen, D. Gao, X. Pan, Y. Xie, Y. Li, B. Ding, and J. Zhou, “Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,” arXiv preprint arXiv:2309.00363, 2023. [261] 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. [262] M. Engelbach, D. Klau, F. Scheerer, J. Drawehn, and M. Kintz, “Fine-tuning and aligning question answering models for complex information extraction tasks,” arXiv preprint arXiv:2309.14805, 2023. [263] T. T. Nguyen, C. Wilson, and J. Dalins, “Fine-tuning llama 2 large language models for detecting online sexual predatory chats and abusive texts,” arXiv preprint arXiv:2308.14683, 2023. [264] Q. Zhou, C. Yu, S. Zhang, S. Wu, Z. Wang, and F. Wang, “Regionblip: A unified multi-modal pre-training framework for holistic and regional comprehension,” arXiv preprint arXiv:2308.02299, 2023. [265] T. Arnold and D. Kasenberg, “Value alignment or misalignment - what will keep systems accountable?” in AAAI Workshop on AI, Ethics, and Society, 2017. [266] I. Gabriel and V. Ghazavi, “The challenge of value alignment: From fairer algorithms to ai safety,” arXiv preprint arXiv:2101.06060, 2021. [267] S. Nyholm, “Responsibility gaps, value alignment, and meaningful human control over artificial intelligence,” in Risk and responsibility in context. Routledge, 2023, pp. 191–213. [268] S. Wu, H. Fei, L. Qu, W. Ji, and T.-S. Chua, “Next-gpt: Any-to-any multimodal llm,” arXiv preprint arXiv:2309.05519, 2023. [269] K. Bayoudh, R. Knani, F. Hamdaoui, and A. Mtibaa, “A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets,” The Visual Computer, pp. 1–32, 2021. [270] P. Hu, L. Zhen, D. Peng, and P. Liu, “Scalable deep multimodal learning for cross-modal retrieval,” in Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, 2019, pp. 635–644. [271] A. Rahate, R. Walambe, S. Ramanna, and K. Kotecha, “Multimodal colearning: Challenges, applications with datasets, recent advances and future directions,” Information Fusion, vol. 81, pp. 203–239, 2022. [272] L. Che, J. Wang, Y. Zhou, and F. Ma, “Multimodal federated learning: A survey,” Sensors, vol. 23, no. 15, p. 6986, 2023. [273] P. P. Liang, Y. Lyu, X. Fan, Z. Wu, Y. Cheng, J. Wu, L. Chen, P. Wu, M. A. Lee, Y. Zhu et al., “Multibench: Multiscale benchmarks for multimodal representation learning,” arXiv preprint arXiv:2107.07502, 2021. [274] Z. Ashktorab, Q. V. Liao, C. Dugan, J. Johnson, Q. Pan, W. Zhang, S. Kumaravel, and M. Campbell, “Human-ai collaboration in a cooperative game setting: Measuring social perception and outcomes,” Proceedings of the ACM on Human-Computer Interaction, vol. 4, no. CSCW2, pp. 1–20, 2020. [275] P. Esmaeilzadeh, T. Mirzaei, and S. Dharanikota, “Patients’ perceptions toward human–artificial intelligence interaction in health care: experimental study,” Journal of medical Internet research, vol. 23, no. 11, p. e25856, 2021. [276] M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, “A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques,” IEEE Access, vol. 9, pp. 153 316–153 348, 2021. [277] A. S. Rajawat, R. Rawat, K. Barhanpurkar, R. N. Shaw, and A. Ghosh, “Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning,” in Artificial Intelligence for Future Generation Robotics. Elsevier, 2021, pp. 1–13. [278] S. Mohseni, N. Zarei, and E. D. Ragan, “A multidisciplinary survey and framework for design and evaluation of explainable ai systems,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 11, no. 3-4, pp. 1–45, 2021. [279] M. C. Buehler and T. H. Weisswange, “Theory of mind based communication for human agent cooperation,” in 2020 IEEE International Conference on Human-Machine Systems (ICHMS). IEEE, 2020, pp. 1–6. [280] M. M. C¸ elikok, T. Peltola, P. Daee, and S. Kaski, “Interactive ai with a theory of mind,” arXiv preprint arXiv:1912.05284, 2019. [281] A. Dafoe, E. Hughes, Y. Bachrach, T. Collins, K. R. McKee, J. Z. Leibo, K. Larson, and T. Graepel, “Open problems in cooperative ai,” arXiv preprint arXiv:2012.08630, 2020. [282] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al., “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712, 2023. [283] N. Fei, Z. Lu, Y. Gao, G. Yang, Y. Huo, J. Wen, H. Lu, R. Song, X. Gao, T. Xiang et al., “Towards artificial general intelligence via a multimodal foundation model,” Nature Communications, vol. 13, no. 1, p. 3094, 2022. [284] R. Williams and R. Yampolskiy, “Understanding and avoiding ai failures: A practical guide,” Philosophies, vol. 6, no. 3, p. 53, 2021. [285] W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” The Journal of Machine Learning Research, vol. 23, no. 1, pp. 5232–5270, 2022. [286] S. Shen, L. Hou, Y. Zhou, N. Du, S. Longpre, J. Wei, H. W. Chung, B. Zoph, W. Fedus, X. Chen et al., “Mixture-of-experts meets instruction tuning: A winning combination for large language models,” arXiv preprint arXiv:2305.14705, 2023. [287] S. Rajbhandari, C. Li, Z. Yao, M. Zhang, R. Y. Aminabadi, A. A. Awan, J. Rasley, and Y. He, “Deepspeed-moe: Advancing mixture-ofexperts inference and training to power next-generation ai scale,” in International Conference on Machine Learning. PMLR, 2022, pp. 18 332–18 346. [288] L. Shen, Z. Wu, W. Gong, H. Hao, Y. Bai, H. Wu, X. Wu, J. Bian, H. Xiong, D. Yu et al., “Se-moe: A scalable and efficient mixtureof-experts distributed training and inference system,” arXiv preprint arXiv:2205.10034, 2022. [289] 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. [290] Y. Wang, S. Mukherjee, X. Liu, J. Gao, A. H. Awadallah, and J. Gao, “Adamix: Mixture-of-adapter for parameter-efficient tuning of large language models,” arXiv preprint arXiv:2205.12410, vol. 1, no. 2, p. 4, 2022. [291] T. Chen, Z. Zhang, A. Jaiswal, S. Liu, and Z. Wang, “Sparse moe as the new dropout: Scaling dense and self-slimmable transformers,” arXiv preprint arXiv:2303.01610, 2023. [292] H. Zhu, B. He, and X. Zhang, “Multi-gate mixture-of-experts stacked autoencoders for quality prediction in blast furnace ironmaking,” ACS omega, vol. 7, no. 45, pp. 41 296–41 303, 2022. [293] Z. Chi, L. Dong, S. Huang, D. Dai, S. Ma, B. Patra, S. Singhal, P. Bajaj, X. Song, X.-L. Mao et al., “On the representation collapse of sparse mixture of experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 34 600–34 613, 2022. [294] S. Gupta, S. Mukherjee, K. Subudhi, E. Gonzalez, D. Jose, A. H. Awadallah, and J. Gao, “Sparsely activated mixture-of-experts are robust multi-task learners,” arXiv preprint arXiv:2204.07689, 2022. [295] 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. [296] N. Dryden and T. Hoefler, “Spatial mixture-of-experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 11 697–11 713, 2022. [297] Z. You, S. Feng, D. Su, and D. Yu, “Speechmoe2: Mixture-ofexperts model with improved routing,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 7217–7221. [298] J. Puigcerver, R. Jenatton, C. Riquelme, P. Awasthi, and S. Bhojanapalli, “On the adversarial robustness of mixture of experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 9660–9671, 2022. [299] 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. [300] L. Wu, M. Liu, Y. Chen, D. Chen, X. Dai, and L. Yuan, “Residual mixture of experts,” arXiv preprint arXiv:2204.09636, 2022. [301] B. Zoph, I. Bello, S. Kumar, N. Du, Y. Huang, J. Dean, N. Shazeer, and W. Fedus, “Designing effective sparse expert models,” arXiv preprint arXiv:2202.08906, vol. 2, 2022. [302] ——, “St-moe: Designing stable and transferable sparse expert models,” arXiv preprint arXiv:2202.08906, 2022. [303] Y. Chow, A. Tulepbergenov, O. Nachum, M. Ryu, M. Ghavamzadeh, and C. Boutilier, “A mixture-of-expert approach to rl-based dialogue management,” arXiv preprint arXiv:2206.00059, 2022. [304] Z. Fan, R. Sarkar, Z. Jiang, T. Chen, K. Zou, Y. Cheng, C. Hao, Z. Wang et al., “M3vit: Mixture-of-experts vision transformer for efficient multitask learning with model-accelerator co-design,” Advances in Neural Information Processing Systems, vol. 35, pp. 28 441–28 457, 2022. [305] T. Zadouri, A. Ust ¨un, A. Ahmadian, B. Ermis¸, A. Locatelli, and ¨ S. Hooker, “Pushing mixture of experts to the limit: Extremely parameter efficient moe for instruction tuning,” arXiv preprint arXiv:2309.05444, 2023. [306] J. Zhu, X. Zhu, W. Wang, X. Wang, H. Li, X. Wang, and J. Dai, “Uniperceiver-moe: Learning sparse generalist models with conditional moes,” Advances in Neural Information Processing Systems, vol. 35, pp. 2664–2678, 2022. [307] F. Dou, J. Ye, G. Yuan, Q. Lu, W. Niu, H. Sun, L. Guan, G. Lu, G. Mai, N. Liu et al., “Towards artificial general intelligence (agi) in the internet of things (iot): Opportunities and challenges,” arXiv preprint arXiv:2309.07438, 2023. [308] Z. Jia, X. Li, Z. Ling, S. Liu, Y. Wu, and H. Su, “Improving policy optimization with generalist-specialist learning,” in International Conference on Machine Learning. PMLR, 2022, pp. 10 104–10 119. [309] M. Simeone, “Unknown future, repeated present: A narrative-centered analysis of long-term ai discourse,” Humanist Studies & the Digital Age, vol. 7, no. 1, 2022. [310] A. Nair and F. Banaei-Kashani, “Bridging the gap between artificial intelligence and artificial general intelligence: A ten commandment framework for human-like intelligence,” arXiv preprint arXiv:2210.09366, 2022. [311] 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. [312] D. J. Edwards, C. McEnteggart, and Y. Barnes-Holmes, “A functional contextual account of background knowledge in categorization: Implications for artificial general intelligence and cognitive accounts of general knowledge,” Frontiers in Psychology, vol. 13, p. 745306, 2022. [313] J. McCarthy, “Artificial intelligence, logic, and formalising common sense,” Machine Learning and the City: Applications in Architecture and Urban Design, pp. 69–90, 2022. [314] 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. [315] S. Makridakis, “The forthcoming artificial intelligence (ai) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, 2017. [316] S. Pal, K. Kumari, S. Kadam, and A. Saha, “The ai revolution,” IARA Publication, 2023. [317] S. Verma, R. Sharma, S. Deb, and D. Maitra, “Artificial intelligence in marketing: Systematic review and future research direction,” International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100002, 2021. [318] 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. [319] J. B. Telkamp and M. H. Anderson, “The implications of diverse human moral foundations for assessing the ethicality of artificial intelligence,” Journal of Business Ethics, vol. 178, no. 4, pp. 961–976, 2022. [320] X. Zhou, C. Liu, L. Zhai, Z. Jia, C. Guan, and Y. Liu, “Interpretable and robust ai in eeg systems: A survey,” arXiv preprint arXiv:2304.10755, 2023. [321] C. Zhang, C. Zhang, C. Li, Y. Qiao, S. Zheng, S. K. Dam, M. Zhang, J. U. Kim, S. T. Kim, J. Choi et al., “One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era,” arXiv preprint arXiv:2304.06488, 2023. [322] K. Singhal, T. Tu, J. Gottweis, R. Sayres, E. Wulczyn, L. Hou, K. Clark, S. Pfohl, H. Cole-Lewis, D. Neal et al., “Towards expertlevel medical question answering with large language models,” arXiv preprint arXiv:2305.09617, 2023. [323] S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann, “Bloomberggpt: A large language model for finance,” arXiv preprint arXiv:2303.17564, 2023. [324] P. Henderson, K. Sinha, N. Angelard-Gontier, N. R. Ke, G. Fried, R. Lowe, and J. Pineau, “Ethical challenges in data-driven dialogue systems,” in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, pp. 123–129. [325] 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. [326] 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. [327] A. P. Siddaway, A. M. Wood, and L. V. Hedges, “How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses,” Annual review of psychology, vol. 70, pp. 747–770, 2019. [328] E. Landhuis, “Scientific literature: Information overload,” Nature, vol. 535, no. 7612, pp. 457–458, 2016. [329] G. D. Chloros, V. P. Giannoudis, and P. V. Giannoudis, “Peer-reviewing in surgical journals: revolutionize or perish?” Annals of surgery, vol. 275, no. 1, pp. e82–e90, 2022. [330] K.-A. Allen, J. Reardon, Y. Lu, D. V. Smith, E. Rainsford, and L. Walsh, “Towards improving peer review: Crowd-sourced insights from twitter,” Journal of university teaching & learning practice, vol. 19, no. 3, p. 02, 2022. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References XI. CONCLUSIONS This roadmap survey has embarked on an exploration of the transformative trends in generative AI research, particularly focusing on speculated advancements like Q* and the progressive strides towards AGI. Our analysis highlights a crucial paradigm shift, driven by innovations such as MoE, multimodal learning, and the pursuit of AGI. These advancements signal a future where AI systems could significantly extend their capabilities in reasoning, contextual understanding, and creative problem-solving. This study reflects on AI’s dual potential to either contribute to or impede global equity and justice. The equitable distribution of AI benefits and its role in decision-making processes raise crucial questions about fairness and inclusivity. It is imperative to thoughtfully integrate AI into societal structures to enhance justice and reduce disparities. Despite these advancements, several open questions and research gaps remain. These include ensuring the ethical alignment of advanced AI systems with human values and societal norms, a challenge compounded by their increasing autonomy. The safety and robustness of AGI systems in diverse environments also remain a significant research gap. Addressing these challenges requires a multidisciplinary approach, incorporating ethical, social, and philosophical perspectives. Our survey has highlighted key areas for future interdisciplinary research in AI, emphasizing the integration of ethical, sociological, and technical perspectives. This approach will foster collaborative research, bridging the gap between technological advancement and societal needs, ensuring that AI development is aligned with human values and global welfare. The roles of MoE, multimodal, and AGI in reshaping generative AI have been identified as significant, as their advancements can enhance model performance and versatility, and pave the way for future research in areas like ethical AI alignment and AGI. As we forge ahead, the balance between AI advancements and human creativity is not just a goal but a necessity, ensuring AI’s role as a complementary force that amplifies our capacity to innovate and solve complex challenges. Our responsibility is to guide these advancements towards enriching the human experience, aligning technological progress with ethical standards and societal well-being. DISCLAIMER The authors hereby declare no conflict of interest. ABBREVIATIONS AGI Artificial General Intelligence AI Artificial Intelligence AIGC AI-generated content BERT Bidirectional Encoder Representations from Transformers CCPA California Consumer Privacy Act DQN Deep Q-Networks EU European Union GAN Generative Adversarial Network GDPR General Data Protection Regulation GPT Generative Pre-trained Transformers GPU Graphics Processing Unit LIDAR Light Detection and Ranging LLM Large Language Model LSTM Long Short-Term Memory MCTS Monte Carlo Tree Search ML Machine Learning MoE Mixture of Experts NLG Natural Language Generation NLP Natural Language Processing NLU Natural Language Understanding NN Neural Network PPO Proximal Policy Optimization RNNs Recurrent Neural Networks VNN Value Neural Network VRAM Video Random Access Memory REFERENCES [1] A. Turing, “Computing machinery and intelligence,” Mind, vol. 59, no. 236, p. 433, 1950. [2] D. McDermott, “Artificial intelligence meets natural stupidity,” Acm Sigart Bulletin, no. 57, pp. 4–9, 1976. [3] M. Minsky, “Steps toward artificial intelligence,” Proceedings of the IRE, vol. 49, no. 1, pp. 8–30, 1961. [4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015. [5] M. Minsky and S. Papert, “An introduction to computational geometry,” Cambridge tiass., HIT, vol. 479, no. 480, p. 104, 1969. [6] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986. [7] G.-G. Lee, L. Shi, E. Latif, Y. Gao, A. Bewersdorf, M. Nyaaba, S. Guo, Z. Wu, Z. Liu, H. Wang et al., “Multimodality of ai for education: Towards artificial general intelligence,” arXiv preprint arXiv:2312.06037, 2023. [8] P. Maddigan and T. Susnjak, “Chat2vis: Generating data visualisations via natural language using chatgpt, codex and gpt-3 large language models,” IEEE Access, 2023. [9] T. R. McIntosh, T. Liu, T. Susnjak, P. Watters, A. Ng, and M. N. Halgamuge, “A culturally sensitive test to evaluate nuanced gpt hallucination,” IEEE Transactions on Artificial Intelligence, vol. 1, no. 01, pp. 1–13, 2023. [10] M. R. Morris, J. Sohl-dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg, “Levels of agi: Operationalizing progress on the path to agi,” arXiv preprint arXiv:2311.02462, 2023. [11] J. Schuett, N. Dreksler, M. Anderljung, D. McCaffary, L. Heim, E. Bluemke, and B. Garfinkel, “Towards best practices in agi safety and governance: A survey of expert opinion,” arXiv preprint arXiv:2305.07153, 2023. [12] X. Shuai, J. Rollins, I. Moulinier, T. Custis, M. Edmunds, and F. Schilder, “A multidimensional investigation of the effects of publication retraction on scholarly impact,” Journal of the Association for Information Science and Technology, vol. 68, no. 9, pp. 2225–2236, 2017. [13] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017. [14] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al., “Improving language understanding by generative pre-training,” 2018. [15] C. Huang, Z. Zhang, B. Mao, and X. Yao, “An overview of artificial intelligence ethics,” IEEE Transactions on Artificial Intelligence, 2022. [16] L. Besanc¸on, N. Peiffer-Smadja, C. Segalas, H. Jiang, P. Masuzzo, C. Smout, E. Billy, M. Deforet, and C. Leyrat, “Open science saves lives: lessons from the covid-19 pandemic,” BMC Medical Research Methodology, vol. 21, no. 1, pp. 1–18, 2021. [17] C. R. Triggle, R. MacDonald, D. J. Triggle, and D. Grierson, “Requiem for impact factors and high publication charges,” Accountability in Research, vol. 29, no. 3, pp. 133–164, 2022. [18] T. McIntosh, A. Kayes, Y.-P. P. Chen, A. Ng, and P. Watters, “Ransomware mitigation in the modern era: A comprehensive review, research challenges, and future directions,” ACM Computing Surveys (CSUR), vol. 54, no. 9, pp. 1–36, 2021. [19] T. McIntosh, T. Liu, T. Susnjak, H. Alavizadeh, A. Ng, R. Nowrozy, and P. Watters, “Harnessing gpt-4 for generation of cybersecurity grc policies: A focus on ransomware attack mitigation,” Computers & Security, vol. 134, p. 103424, 2023. [20] H. Bao, W. Wang, L. Dong, Q. Liu, O. K. Mohammed, K. Aggarwal, S. Som, S. Piao, and F. Wei, “Vlmo: Unified vision-language pretraining with mixture-of-modality-experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 32 897–32 912, 2022. [21] N. Du, Y. Huang, A. M. Dai, S. Tong, D. Lepikhin, Y. Xu, M. Krikun, Y. Zhou, A. W. Yu, O. Firat et al., “Glam: Efficient scaling of language models with mixture-of-experts,” in International Conference on Machine Learning. PMLR, 2022, pp. 5547–5569. [22] S. Masoudnia and R. Ebrahimpour, “Mixture of experts: a literature survey,” Artificial Intelligence Review, vol. 42, pp. 275–293, 2014. [23] C. Riquelme, J. Puigcerver, B. Mustafa, M. Neumann, R. Jenatton, A. Susano Pinto, D. Keysers, and N. Houlsby, “Scaling vision with sparse mixture of experts,” Advances in Neural Information Processing Systems, vol. 34, pp. 8583–8595, 2021. [24] S. E. Yuksel, J. N. Wilson, and P. D. Gader, “Twenty years of mixture of experts,” IEEE transactions on neural networks and learning systems, vol. 23, no. 8, pp. 1177–1193, 2012. [25] L. Zhang, S. Huang, W. Liu, and D. Tao, “Learning a mixture of granularity-specific experts for fine-grained categorization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 8331–8340. [26] D. Martin, S. Malpica, D. Gutierrez, B. Masia, and A. Serrano, “Multimodality in vr: A survey,” ACM Computing Surveys (CSUR), vol. 54, no. 10s, pp. 1–36, 2022. [27] Q. Sun, Q. Yu, Y. Cui, F. Zhang, X. Zhang, Y. Wang, H. Gao, J. Liu, T. Huang, and X. Wang, “Generative pretraining in multimodality,” arXiv preprint arXiv:2307.05222, 2023. [28] L. Wei, L. Xie, W. Zhou, H. Li, and Q. Tian, “Mvp: Multimodalityguided visual pre-training,” in European Conference on Computer Vision. Springer, 2022, pp. 337–353. [29] J. Wu, W. Zhou, X. Qian, J. Lei, L. Yu, and T. Luo, “Menet: Lightweight multimodality enhancement network for detecting salient objects in rgb-thermal images,” Neurocomputing, vol. 527, pp. 119– 129, 2023. [30] Q. Ye, H. Xu, G. Xu, J. Ye, M. Yan, Y. Zhou, J. Wang, A. Hu, P. Shi, Y. Shi et al., “mplug-owl: Modularization empowers large language models with multimodality,” arXiv preprint arXiv:2304.14178, 2023. [31] K. LaGrandeur, “How safe is our reliance on ai, and should we regulate it?” AI and Ethics, vol. 1, pp. 93–99, 2021. [32] S. McLean, G. J. Read, J. Thompson, C. Baber, N. A. Stanton, and P. M. Salmon, “The risks associated with artificial general intelligence: A systematic review,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 35, no. 5, pp. 649–663, 2023. [33] Y. K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan, R. Dwivedi, J. Edwards, A. Eirug, V. Galanos, P. V. Ilavarasan, M. Janssen, P. Jones, A. K. Kar, H. Kizgin, B. Kronemann, B. Lal, B. Lucini, R. Medaglia, K. Le Meunier-FitzHugh, L. C. Le Meunier-FitzHugh, S. Misra, E. Mogaji, S. K. Sharma, J. B. Singh, V. Raghavan, R. Raman, N. P. Rana, S. Samothrakis, J. Spencer, K. Tamilmani, A. Tubadji, P. Walton, and M. D. Williams, “Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management, vol. 57, p. 101994, 2021. [34] I. Gabriel, “Artificial intelligence, values, and alignment,” Minds and Machines, vol. 30, pp. 411–437, 2020. [35] A. Shaban-Nejad, M. Michalowski, S. Bianco, J. S. Brownstein, D. L. Buckeridge, and R. L. Davis, “Applied artificial intelligence in healthcare: Listening to the winds of change in a post-covid-19 world,” pp. 1969–1971, 2022. [36] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023. [37] B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Computing Surveys, vol. 56, no. 2, pp. 1–40, 2023. [38] J. Li, X. Cheng, W. X. Zhao, J.-Y. Nie, and J.-R. Wen, “Halueval: A large-scale hallucination evaluation benchmark for large language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, pp. 6449–6464. [39] L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese, B. Balle, A. Kasirzadeh et al., “Ethical and social risks of harm from language models,” arXiv preprint arXiv:2112.04359, 2021. [40] X. Zhiheng, Z. Rui, and G. Tao, “Safety and ethical concerns of large language models,” in Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 4: Tutorial Abstracts), 2023, pp. 9–16. [41] P. F. Brown, V. J. Della Pietra, P. V. Desouza, J. C. Lai, and R. L. Mercer, “Class-based n-gram models of natural language,” Computational linguistics, vol. 18, no. 4, pp. 467–480, 1992. [42] S. Katz, “Estimation of probabilities from sparse data for the language model component of a speech recognizer,” IEEE transactions on acoustics, speech, and signal processing, vol. 35, no. 3, pp. 400–401, 1987. [43] R. Kneser and H. Ney, “Improved backing-off for m-gram language modeling,” in 1995 international conference on acoustics, speech, and signal processing, vol. 1. IEEE, 1995, pp. 181–184. [44] R. Kuhn and R. De Mori, “A cache-based natural language model for speech recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 12, no. 6, pp. 570–583, 1990. [45] H. Ney, U. Essen, and R. Kneser, “On structuring probabilistic dependences in stochastic language modelling,” Computer Speech & Language, vol. 8, no. 1, pp. 1–38, 1994. [46] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997. [47] M. K. Nammous and K. Saeed, “Natural language processing: speaker, language, and gender identification with lstm,” Advanced Computing and Systems for Security: Volume Eight, pp. 143–156, 2019. [48] D. Wei, B. Wang, G. Lin, D. Liu, Z. Dong, H. Liu, and Y. Liu, “Research on unstructured text data mining and fault classification based on rnn-lstm with malfunction inspection report,” Energies, vol. 10, no. 3, p. 406, 2017. [49] L. Yao and Y. Guan, “An improved lstm structure for natural language processing,” in 2018 IEEE International Conference of Safety Produce Informatization (IICSPI). IEEE, 2018, pp. 565–569. [50] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al., “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems, vol. 35, pp. 27 730–27 744, 2022. [51] T. Susnjak, “Beyond predictive learning analytics modelling and onto explainable artificial intelligence with prescriptive analytics and chatgpt,” International Journal of Artificial Intelligence in Education, pp. 1–31, 2023. [52] T. Susnjak, E. Griffin, M. McCutcheon, and K. Potter, “Towards clinical prediction with transparency: An explainable ai approach to survival modelling in residential aged care,” arXiv preprint arXiv:2312.00271, 2023. [53] R. Yang, T. F. Tan, W. Lu, A. J. Thirunavukarasu, D. S. W. Ting, and N. Liu, “Large language models in health care: Development, applications, and challenges,” Health Care Science, vol. 2, no. 4, pp. 255–263, 2023. [54] D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning,” Journal of AI, vol. 7, no. 1, pp. 52–62, 2023. [55] T. Susnjak, “Chatgpt: The end of online exam integrity?” arXiv preprint arXiv:2212.09292, 2022. [56] A. Tlili, B. Shehata, M. A. Adarkwah, A. Bozkurt, D. T. Hickey, R. Huang, and B. Agyemang, “What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education,” Smart Learning Environments, vol. 10, no. 1, p. 15, 2023. [57] M. A. AlAfnan, S. Dishari, M. Jovic, and K. Lomidze, “Chatgpt as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses,” Journal of Artificial Intelligence and Technology, vol. 3, no. 2, pp. 60–68, 2023. [58] A. S. George and A. H. George, “A review of chatgpt ai’s impact on several business sectors,” Partners Universal International Innovation Journal, vol. 1, no. 1, pp. 9–23, 2023. [59] G. K. Hadfield and J. Clark, “Regulatory markets: The future of ai governance,” arXiv preprint arXiv:2304.04914, 2023. [60] M. Bakker, M. Chadwick, H. Sheahan, M. Tessler, L. CampbellGillingham, J. Balaguer, N. McAleese, A. Glaese, J. Aslanides, M. Botvinick et al., “Fine-tuning language models to find agreement among humans with diverse preferences,” Advances in Neural Information Processing Systems, vol. 35, pp. 38 176–38 189, 2022. [61] Z. Hu, Y. Lan, L. Wang, W. Xu, E.-P. Lim, R. K.-W. Lee, L. Bing, and S. Poria, “Llm-adapters: An adapter family for parameter-efficient finetuning of large language models,” arXiv preprint arXiv:2304.01933, 2023. [62] H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal, and C. A. Raffel, “Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,” Advances in Neural Information Processing Systems, vol. 35, pp. 1950–1965, 2022. [63] H. Zheng, L. Shen, A. Tang, Y. Luo, H. Hu, B. Du, and D. Tao, “Learn from model beyond fine-tuning: A survey,” arXiv preprint arXiv:2310.08184, 2023. [64] P. Manakul, A. Liusie, and M. J. Gales, “Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models,” arXiv preprint arXiv:2303.08896, 2023. [65] A. Martino, M. Iannelli, and C. Truong, “Knowledge injection to counter large language model (llm) hallucination,” in European Semantic Web Conference. Springer, 2023, pp. 182–185. [66] J.-Y. Yao, K.-P. Ning, Z.-H. Liu, M.-N. Ning, and L. Yuan, “Llm lies: Hallucinations are not bugs, but features as adversarial examples,” arXiv preprint arXiv:2310.01469, 2023. [67] Y. Zhang, Y. Li, L. Cui, D. Cai, L. Liu, T. Fu, X. Huang, E. Zhao, Y. Zhang, Y. Chen et al., “Siren’s song in the ai ocean: A survey on hallucination in large language models,” arXiv preprint arXiv:2309.01219, 2023. [68] J. Ji, M. Liu, J. Dai, X. Pan, C. Zhang, C. Bian, R. Sun, Y. Wang, and Y. Yang, “Beavertails: Towards improved safety alignment of llm via a human-preference dataset,” arXiv preprint arXiv:2307.04657, 2023. [69] Y. Liu, Y. Yao, J.-F. Ton, X. Zhang, R. G. H. Cheng, Y. Klochkov, M. F. Taufiq, and H. Li, “Trustworthy llms: a survey and guideline for evaluating large language models’ alignment,” arXiv preprint arXiv:2308.05374, 2023. [70] Y. Wang, W. Zhong, L. Li, F. Mi, X. Zeng, W. Huang, L. Shang, X. Jiang, and Q. Liu, “Aligning large language models with human: A survey,” arXiv preprint arXiv:2307.12966, 2023. [71] Z. Sun, Y. Shen, Q. Zhou, H. Zhang, Z. Chen, D. Cox, Y. Yang, and C. Gan, “Principle-driven self-alignment of language models from scratch with minimal human supervision,” arXiv preprint arXiv:2305.03047, 2023. [72] Y. Wolf, N. Wies, Y. Levine, and A. Shashua, “Fundamental limitations of alignment in large language models,” arXiv preprint arXiv:2304.11082, 2023. [73] H. Dang, L. Mecke, F. Lehmann, S. Goller, and D. Buschek, “How to prompt? opportunities and challenges of zero-and few-shot learning for human-ai interaction in creative applications of generative models,” arXiv preprint arXiv:2209.01390, 2022. [74] R. Ma, X. Zhou, T. Gui, Y. Tan, L. Li, Q. Zhang, and X. Huang, “Template-free prompt tuning for few-shot ner,” arXiv preprint arXiv:2109.13532, 2021. [75] C. Qin and S. Joty, “Lfpt5: A unified framework for lifelong fewshot language learning based on prompt tuning of t5,” arXiv preprint arXiv:2110.07298, 2021. [76] S. Wang, L. Tang, A. Majety, J. F. Rousseau, G. Shih, Y. Ding, and Y. Peng, “Trustworthy assertion classification through prompting,” Journal of biomedical informatics, vol. 132, p. 104139, 2022. [77] 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. [78] D. Liga and L. Robaldo, “Fine-tuning gpt-3 for legal rule classification,” Computer Law & Security Review, vol. 51, p. 105864, 2023. [79] Y. Liu, A. Singh, C. D. Freeman, J. D. Co-Reyes, and P. J. Liu, “Improving large language model fine-tuning for solving math problems,” arXiv preprint arXiv:2310.10047, 2023. [80] Z. Talat, A. N´ev´eol, S. Biderman, M. Clinciu, M. Dey, S. Longpre, S. Luccioni, M. Masoud, M. Mitchell, D. Radev et al., “You reap what you sow: On the challenges of bias evaluation under multilingual settings,” in Proceedings of BigScience Episode# 5–Workshop on Challenges & Perspectives in Creating Large Language Models, 2022, pp. 26–41. [81] Y. Liu, S. Yu, and T. Lin, “Hessian regularization of deep neural networks: A novel approach based on stochastic estimators of hessian trace,” Neurocomputing, vol. 536, pp. 13–20, 2023. [82] Y. Lu, Y. Bo, and W. He, “Confidence adaptive regularization for deep learning with noisy labels,” arXiv preprint arXiv:2108.08212, 2021. [83] G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton, “Regularizing neural networks by penalizing confident output distributions,” arXiv preprint arXiv:1701.06548, 2017. [84] E. Chen, Z.-W. Hong, J. Pajarinen, and P. Agrawal, “Redeeming intrinsic rewards via constrained optimization,” Advances in Neural Information Processing Systems, vol. 35, pp. 4996–5008, 2022. [85] Y. Jiang, Z. Li, M. Tan, S. Wei, G. Zhang, Z. Guan, and B. Han, “A stable block adjustment method without ground control points using bound constrained optimization,” International Journal of Remote Sensing, vol. 43, no. 12, pp. 4708–4722, 2022. [86] M. Kachuee and S. Lee, “Constrained policy optimization for controlled self-learning in conversational ai systems,” arXiv preprint arXiv:2209.08429, 2022. [87] Z. Song, H. Wang, and Y. Jin, “A surrogate-assisted evolutionary framework with regions of interests-based data selection for expensive constrained optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023. [88] J. Yu, T. Xu, Y. Rong, J. Huang, and R. He, “Structure-aware conditional variational auto-encoder for constrained molecule optimization,” Pattern Recognition, vol. 126, p. 108581, 2022. [89] P. Butlin, “Ai alignment and human reward,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 437–445. [90] 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. [91] J. Leike, D. Krueger, T. Everitt, M. Martic, V. Maini, and S. Legg, “Scalable agent alignment via reward modeling: a research direction,” arXiv preprint arXiv:1811.07871, 2018. [92] L. Li, Y. Chai, S. Wang, Y. Sun, H. Tian, N. Zhang, and H. Wu, “Toolaugmented reward modeling,” arXiv preprint arXiv:2310.01045, 2023. [93] F. Barreto, L. Moharkar, M. Shirodkar, V. Sarode, S. Gonsalves, and A. Johns, “Generative artificial intelligence: Opportunities and challenges of large language models,” in International Conference on Intelligent Computing and Networking. Springer, 2023, pp. 545–553. [94] Z. Chen, Z. Wang, Z. Wang, H. Liu, Z. Yin, S. Liu, L. Sheng, W. Ouyang, Y. Qiao, and J. Shao, “Octavius: Mitigating task interference in mllms via moe,” arXiv preprint arXiv:2311.02684, 2023. [95] C. Dun, M. D. C. H. Garcia, G. Zheng, A. H. Awadallah, A. Kyrillidis, and R. Sim, “Sweeping heterogeneity with smart mops: Mixture of prompts for llm task adaptation,” arXiv preprint arXiv:2310.02842, 2023. [96] H. Naveed, A. U. Khan, S. Qiu, M. Saqib, S. Anwar, M. Usman, N. Barnes, and A. Mian, “A comprehensive overview of large language models,” arXiv preprint arXiv:2307.06435, 2023. [97] F. Xue, Y. Fu, W. Zhou, Z. Zheng, and Y. You, “To repeat or not to repeat: Insights from scaling llm under token-crisis,” arXiv preprint arXiv:2305.13230, 2023. [98] M. Nowaz Rabbani Chowdhury, S. Zhang, M. Wang, S. Liu, and P.-Y. Chen, “Patch-level routing in mixture-of-experts is provably sampleefficient for convolutional neural networks,” arXiv e-prints, pp. arXiv– 2306, 2023. [99] 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. [100] C. N. d. Santos, J. Lee-Thorp, I. Noble, C.-C. Chang, and D. Uthus, “Memory augmented language models through mixture of word experts,” arXiv preprint arXiv:2311.10768, 2023. [101] W. Wang, G. Ma, Y. Li, and B. Du, “Language-routing mixture of experts for multilingual and code-switching speech recognition,” arXiv preprint arXiv:2307.05956, 2023. [102] X. Zhao, X. Chen, Y. Cheng, and T. Chen, “Sparse moe with language guided routing for multilingual machine translation,” in Conference on Parsimony and Learning (Recent Spotlight Track), 2023. [103] W. Huang, H. Zhang, P. Peng, and H. Wang, “Multi-gate mixtureof-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. [104] B. Liu, L. Ding, L. Shen, K. Peng, Y. Cao, D. Cheng, and D. Tao, “Diversifying the mixture-of-experts representation for language models with orthogonal optimizer,” arXiv preprint arXiv:2310.09762, 2023. [105] W. Wang, Z. Lai, S. Li, W. Liu, K. Ge, Y. Liu, A. Shen, and D. Li, “Prophet: Fine-grained load balancing for parallel training of largescale moe models,” in 2023 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2023, pp. 82–94. [106] X. Yao, S. Liang, S. Han, and H. Huang, “Enhancing molecular property prediction via mixture of collaborative experts,” arXiv preprint arXiv:2312.03292, 2023. [107] Z. Xiao, Y. Jiang, G. Tang, L. Liu, S. Xu, Y. Xiao, and W. Yan, “Adversarial mixture of experts with category hierarchy soft constraint,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021, pp. 2453–2463. [108] 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. [109] Z. Chen, Y. Deng, Y. Wu, Q. Gu, and Y. Li, “Towards understanding the mixture-of-experts layer in deep learning,” Advances in neural information processing systems, vol. 35, pp. 23 049–23 062, 2022. [110] Y. Zhou, T. Lei, H. Liu, N. Du, Y. Huang, V. Zhao, A. M. Dai, Q. V. Le, J. Laudon et al., “Mixture-of-experts with expert choice routing,” Advances in Neural Information Processing Systems, vol. 35, pp. 7103– 7114, 2022. [111] N. Guha, C. Lawrence, L. A. Gailmard, K. Rodolfa, F. Surani, R. Bommasani, I. Raji, M.-F. Cu´ellar, 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. [112] 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 [113] J. N. Acosta, G. J. Falcone, P. Rajpurkar, and E. J. Topol, “Multimodal biomedical ai,” Nature Medicine, vol. 28, no. 9, pp. 1773–1784, 2022. [114] 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. [115] B. Xu, D. Kocyigit, R. Grimm, B. P. Griffin, and F. Cheng, “Applications of artificial intelligence in multimodality cardiovascular imaging: a state-of-the-art review,” Progress in cardiovascular diseases, vol. 63, no. 3, pp. 367–376, 2020. [116] A. Birhane, V. U. Prabhu, and E. Kahembwe, “Multimodal datasets: misogyny, pornography, and malignant stereotypes,” arXiv preprint arXiv:2110.01963, 2021. [117] Y. Li, W. Li, N. Li, X. Qiu, and K. B. Manokaran, “Multimodal information interaction and fusion for the parallel computing system using ai techniques,” International Journal of High Performance Systems Architecture, vol. 10, no. 3-4, pp. 185–196, 2021. [118] C. Zhang, Z. Yang, X. He, and L. Deng, “Multimodal intelligence: Representation learning, information fusion, and applications,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 3, pp. 478–493, 2020. [119] H. Qiao, V. Liu, and L. Chilton, “Initial images: using image prompts to improve subject representation in multimodal ai generated art,” in Proceedings of the 14th Conference on Creativity and Cognition, 2022, pp. 15–28. [120] A. E. Stewart, Z. Keirn, and S. K. D’Mello, “Multimodal modeling of collaborative problem-solving facets in triads,” User Modeling and User-Adapted Interaction, pp. 1–39, 2021. [121] L. Xue, N. Yu, S. Zhang, J. Li, R. Mart´ın-Mart´ın, J. Wu, C. Xiong, R. Xu, J. C. Niebles, and S. Savarese, “Ulip-2: Towards scalable multimodal pre-training for 3d understanding,” arXiv preprint arXiv:2305.08275, 2023. [122] L. Yan, L. Zhao, D. Gasevic, and R. Martinez-Maldonado, “Scalability, sustainability, and ethicality of multimodal learning analytics,” in LAK22: 12th international learning analytics and knowledge conference, 2022, pp. 13–23. [123] Y. Liu-Thompkins, S. Okazaki, and H. Li, “Artificial empathy in marketing interactions: Bridging the human-ai gap in affective and social customer experience,” Journal of the Academy of Marketing Science, vol. 50, no. 6, pp. 1198–1218, 2022. [124] 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. [125] E. Sachdeva, N. Agarwal, S. Chundi, S. Roelofs, J. Li, B. Dariush, C. Choi, and M. Kochenderfer, “Rank2tell: A multimodal driving dataset for joint importance ranking and reasoning,” arXiv preprint arXiv:2309.06597, 2023. [126] C. Cui, Y. Ma, X. Cao, W. Ye, Y. Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao et al., “A survey on multimodal large language models for autonomous driving,” arXiv preprint arXiv:2311.12320, 2023. [127] A. B. Temsamani, A. K. Chavali, W. Vervoort, T. Tuytelaars, G. Radevski, H. Van Hamme, K. Mets, M. Hutsebaut-Buysse, T. De Schepper, and S. Latr´e, “A multimodal ai approach for intuitively instructable autonomous systems: a case study of an autonomous off-highway vehicle,” in The Eighteenth International Conference on Autonomic and Autonomous Systems, ICAS 2022, May 22-26, 2022, Venice, Italy, 2022, pp. 31–39. [128] J. Lee and S. Y. Shin, “Something that they never said: Multimodal disinformation and source vividness in understanding the power of aienabled deepfake news,” Media Psychology, vol. 25, no. 4, pp. 531– 546, 2022. [129] S. Muppalla, S. Jia, and S. Lyu, “Integrating audio-visual features for multimodal deepfake detection,” arXiv preprint arXiv:2310.03827, 2023. [130] S. Kumar, M. K. Chaube, S. N. Nenavath, S. K. Gupta, and S. K. Tetarave, “Privacy preservation and security challenges: a new frontier multimodal machine learning research,” International Journal of Sensor Networks, vol. 39, no. 4, pp. 227–245, 2022. [131] J. Marchang and A. Di Nuovo, “Assistive multimodal robotic system (amrsys): security and privacy issues, challenges, and possible solutions,” Applied Sciences, vol. 12, no. 4, p. 2174, 2022. [132] A. Pe˜na, I. Serna, A. Morales, J. Fierrez, A. Ortega, A. Herrarte, M. Alcantara, and J. Ortega-Garcia, “Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment,” SN Computer Science, vol. 4, no. 5, p. 434, 2023. [133] R. Wolfe and A. Caliskan, “American== white in multimodal languageand-image ai,” in Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, pp. 800–812. [134] R. Wolfe, Y. Yang, B. Howe, and A. Caliskan, “Contrastive languagevision 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. [135] 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. [136] 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. [137] 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. [138] 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. [139] Y. Gao, M. Zhou, D. Liu, Z. Yan, S. Zhang, and D. N. Metaxas, “A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark,” arXiv preprint arXiv:2203.00131, 2022. [140] 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. [141] R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, J. Heek, K. Xiao, S. Agrawal, and J. Dean, “Efficiently scaling transformer inference,” Proceedings of Machine Learning and Systems, vol. 5, 2023. [142] Y. Ding and M. Jia, “Convolutional transformer: An enhanced attention mechanism architecture for remaining useful life estimation of bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022. [143] Y. Ding, M. Jia, Q. Miao, and Y. Cao, “A novel time–frequency transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings,” Mechanical Systems and Signal Processing, vol. 168, p. 108616, 2022. [144] G. Wang, Y. Zhao, C. Tang, C. Luo, and W. Zeng, “When shift operation meets vision transformer: An extremely simple alternative to attention mechanism,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 2, 2022, pp. 2423–2430. [145] 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. [146] 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. [147] 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. [148] 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. [149] S. Tamang, B. Sen, A. Pradhan, K. Sharma, and V. K. Singh, “Enhancing covid-19 safety: Exploring yolov8 object detection for accurate face mask classification,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2, pp. 892–897, 2023. [150] J. Lu, R. Xiong, J. Tian, C. Wang, C.-W. Hsu, N.-T. Tsou, F. Sun, and J. Li, “Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning,” Energy Storage Materials, vol. 50, pp. 139–151, 2022. [151] A. Onan, “Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 5, pp. 2098–2117, 2022. [152] F. Shan, X. He, D. J. Armaghani, P. Zhang, and D. Sheng, “Success and challenges in predicting tbm penetration rate using recurrent neural networks,” Tunnelling and Underground Space Technology, vol. 130, p. 104728, 2022. [153] C. Sridhar, P. K. Pareek, R. Kalidoss, S. S. Jamal, P. K. Shukla, S. J. Nuagah et al., “Optimal medical image size reduction model creation using recurrent neural network and genpsowvq,” Journal of Healthcare Engineering, vol. 2022, 2022. [154] J. Zhu, Q. Jiang, Y. Shen, C. Qian, F. Xu, and Q. Zhu, “Application of recurrent neural network to mechanical fault diagnosis: A review,” Journal of Mechanical Science and Technology, vol. 36, no. 2, pp. 527–542, 2022. [155] S. Lin, W. Lin, W. Wu, F. Zhao, R. Mo, and H. Zhang, “Segrnn: Segment recurrent neural network for long-term time series forecasting,” arXiv preprint arXiv:2308.11200, 2023. [156] Z. Wei, X. Zhang, and M. Sun, “Extracting weighted finite automata from recurrent neural networks for natural languages,” in International Conference on Formal Engineering Methods. Springer, 2022, pp. 370–385. [157] F. Bonassi, M. Farina, J. Xie, and R. Scattolini, “On recurrent neural networks for learning-based control: recent results and ideas for future developments,” Journal of Process Control, vol. 114, pp. 92–104, 2022. [158] 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. [159] 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. [160] P. Xu, X. Zhu, and D. A. Clifton, “Multimodal learning with transformers: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [161] I. Molenaar, S. de Mooij, R. Azevedo, M. Bannert, S. J¨arvel¨a, and D. Gaˇsevi´c, “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. [162] S. Steyaert, M. Pizurica, D. Nagaraj, P. Khandelwal, T. HernandezBoussard, 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. [163] V. Rani, S. T. Nabi, M. Kumar, A. Mittal, and K. Kumar, “Selfsupervised learning: A succinct review,” Archives of Computational Methods in Engineering, vol. 30, no. 4, pp. 2761–2775, 2023. [164] 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. [165] 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. [166] 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. [167] 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. [168] 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. [169] Q. Li, X. Peng, Y. Qiao, and Q. Hao, “Unsupervised person reidentification with multi-label learning guided self-paced clustering,” Pattern Recognition, vol. 125, p. 108521, 2022. [170] P. Nancy, H. Pallathadka, M. Naved, K. Kaliyaperumal, K. Arumugam, and V. Garchar, “Deep learning and machine learning based efficient framework for image based plant disease classification and detection,” in 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA). IEEE, 2022, pp. 1–6. [171] P. An, Z. Wang, and C. Zhang, “Ensemble unsupervised autoencoders and gaussian mixture model for cyberattack detection,” Information Processing & Management, vol. 59, no. 2, p. 102844, 2022. [172] 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. [173] E. Ayanoglu, K. Davaslioglu, and Y. E. Sagduyu, “Machine learning in nextg networks via generative adversarial networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 480–501, 2022. [174] K. Yan, X. Chen, X. Zhou, Z. Yan, and J. Ma, “Physical model informed fault detection and diagnosis of air handling units based on transformer generative adversarial network,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2192–2199, 2022. [175] 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. [176] P. Ladosz, L. Weng, M. Kim, and H. Oh, “Exploration in deep reinforcement learning: A survey,” Information Fusion, vol. 85, pp. 1–22, 2022. [177] Y. Matsuo, Y. LeCun, M. Sahani, D. Precup, D. Silver, M. Sugiyama, E. Uchibe, and J. Morimoto, “Deep learning, reinforcement learning, and world models,” Neural Networks, vol. 152, pp. 267–275, 2022. [178] D. Bertoin, A. Zouitine, M. Zouitine, and E. Rachelson, “Look where you look! saliency-guided q-networks for generalization in visual reinforcement learning,” Advances in Neural Information Processing Systems, vol. 35, pp. 30 693–30 706, 2022. [179] A. Hafiz, “A survey of deep q-networks used for reinforcement learning: State of the art,” Intelligent Communication Technologies and Virtual Mobile Networks: Proceedings of ICICV 2022, pp. 393–402, 2022. [180] A. Hafiz, M. Hassaballah, A. Alqahtani, S. Alsubai, and M. A. Hameed, “Reinforcement learning with an ensemble of binary action deep qnetworks.” Computer Systems Science & Engineering, vol. 46, no. 3, 2023. [181] A. Alagha, S. Singh, R. Mizouni, J. Bentahar, and H. Otrok, “Target localization using multi-agent deep reinforcement learning with proximal policy optimization,” Future Generation Computer Systems, vol. 136, pp. 342–357, 2022. [182] S. S. Hassan, Y. M. Park, Y. K. Tun, W. Saad, Z. Han, and C. S. Hong, “3to: Thz-enabled throughput and trajectory optimization of uavs in 6g networks by proximal policy optimization deep reinforcement learning,” in ICC 2022-IEEE International Conference on Communications. IEEE, 2022, pp. 5712–5718. [183] A. K. Jayant and S. Bhatnagar, “Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm,” Advances in Neural Information Processing Systems, vol. 35, pp. 24 432–24 445, 2022. [184] B. Lin, “Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook,” Expert Systems with Applications, p. 122254, 2023. [185] 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. [186] A. Raza, K. P. Tran, L. Koehl, and S. Li, “Designing ecg monitoring healthcare system with federated transfer learning and explainable ai,” Knowledge-Based Systems, vol. 236, p. 107763, 2022. [187] S. Siahpour, X. Li, and J. Lee, “A novel transfer learning approach in remaining useful life prediction for incomplete dataset,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–11, 2022. [188] Z. Guo, K. Lin, X. Chen, and C.-Y. Chit, “Transfer learning for angle of arrivals estimation in massive mimo system,” in 2022 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2022, pp. 506–511. [189] S. Liu, Y. Lu, P. Zheng, H. Shen, and J. Bao, “Adaptive reconstruction of digital twins for machining systems: A transfer learning approach,” Robotics and Computer-Integrated Manufacturing, vol. 78, p. 102390, 2022. [190] 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. [191] Y. Meng, J. Huang, Y. Zhang, and J. Han, “Generating training data with language models: Towards zero-shot language understanding,” Advances in Neural Information Processing Systems, vol. 35, pp. 462– 477, 2022. [192] R. M. Samant, M. R. Bachute, S. Gite, and K. Kotecha, “Framework for deep learning-based language models using multi-task learning in natural language understanding: A systematic literature review and future directions,” IEEE Access, vol. 10, pp. 17 078–17 097, 2022. [193] H. Weld, X. Huang, S. Long, J. Poon, and S. C. Han, “A survey of joint intent detection and slot filling models in natural language understanding,” ACM Computing Surveys, vol. 55, no. 8, pp. 1–38, 2022. [194] 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. [195] A. Montejo-R´aez and S. M. Jim´enez-Zafra, “Current approaches and applications in natural language processing,” Applied Sciences, vol. 12, no. 10, p. 4859, 2022. [196] K. Vijayan, O. Anand, and A. Sahaj, “Language-agnostic text processing for information extraction,” in CS & IT Conference Proceedings, vol. 12, no. 23. CS & IT Conference Proceedings, 2022. [197] C. D. Manning, “Human language understanding & reasoning,” Daedalus, vol. 151, no. 2, pp. 127–138, 2022. [198] 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. [199] E. Erdem, M. Kuyu, S. Yagcioglu, A. Frank, L. Parcalabescu, B. Plank, A. Babii, O. Turuta, A. Erdem, I. Calixto et al., “Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning,” Journal of Artificial Intelligence Research, vol. 73, pp. 1131–1207, 2022. [200] J. Qian, L. Dong, Y. Shen, F. Wei, and W. Chen, “Controllable natural language generation with contrastive prefixes,” arXiv preprint arXiv:2202.13257, 2022. [201] 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. [202] A. K. Pandey and S. S. Roy, “Natural language generation using sequential models: A survey,” Neural Processing Letters, pp. 1–34, 2023. [203] J. Y. Khan and G. Uddin, “Automatic code documentation generation using gpt-3,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022, pp. 1–6. [204] 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. [205] T. Fu, S. Gao, X. Zhao, J.-r. Wen, and R. Yan, “Learning towards conversational ai: A survey,” AI Open, vol. 3, pp. 14–28, 2022. [206] 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. [207] 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. [208] S. Kusal, S. Patil, J. Choudrie, K. Kotecha, S. Mishra, and A. Abraham, “Ai-based conversational agents: A scoping review from technologies to future directions,” IEEE Access, 2022. [209] Z. Xiao, “Seeing us through machines: designing and building conversational ai to understand humans,” Ph.D. dissertation, University of Illinois at Urbana-Champaign, 2023. [210] 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. [211] 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. [212] 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. [213] 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. [214] B. Attard-Frost, A. De los R´ıos, 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. [215] A. Gardner, A. L. Smith, A. Steventon, E. Coughlan, and M. Oldfield, “Ethical funding for trustworthy ai: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy ai practice,” AI and Ethics, pp. 1–15, 2022. [216] J. Schuett, “Three lines of defense against risks from ai,” AI & SOCIETY, pp. 1–15, 2023. [217] M. Sloane and J. Zakrzewski, “German ai start-ups and “ai ethics”: Using a social practice lens for assessing and implementing sociotechnical innovation,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 935–947. [218] M. Vasconcelos, C. Cardonha, and B. Gonc¸alves, “Modeling epistemological principles for bias mitigation in ai systems: an illustration in hiring decisions,” in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, pp. 323–329. [219] Y. Yang, A. Gupta, J. Feng, P. Singhal, V. Yadav, Y. Wu, P. Natarajan, V. Hedau, and J. Joo, “Enhancing fairness in face detection in computer vision systems by demographic bias mitigation,” in Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, pp. 813– 822. [220] R. Schwartz, A. Vassilev, K. Greene, L. Perine, A. Burt, P. Hall et al., “Towards a standard for identifying and managing bias in artificial intelligence,” NIST special publication, vol. 1270, no. 10.6028, 2022. [221] W. Guo and A. Caliskan, “Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 122–133. [222] Y. Kong, “Are “intersectionally fair” ai algorithms really fair to women of color? a philosophical analysis,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 485–494. [223] Y. C. Tan and L. E. Celis, “Assessing social and intersectional biases in contextualized word representations,” Advances in neural information processing systems, vol. 32, 2019. [224] L. Cheng, A. Mosallanezhad, P. Sheth, and H. Liu, “Causal learning for socially responsible ai,” arXiv preprint arXiv:2104.12278, 2021. [225] J. D. Correa, J. Tian, and E. Bareinboim, “Identification of causal effects in the presence of selection bias,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 2744– 2751. [226] B. Ghai and K. Mueller, “D-bias: a causality-based human-in-theloop system for tackling algorithmic bias,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 473–482, 2022. [227] J. N. Yan, Z. Gu, H. Lin, and J. M. Rzeszotarski, “Silva: Interactively assessing machine learning fairness using causality,” in Proceedings of the 2020 chi conference on human factors in computing systems, 2020, pp. 1–13. [228] E. Bertino, M. Kantarcioglu, C. G. Akcora, S. Samtani, S. Mittal, and M. Gupta, “Ai for security and security for ai,” in Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy, 2021, pp. 333–334. [229] H. Susanto, L. F. Yie, D. Rosiyadi, A. I. Basuki, and D. Setiana, “Data security for connected governments and organisations: Managing automation and artificial intelligence,” in Web 2.0 and cloud technologies for implementing connected government. IGI Global, 2021, pp. 229– 251. [230] S. Dilmaghani, M. R. Brust, G. Danoy, N. Cassagnes, J. Pecero, and P. Bouvry, “Privacy and security of big data in ai systems: A research and standards perspective,” in 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019, pp. 5737–5743. [231] T. McIntosh, “Intercepting ransomware attacks with staged eventdriven access control,” Ph.D. dissertation, La Trobe, 2022. [232] 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. [233] P. Hummel, M. Braun, M. Tretter, and P. Dabrock, “Data sovereignty: A review,” Big Data & Society, vol. 8, no. 1, p. 2053951720982012, 2021. [234] M. Lukings and A. Habibi Lashkari, “Data sovereignty,” in Understanding Cybersecurity Law in Data Sovereignty and Digital Governance: An Overview from a Legal Perspective. Springer, 2022, pp. 1–38. [235] M. Hickok, “Lessons learned from ai ethics principles for future actions,” AI and Ethics, vol. 1, no. 1, pp. 41–47, 2021. [236] J. Zhou and F. Chen, “Ai ethics: From principles to practice,” AI & SOCIETY, pp. 1–11, 2022. [237] J. A. Kroll, “Outlining traceability: A principle for operationalizing accountability in computing systems,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp. 758–771. [238] A. Oseni, N. Moustafa, H. Janicke, P. Liu, Z. Tari, and A. Vasilakos, “Security and privacy for artificial intelligence: Opportunities and challenges,” arXiv preprint arXiv:2102.04661, 2021. [239] B. C. Stahl and D. Wright, “Ethics and privacy in ai and big data: Implementing responsible research and innovation,” IEEE Security & Privacy, vol. 16, no. 3, pp. 26–33, 2018. [240] 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. [241] M. Song, Z. Wang, Z. Zhang, Y. Song, Q. Wang, J. Ren, and H. Qi, “Analyzing user-level privacy attack against federated learning,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2430–2444, 2020. [242] I. Misra and L. v. d. Maaten, “Self-supervised learning of pretextinvariant representations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 6707–6717. [243] X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, “S4l: Self-supervised semi-supervised learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 1476–1485. [244] T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby, “Self-supervised gans via auxiliary rotation loss,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 12 154–12 163. [245] S. Jenni and P. Favaro, “Self-supervised feature learning by learning to spot artifacts,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2733–2742. [246] P. Patel, N. Kumari, M. Singh, and B. Krishnamurthy, “Lt-gan: Selfsupervised gan with latent transformation detection,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2021, pp. 3189–3198. [247] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning. PMLR, 2020, pp. 1597–1607. [248] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9729–9738. [249] A. T. Liu, S.-W. Li, and H.-y. Lee, “Tera: Self-supervised learning of transformer encoder representation for speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 2351– 2366, 2021. [250] Y. Pang, W. Wang, F. E. Tay, W. Liu, Y. Tian, and L. Yuan, “Masked autoencoders for point cloud self-supervised learning,” in European conference on computer vision. Springer, 2022, pp. 604–621. [251] T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, “Metalearning in neural networks: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 9, pp. 5149–5169, 2021. [252] R. Vilalta and Y. Drissi, “A perspective view and survey of metalearning,” Artificial intelligence review, vol. 18, pp. 77–95, 2002. [253] M. Al-Shedivat, L. Li, E. Xing, and A. Talwalkar, “On data efficiency of meta-learning,” in International Conference on Artificial Intelligence and Statistics. PMLR, 2021, pp. 1369–1377. [254] Y. Hu, R. Liu, X. Li, D. Chen, and Q. Hu, “Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data,” IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 3894– 3904, 2021. [255] S. Baik, J. Choi, H. Kim, D. Cho, J. Min, and K. M. Lee, “Metalearning with task-adaptive loss function for few-shot learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9465–9474. [256] Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang, “Meta-baseline: Exploring simple meta-learning for few-shot learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9062–9071. [257] M. A. Jamal and G.-J. Qi, “Task agnostic meta-learning for few-shot learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11 719–11 727. [258] R. Behnia, M. R. Ebrahimi, J. Pacheco, and B. Padmanabhan, “Ewtune: A framework for privately fine-tuning large language models with differential privacy,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2022, pp. 560–566. [259] J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le, “Finetuned language models are zero-shot learners,” arXiv preprint arXiv:2109.01652, 2021. [260] W. Kuang, B. Qian, Z. Li, D. Chen, D. Gao, X. Pan, Y. Xie, Y. Li, B. Ding, and J. Zhou, “Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,” arXiv preprint arXiv:2309.00363, 2023. [261] 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. [262] M. Engelbach, D. Klau, F. Scheerer, J. Drawehn, and M. Kintz, “Fine-tuning and aligning question answering models for complex information extraction tasks,” arXiv preprint arXiv:2309.14805, 2023. [263] T. T. Nguyen, C. Wilson, and J. Dalins, “Fine-tuning llama 2 large language models for detecting online sexual predatory chats and abusive texts,” arXiv preprint arXiv:2308.14683, 2023. [264] Q. Zhou, C. Yu, S. Zhang, S. Wu, Z. Wang, and F. Wang, “Regionblip: A unified multi-modal pre-training framework for holistic and regional comprehension,” arXiv preprint arXiv:2308.02299, 2023. [265] T. Arnold and D. Kasenberg, “Value alignment or misalignment - what will keep systems accountable?” in AAAI Workshop on AI, Ethics, and Society, 2017. [266] I. Gabriel and V. Ghazavi, “The challenge of value alignment: From fairer algorithms to ai safety,” arXiv preprint arXiv:2101.06060, 2021. [267] S. Nyholm, “Responsibility gaps, value alignment, and meaningful human control over artificial intelligence,” in Risk and responsibility in context. Routledge, 2023, pp. 191–213. [268] S. Wu, H. Fei, L. Qu, W. Ji, and T.-S. Chua, “Next-gpt: Any-to-any multimodal llm,” arXiv preprint arXiv:2309.05519, 2023. [269] K. Bayoudh, R. Knani, F. Hamdaoui, and A. Mtibaa, “A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets,” The Visual Computer, pp. 1–32, 2021. [270] P. Hu, L. Zhen, D. Peng, and P. Liu, “Scalable deep multimodal learning for cross-modal retrieval,” in Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, 2019, pp. 635–644. [271] A. Rahate, R. Walambe, S. Ramanna, and K. Kotecha, “Multimodal colearning: Challenges, applications with datasets, recent advances and future directions,” Information Fusion, vol. 81, pp. 203–239, 2022. [272] L. Che, J. Wang, Y. Zhou, and F. Ma, “Multimodal federated learning: A survey,” Sensors, vol. 23, no. 15, p. 6986, 2023. [273] P. P. Liang, Y. Lyu, X. Fan, Z. Wu, Y. Cheng, J. Wu, L. Chen, P. Wu, M. A. Lee, Y. Zhu et al., “Multibench: Multiscale benchmarks for multimodal representation learning,” arXiv preprint arXiv:2107.07502, 2021. [274] Z. Ashktorab, Q. V. Liao, C. Dugan, J. Johnson, Q. Pan, W. Zhang, S. Kumaravel, and M. Campbell, “Human-ai collaboration in a cooperative game setting: Measuring social perception and outcomes,” Proceedings of the ACM on Human-Computer Interaction, vol. 4, no. CSCW2, pp. 1–20, 2020. [275] P. Esmaeilzadeh, T. Mirzaei, and S. Dharanikota, “Patients’ perceptions toward human–artificial intelligence interaction in health care: experimental study,” Journal of medical Internet research, vol. 23, no. 11, p. e25856, 2021. [276] M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, “A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques,” IEEE Access, vol. 9, pp. 153 316–153 348, 2021. [277] A. S. Rajawat, R. Rawat, K. Barhanpurkar, R. N. Shaw, and A. Ghosh, “Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning,” in Artificial Intelligence for Future Generation Robotics. Elsevier, 2021, pp. 1–13. [278] S. Mohseni, N. Zarei, and E. D. Ragan, “A multidisciplinary survey and framework for design and evaluation of explainable ai systems,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 11, no. 3-4, pp. 1–45, 2021. [279] M. C. Buehler and T. H. Weisswange, “Theory of mind based communication for human agent cooperation,” in 2020 IEEE International Conference on Human-Machine Systems (ICHMS). IEEE, 2020, pp. 1–6. [280] M. M. C¸ elikok, T. Peltola, P. Daee, and S. Kaski, “Interactive ai with a theory of mind,” arXiv preprint arXiv:1912.05284, 2019. [281] A. Dafoe, E. Hughes, Y. Bachrach, T. Collins, K. R. McKee, J. Z. Leibo, K. Larson, and T. Graepel, “Open problems in cooperative ai,” arXiv preprint arXiv:2012.08630, 2020. [282] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al., “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712, 2023. [283] N. Fei, Z. Lu, Y. Gao, G. Yang, Y. Huo, J. Wen, H. Lu, R. Song, X. Gao, T. Xiang et al., “Towards artificial general intelligence via a multimodal foundation model,” Nature Communications, vol. 13, no. 1, p. 3094, 2022. [284] R. Williams and R. Yampolskiy, “Understanding and avoiding ai failures: A practical guide,” Philosophies, vol. 6, no. 3, p. 53, 2021. [285] W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” The Journal of Machine Learning Research, vol. 23, no. 1, pp. 5232–5270, 2022. [286] S. Shen, L. Hou, Y. Zhou, N. Du, S. Longpre, J. Wei, H. W. Chung, B. Zoph, W. Fedus, X. Chen et al., “Mixture-of-experts meets instruction tuning: A winning combination for large language models,” arXiv preprint arXiv:2305.14705, 2023. [287] S. Rajbhandari, C. Li, Z. Yao, M. Zhang, R. Y. Aminabadi, A. A. Awan, J. Rasley, and Y. He, “Deepspeed-moe: Advancing mixture-ofexperts inference and training to power next-generation ai scale,” in International Conference on Machine Learning. PMLR, 2022, pp. 18 332–18 346. [288] L. Shen, Z. Wu, W. Gong, H. Hao, Y. Bai, H. Wu, X. Wu, J. Bian, H. Xiong, D. Yu et al., “Se-moe: A scalable and efficient mixtureof-experts distributed training and inference system,” arXiv preprint arXiv:2205.10034, 2022. [289] 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. [290] Y. Wang, S. Mukherjee, X. Liu, J. Gao, A. H. Awadallah, and J. Gao, “Adamix: Mixture-of-adapter for parameter-efficient tuning of large language models,” arXiv preprint arXiv:2205.12410, vol. 1, no. 2, p. 4, 2022. [291] T. Chen, Z. Zhang, A. Jaiswal, S. Liu, and Z. Wang, “Sparse moe as the new dropout: Scaling dense and self-slimmable transformers,” arXiv preprint arXiv:2303.01610, 2023. [292] H. Zhu, B. He, and X. Zhang, “Multi-gate mixture-of-experts stacked autoencoders for quality prediction in blast furnace ironmaking,” ACS omega, vol. 7, no. 45, pp. 41 296–41 303, 2022. [293] Z. Chi, L. Dong, S. Huang, D. Dai, S. Ma, B. Patra, S. Singhal, P. Bajaj, X. Song, X.-L. Mao et al., “On the representation collapse of sparse mixture of experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 34 600–34 613, 2022. [294] S. Gupta, S. Mukherjee, K. Subudhi, E. Gonzalez, D. Jose, A. H. Awadallah, and J. Gao, “Sparsely activated mixture-of-experts are robust multi-task learners,” arXiv preprint arXiv:2204.07689, 2022. [295] 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. [296] N. Dryden and T. Hoefler, “Spatial mixture-of-experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 11 697–11 713, 2022. [297] Z. You, S. Feng, D. Su, and D. Yu, “Speechmoe2: Mixture-ofexperts model with improved routing,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 7217–7221. [298] J. Puigcerver, R. Jenatton, C. Riquelme, P. Awasthi, and S. Bhojanapalli, “On the adversarial robustness of mixture of experts,” Advances in Neural Information Processing Systems, vol. 35, pp. 9660–9671, 2022. [299] 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. [300] L. Wu, M. Liu, Y. Chen, D. Chen, X. Dai, and L. Yuan, “Residual mixture of experts,” arXiv preprint arXiv:2204.09636, 2022. [301] B. Zoph, I. Bello, S. Kumar, N. Du, Y. Huang, J. Dean, N. Shazeer, and W. Fedus, “Designing effective sparse expert models,” arXiv preprint arXiv:2202.08906, vol. 2, 2022. [302] ——, “St-moe: Designing stable and transferable sparse expert models,” arXiv preprint arXiv:2202.08906, 2022. [303] Y. Chow, A. Tulepbergenov, O. Nachum, M. Ryu, M. Ghavamzadeh, and C. Boutilier, “A mixture-of-expert approach to rl-based dialogue management,” arXiv preprint arXiv:2206.00059, 2022. [304] Z. Fan, R. Sarkar, Z. Jiang, T. Chen, K. Zou, Y. Cheng, C. Hao, Z. Wang et al., “M3vit: Mixture-of-experts vision transformer for efficient multitask learning with model-accelerator co-design,” Advances in Neural Information Processing Systems, vol. 35, pp. 28 441–28 457, 2022. [305] T. Zadouri, A. Ust ¨un, A. Ahmadian, B. Ermis¸, A. Locatelli, and ¨ S. Hooker, “Pushing mixture of experts to the limit: Extremely parameter efficient moe for instruction tuning,” arXiv preprint arXiv:2309.05444, 2023. [306] J. Zhu, X. Zhu, W. Wang, X. Wang, H. Li, X. Wang, and J. Dai, “Uniperceiver-moe: Learning sparse generalist models with conditional moes,” Advances in Neural Information Processing Systems, vol. 35, pp. 2664–2678, 2022. [307] F. Dou, J. Ye, G. Yuan, Q. Lu, W. Niu, H. Sun, L. Guan, G. Lu, G. Mai, N. Liu et al., “Towards artificial general intelligence (agi) in the internet of things (iot): Opportunities and challenges,” arXiv preprint arXiv:2309.07438, 2023. [308] Z. Jia, X. Li, Z. Ling, S. Liu, Y. Wu, and H. Su, “Improving policy optimization with generalist-specialist learning,” in International Conference on Machine Learning. PMLR, 2022, pp. 10 104–10 119. [309] M. Simeone, “Unknown future, repeated present: A narrative-centered analysis of long-term ai discourse,” Humanist Studies & the Digital Age, vol. 7, no. 1, 2022. [310] A. Nair and F. Banaei-Kashani, “Bridging the gap between artificial intelligence and artificial general intelligence: A ten commandment framework for human-like intelligence,” arXiv preprint arXiv:2210.09366, 2022. [311] 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. [312] D. J. Edwards, C. McEnteggart, and Y. Barnes-Holmes, “A functional contextual account of background knowledge in categorization: Implications for artificial general intelligence and cognitive accounts of general knowledge,” Frontiers in Psychology, vol. 13, p. 745306, 2022. [313] J. McCarthy, “Artificial intelligence, logic, and formalising common sense,” Machine Learning and the City: Applications in Architecture and Urban Design, pp. 69–90, 2022. [314] 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. [315] S. Makridakis, “The forthcoming artificial intelligence (ai) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, 2017. [316] S. Pal, K. Kumari, S. Kadam, and A. Saha, “The ai revolution,” IARA Publication, 2023. [317] S. Verma, R. Sharma, S. Deb, and D. Maitra, “Artificial intelligence in marketing: Systematic review and future research direction,” International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100002, 2021. [318] 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. [319] J. B. Telkamp and M. H. Anderson, “The implications of diverse human moral foundations for assessing the ethicality of artificial intelligence,” Journal of Business Ethics, vol. 178, no. 4, pp. 961–976, 2022. [320] X. Zhou, C. Liu, L. Zhai, Z. Jia, C. Guan, and Y. Liu, “Interpretable and robust ai in eeg systems: A survey,” arXiv preprint arXiv:2304.10755, 2023. [321] C. Zhang, C. Zhang, C. Li, Y. Qiao, S. Zheng, S. K. Dam, M. Zhang, J. U. Kim, S. T. Kim, J. Choi et al., “One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era,” arXiv preprint arXiv:2304.06488, 2023. [322] K. Singhal, T. Tu, J. Gottweis, R. Sayres, E. Wulczyn, L. Hou, K. Clark, S. Pfohl, H. Cole-Lewis, D. Neal et al., “Towards expertlevel medical question answering with large language models,” arXiv preprint arXiv:2305.09617, 2023. [323] S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann, “Bloomberggpt: A large language model for finance,” arXiv preprint arXiv:2303.17564, 2023. [324] P. Henderson, K. Sinha, N. Angelard-Gontier, N. R. Ke, G. Fried, R. Lowe, and J. Pineau, “Ethical challenges in data-driven dialogue systems,” in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, pp. 123–129. [325] 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. [326] 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. [327] A. P. Siddaway, A. M. Wood, and L. V. Hedges, “How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses,” Annual review of psychology, vol. 70, pp. 747–770, 2019. [328] E. Landhuis, “Scientific literature: Information overload,” Nature, vol. 535, no. 7612, pp. 457–458, 2016. [329] G. D. Chloros, V. P. Giannoudis, and P. V. Giannoudis, “Peer-reviewing in surgical journals: revolutionize or perish?” Annals of surgery, vol. 275, no. 1, pp. e82–e90, 2022. [330] K.-A. Allen, J. Reardon, Y. Lu, D. V. Smith, E. Rainsford, and L. Walsh, “Towards improving peer review: Crowd-sourced insights from twitter,” Journal of university teaching & learning practice, vol. 19, no. 3, p. 02, 2022. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv