From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape

Probably not a substantial claim: may not pass S3 (load-bearing).

The exponential increase in preprints posted on arXiv under cs.AI appears to signify a paradigm shift in research dissemination within the AI community.

“Meanwhile, the exponential increase in the number of preprints posted on arXiv under the Computer Science > Artificial Intelligence (cs.AI) category, as illustrated in Figure 2, appears to signify a paradigm shift in research dissemination within the AI community.”

Probably not a substantial claim: may not pass S3 (load-bearing).

ChatGPT achieved rapid adoption by over 100 million users shortly after launch.

“ChatGPT has shown impressive conversational skills and contextual understanding with a broad spectrum of functional uses in many areas, as evidenced by its technical and commercial success, including rapid adoption by over 100 million users shortly after launch”

• cites [8] (2023, December 17). Gemini Team, Google. Gemini: A Family of Highly Capable Multimodal Models. Available online: https://gemini.google.com.Not checked yet

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• cites [50] Ney, 1994, On structuring probabilistic dependences in stochastic language modelling, Comput. Speech Lang. sourceNot checked yet

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• cites [51] Hochreiter, 1997, Long short-term memory, Neural Comput. sourceNot checked yet

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Creative AI was scored as requiring redirection (4) in MoE, emerging (5) in multimodality, and emerging (5) in AGI, with an overall score of 14.

“| Creative AI | ֒ → (4) | ր (5) | ր (5) | 14 |”

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Supervised Learning was assigned a redirection score of 4, a relevance score of 3 in multimodality, and a score indicating potential redundancy of 2 in the context of AGI, culminating in an overall score of 9.

“Supervised Learning has been assigned a redirection score ( ֒ → ) of 4, a relevance score ( ↔ ) of 3 in multimodality, and a score indicating potential redundancy ( ց ) of 2 in the context of AGI, culminating in an overall score of 9.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Reinforcement Learning is rated as still relevant with a score of 3 in MoE, requiring redirection with a score of 4 in multimodality, and identified as an emerging research area with a score of 5 in AGI, giving it a total score of 12.

“Reinforcement Learning is rated as still relevant ( ↔ ) with a score of 3 in MoE, requiring redirection ( ֒ → ) with a score of 4 in multimodality, and identified as an emerging research area ( ր ) with a score of 5 in AGI, giving it a total score of 12.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Self-supervised Learning was scored as requiring redirection (4) in MoE, emerging (5) in multimodality, and still relevant (3) in AGI, with an overall score of 12.

“| Self-supervised Learning | ֒ → (4) | ր (5) | ↔ (3) | 12 |”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AGI Containment was scored as inherently unresolvable (1) in MoE, inherently unresolvable (1) in multimodality, and emerging (5) in AGI, with an overall score of 7.

“| AGI Containment | △ (1) | △ (1) | ր (5) | 7 |”

May combine two claims.

Multimodal Models received high scores for emerging research directions of 5 in both MoE and AGI contexts, alongside a score of 3 for current relevance in multimodality, culminating in an overall score of 13.

“Multimodal Models have received high scores for emerging research directions ( ր ) of 5 in both MoE and AGI contexts, alongside a score of 3 ( ↔ ) for current relevance in multimodality, culminating in an overall score of 13.”

May combine two claims.

AGI Development was scored as requiring redirection (4) in MoE, requiring redirection (4) in multimodality, and still relevant (3) in AGI, with an overall score of 11.

“| AGI Development | ֒ → (4) | ֒ → (4) | ↔ (3) | 11 |”

May combine two claims.

Natural Language Understanding holds steady relevance with a score of 3 in both MoE and multimodality, and an emerging direction score of 5 in AGI, totaling an overall score of 11.

“Natural Language Understanding holds steady relevance ( ↔ ) with a score of 3 in both MoE and multimodality, and an emerging direction ( ր ) score of 5 in AGI, totaling an overall score of 11.”

May combine two claims.

Recurrent Neural Networks were scored as likely to become redundant (2) in both MoE and AGI contexts and still relevant (3) in multimodality, totaling a score of 7.

“Recurrent Neural Networks (RNNs) are facing a potential decline in relevance, as indicated by their scores: likely to become redundant ( ց ) 2 in both MoE and AGI contexts and still relevant ( ↔ ) 3 in multimodality, totaling a score of 7.”

• AssumptionRests on an assumption: RNNs are challenged by limitations in handling long-range dependencies and lower efficiency compared to newer models like transformers.

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AI Ethics was scored as requiring redirection (4) in MoE, requiring redirection (4) in multimodality, and inherently unresolvable (1) in AGI, with an overall score of 9.

“| AI Ethics | ֒ → (4) | ֒ → (4) | △ (1) | 9 |”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Unsupervised Learning scores a redirection requirement of 4 in both MoE and AGI contexts and maintains its relevance with a score of 3 in multimodality, resulting in a total score of 11.

“Unsupervised Learning scores a redirection requirement ( ֒ → ) of 4 in both MoE and AGI contexts and maintains its relevance ( ↔ ) with a score of 3 in multimodality, resulting in a total score of 11.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Data Security was scored as still relevant (3) in MoE, still relevant (3) in multimodality, and still relevant (3) in AGI, with an overall score of 9.

“| Data Security | ↔ (3) | ↔ (3) | ↔ (3) | 9 |”

May combine two claims.

Natural Language Generation was scored as still relevant (3) in MoE, requiring redirection (4) in multimodality, and emerging (5) in AGI, with an overall score of 12.

“| Natural Language Generation | ↔ (3) | ֒ → (4) | ր (5) | 12 |”

May combine two claims.

Fine Tuning was scored as still relevant (3) in MoE, still relevant (3) in multimodality, and likely to become redundant (2) in AGI, with an overall score of 8.

“| Fine Tuning | ↔ (3) | ↔ (3) | ց (2) | 8 |”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Bias Mitigation was scored as requiring redirection (4) in MoE, requiring redirection (4) in multimodality, and emerging (5) in AGI, with an overall score of 13.

“| Bias Mitigation | ֒ → (4) | ֒ → (4) | ր (5) | 13 |”

May combine two claims.

Conversational AI was scored as requiring redirection (4) in MoE, emerging (5) in multimodality, and emerging (5) in AGI, with an overall score of 14.

“| Conversational AI | ֒ → (4) | ր (5) | ր (5) | 14 |”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Interactive and Cooperative AI was scored as requiring redirection (4) in MoE, still relevant (3) in multimodality, and emerging (5) in AGI, with an overall score of 12.

“| Interactive and Cooperative AI | ֒ → (4) | ↔ (3) | ր (5) | 12 |”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Privacy Preservation was scored as requiring redirection (4) in MoE, requiring redirection (4) in multimodality, and requiring redirection (4) in AGI, with an overall score of 12.

“| Privacy Preservation | ֒ → (4) | ֒ → (4) | ֒ → (4) | 12 |”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Meta-learning was scored as still relevant (3) in MoE, still relevant (3) in multimodality, and emerging (5) in AGI, with an overall score of 11.

“| Meta-learning | ↔ (3) | ↔ (3) | ր (5) | 11 |”

May combine two claims.

MoE models were scored a consistent relevance of 3 in their own development, a score of 5 in multimodality, and a redirection score of 4 in the context of AGI, amounting to an overall score of 12.

“The MoE models have scored a consistent relevance ( ↔ ) of 3 in their own development and a score of 5 ( ր ) in multimodality, combined with a redirection score ( ֒ → ) of 4 in the context of AGI, amounting to an overall score of 12.”

May combine two claims.

Multimodal Learning was scored as emerging (5) in MoE, still relevant (3) in multimodality, and emerging (5) in AGI, with an overall score of 13.

“| Multimodal Learning | ր (5) | ↔ (3) | ր (5) | 13 |”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Transfer Learning receives a consistent relevance score of 3 in MoE, a high score for emerging research directions of 5 in multimodality, and a redirection requirement of 4 in AGI, accumulating to an overall score of 12.

“Transfer Learning receives a consistent relevance score ( ↔ ) of 3 in MoE, a high score for emerging research directions ( ր ) of 5 in multimodality, and a redirection requirement ( ֒ → ) of 4 in AGI, accumulating to an overall score of 12.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Human Value Alignment was scored as inherently unresolvable (1) in MoE, inherently unresolvable (1) in multimodality, and inherently unresolvable (1) in AGI, with an overall score of 3.

“| Human Value Alignment | △ (1) | △ (1) | △ (1) | 3 |”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

The rapid commercialization and adoption of tools such as ChatGPT, as evidenced by over 55,700 entries on Google Scholar mentioning 'ChatGPT' within just one year of its commercialization, exemplify the accelerated pace at which the field is advancing.

“The rapid commercialization and adoption of tools such as ChatGPT, as evidenced by over 55,700 entries on Google Scholar mentioning 'ChatGPT' within just one year of its commercialization, exemplify the accelerated pace at which the field is advancing.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

MoE, multimodality, and AGI are three nascent research domains that stand to reshape the generative AI research landscape profoundly.

“In doing so, we have pinpointed three nascent research domains-MoE, multimodality, and AGI-that stand to reshape the generative AI research landscape profoundly.”

May combine two claims.

The development of multimodal AI systems faces technical hurdles including creating robust and diverse datasets, managing scalability, and enhancing user trust and system interpretability.

“The development of multimodal AI systems faces several technical hurdles, including creating robust and diverse datasets, managing scalability, and enhancing user trust and system interpretability [113], [114], [115].”

• cites [113] Li, Y., Jiang, S., Hu, B., Wang, L., Zhong, W., Luo, W., Ma, L., and Zhang, M. (2024). Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts. arXiv.Not checked yet

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• cites [114] Fedus, W., Dean, J., and Zoph, B. (2022). A review of sparse expert models in deep learning. arXiv.Not checked yet

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• cites [115] Nowaz Rabbani Chowdhury, M., Zhang, S., Wang, M., Liu, S., and Chen, P.Y. (2023). Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. arXiv.Not checked yet

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RNNs excel in sequence modeling, making them particularly effective for tasks involving language and temporal data, because their architecture is designed to process sequences of data such as text, enabling them to capture context and order.

“RNNs excel in the realm of sequence modeling, making them particularly effective for tasks involving language and temporal data, as their architecture is specifically designed to process sequences of data, such as text, enabling them to capture the context and order of the input effectively [150], [151], [152], [153], [154].”

• cites [150] Muppalla, S., Jia, S., and Lyu, S. (2023). Integrating Audio-Visual Features for Multimodal Deepfake Detection. arXiv. sourceNot checked yet

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• cites [151] Kumar, 2022, Privacy preservation and security challenges: A new frontier multimodal machine learning research, Int. J. Sens. Netw. sourceNot checked yet

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• cites [152] Marchang, J., and Di Nuovo, A. (2022). Assistive multimodal robotic system (AMRSys): Security and privacy issues, challenges, and possible solutions. Appl. Sci., 12. sourceNot checked yet

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These innovations symbolize extended capabilities of transformer models in areas such as object detection, offering improved performance and increased computational efficiency.

“These innovations symbolize the extended capabilities of transformer models in areas such as object detection, offering not only improved performance but also increased computational efficiency.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Transformer models have significantly revolutionized AI, especially NLP, due to their higher efficiency and scalability.

“Transformer models have significantly revolutionized the field of AI, especially in NLP, due to their higher efficiency and scalability [139], [140], [141].”

• cites [139] Zhang, 2020, Multimodal intelligence: Representation learning, information fusion, and applications, IEEE J. Sel. Top. Signal Process. sourceNot checked yet

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• cites [140] Qiao, H., Liu, V., and Chilton, L. (2022, January 20–23). Initial images: Using image prompts to improve subject representation in multimodal ai generated art. Proceedings of the 14th Conference on Creativity and Cognition, Venice, Italy. sourceNot checked yet

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• cites [141] Stewart, 2021, Multimodal modeling of collaborative problem-solving facets in triads, User Model. User-Adapt. Interact. sourceNot checked yet

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Supervised learning uses labeled datasets to guide models towards accurate predictions and has been integral to applications including image recognition and NLP.

“Supervised learning, a foundational approach in AI, uses labeled datasets to guide models towards accurate predictions, and it has been integral to various applications, including image recognition and NLP [163], [164], [165].”

• cites [163] Gao, Y., Zhou, M., Liu, D., Yan, Z., Zhang, S., and Metaxas, D.N. (2022). A data-scalable transformer for medical image segmentation: Architecture, model efficiency, and benchmark. arXiv.Not checked yet

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• cites [164] Peebles, W., and Xie, S. (2023, January 2–6). Scalable diffusion models with transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France. sourceNot checked yet

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• cites [165] Pope, R., Douglas, S., Chowdhery, A., Devlin, J., Bradbury, J., Heek, J., Xiao, K., Agrawal, S., and Dean, J. (2023, January 4–8). Efficiently scaling transformer inference. Proceedings of the 6th Machine Learning and Systems, Miami, FL, USA.Not checked yet

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RNNs' proficiency in handling sequential information renders them indispensable in applications requiring deep understanding of temporal dynamics, such as natural language tasks and time-series analysis.

“This proficiency in handling sequential information renders them indispensable in applications that require a deep understanding of the temporal dynamics within data, such as natural language tasks and time-series analysis [155], [156].”

• cites [155] Wolfe, R., Yang, Y., Howe, B., and Caliskan, A. (2023, January 12–15). Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL, USA. sourceNot checked yet

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• cites [156] Afshar, 2022, Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): A retrospective deep learning study, Lancet Digit. Health sourceNot checked yet

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MoE models divide computational loads among various experts, each specializing in different aspects of data, allowing more effective handling of vast parameter scales and more efficient specialized handling of complex tasks.

“MoE models stand out for their ability to divide computational loads among various experts, each specializing in different aspects of the data, which allows for handling vast scales of parameters more effectively, leading to a more efficient and specialized handling of complex tasks [94], [21].”

• cites [94] Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., and Hinton, G. (2017). Regularizing neural networks by penalizing confident output distributions. arXiv.Not checked yet

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• cites [21] DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., and Zhang, C. (2024). DeepSeek-V3 Technical Report. arXiv.Not checked yet

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Combination of different data types processed concurrently enables multimodal models to provide a holistic view, making them especially effective in applications requiring deep and multifaceted understanding of complex scenarios.

“The combination of different data types, processed concurrently, enables these models to provide a holistic view, making them especially effective in applications that require a deep and multifaceted understanding of complex scenarios [113], [161], [162], [160].”

• cites [113] Li, Y., Jiang, S., Hu, B., Wang, L., Zhong, W., Luo, W., Ma, L., and Zhang, M. (2024). Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts. arXiv.Not checked yet

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• cites [161] Miao, 2023, DAO to HANOI via DeSci: AI paradigm shifts from AlphaGo to ChatGPT, IEEE/CAA J. Autom. Sin. sourceNot checked yet

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• cites [162] Rong, Y. (2022, January 25–27). Roadmap of AlphaGo to AlphaStar: Problems and challenges. Proceedings of the 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), Zhuhai, China. sourceNot checked yet

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MoE models can significantly enhance efficiency by deploying model parallelism across multiple specialized expert modules, enabling transformer-based modules for dynamic token routing and scaling to trillions of parameters, reducing memory footprint and computational costs.

“MoE models can significantly enhance efficiency by deploying model parallelism across multiple specialized expert modules, which enables these models to leverage transformer-based modules for dynamic token routing, and to scale to trillions of parameters, thereby reducing both memory footprint and computational costs [94], [98].”

• cites [94] Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., and Hinton, G. (2017). Regularizing neural networks by penalizing confident output distributions. arXiv.Not checked yet

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• cites [98] Song, 2023, A Surrogate-Assisted Evolutionary Framework With Regions of Interests-Based Data Selection for Expensive Constrained Optimization, IEEE Trans. Syst. Man Cybern. Syst. sourceNot checked yet

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Transformer models employ advanced attention mechanisms to achieve enhanced contextual processing, allowing for more subtle understanding and interaction.

“They employ advanced attention mechanisms to achieve enhanced contextual processing, allowing for more subtle understanding and interaction [142], [143], [144].”

• cites [142] Xue, L., Yu, N., Zhang, S., Li, J., Martín-Martín, R., Wu, J., Xiong, C., Xu, R., Niebles, J.C., and Savarese, S. (2023). ULIP-2: Towards Scalable Multimodal Pre-training For 3D Understanding. arXiv. sourceNot checked yet

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• cites [143] Yan, L., Zhao, L., Gasevic, D., and Martinez-Maldonado, R. (2022, January 21–25). Scalability, sustainability, and ethicality of multimodal learning analytics. Proceedings of the LAK22: 12th International Learning Analytics and Knowledge Conference, Online. sourceNot checked yet

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• cites [144] Okazaki, 2022, Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience, J. Acad. Mark. Sci. sourceNot checked yet

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MoE leverages multiple transformer-based expert modules for dynamic token routing, enhancing modeling efficiency and scalability.

“This innovative approach, exemplified by advanced models like Google's Switch Transformer 5 and MistralAI s Mixtral-8x7B 6 , leverages multiple transformerbased expert modules for dynamic token routing, enhancing modeling efficiency and scalability.”

• cites [5] Minsky, 1969, An introduction to computational geometry, Camb. Tiass. HITNot checked yet

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• cites [6] Rumelhart, 1986, Learning representations by back-propagating errors, Nature sourceNot checked yet

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Multimodal models integrate sensory inputs such as text, vision, and audio, and are crucial for comprehensive understanding of complex data sets, particularly transformative in medical imaging.

“Multimodal models, which integrate a variety of sensory inputs such as text, vision, and audio, are crucial in achieving a comprehensive understanding of complex data sets, particularly transformative in fields like medical imaging [113], [112], [115].”

• cites [113] Li, Y., Jiang, S., Hu, B., Wang, L., Zhong, W., Luo, W., Ma, L., and Zhang, M. (2024). Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts. arXiv.Not checked yet

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• cites [112] Tian, Y., Xia, F., and Song, Y. (2024, January 11–16). Dialogue summarization with mixture of experts based on large language models. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand. sourceNot checked yet

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• cites [115] Nowaz Rabbani Chowdhury, M., Zhang, S., Wang, M., Liu, S., and Chen, P.Y. (2023). Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. arXiv.Not checked yet

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RNNs' ability to maintain continuity over sequences is a critical asset in AI, especially where context and historical data play crucial roles.

“RNNs' ability to maintain a sense of continuity over sequences is a critical asset in the broader field of AI, especially in scenarios where context and historical data play crucial roles [157].”

• cites [157] Alwahaby, H., Cukurova, M., Papamitsiou, Z., and Giannakos, M. (2022). The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review. The Multimodal Learning Analytics Handbook, Springer. sourceNot checked yet

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Multimodal AI can propagate and amplify biases and stereotypes across different modalities, and if unchecked, this can perpetuate discrimination and social inequities.

“Moreover, multimodal AI can propagate and amplify biases and stereotypes across different modalities, and if unchecked, this can perpetuate discrimination and social inequities”

• cites [132] Google DeepMind (2024). Introducing Gemini 2.0: Our New AI Model for the Agentic Era, Google.Not checked yet

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• cites [133] Akhtar, 2024, From bard to Gemini: An investigative exploration journey through Google’s evolution in conversational AI and generative AI, Comput. Artif. Intell. sourceNot checked yet

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• cites [134] Acosta, 2022, Multimodal biomedical AI, Nat. Med. sourceNot checked yet

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AlphaGo's mastery in the game of Go highlighted the effectiveness of deep learning and tree search algorithms within well-defined rule-based environments, underscoring the potential of AI in complex strategy and decision-making.

“AlphaGo's mastery in the game of Go highlighted the effectiveness of deep learning and tree search algorithms within well-defined rule-based environments, underscoring the potential of AI in complex strategy and decision-making [137], [138].”

• cites [137] Birhane, A., Prabhu, V.U., and Kahembwe, E. (2021). Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv.Not checked yet

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• cites [138] Li, 2021, Multimodal information interaction and fusion for the parallel computing system using AI techniques, Int. J. High Perform. Syst. Archit.Not checked yet

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The advancement of LLMs has underlined the significance of fine-tuning, hallucination reduction, and alignment.

“The advancement of LLMs has underlined the significance of fine-tuning [60], [61], [62], [63], hallucination reduction [64], [65], [66], [67], and alignment [68], [69], [70], [71], [72].”

• cites [60] Ansah, 2023, Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning, J. AI sourceNot checked yet

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• cites [64] George, 2023, A review of ChatGPT AI’s impact on several business sectors, Partners Univers. Int. Innov. J.Not checked yet

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• cites [68] Mitchell, 2023, AI’s challenge of understanding the world, Science sourceNot checked yet

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Transformer models have made notable strides in computer vision, as evidenced by vision transformers like EfficientViT and YOLOv8.

“These models have also made notable strides in computer vision, as evidenced by the development of vision transformers like EfficientViT [145], [146] and YOLOv8 [147], [148], [149].”

• cites [145] Rahman, 2023, The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement, J. Retail. Consum. Serv. sourceNot checked yet

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• cites [146] Sachdeva, E., Agarwal, N., Chundi, S., Roelofs, S., Li, J., Dariush, B., Choi, C., and Kochenderfer, M. (2023). Rank2tell: A multimodal driving dataset for joint importance ranking and reasoning. arXiv. sourceNot checked yet

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• cites [147] Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., and Liao, K.D. (2023). A survey on multimodal large language models for autonomous driving. arXiv. sourceNot checked yet

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Recent advancements in NLU prominently feature large transformer-based models like BERT and GPT-3, which have significantly advanced the field by enabling deeper and more complex understanding of language subtleties.

“Recent advancements have prominently featured large transformer-based models like BERT and GPT-3, which have significantly advanced the field by enabling a deeper and more complex understanding of language subtleties [197], [198].”

• cites [197] Sam, D., and Kolter, J.Z. (2023, January 7–14). Losses over labels: Weakly supervised learning via direct loss construction. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA. sourceNot checked yet

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• cites [198] Wang, M., Xie, P., Du, Y., and Hu, X. (2023). T5-Based Model for Abstractive Summarization: A Semi-Supervised Learning Approach with Consistency Loss Functions. Appl. Sci., 13. sourceNot checked yet

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Transfer learning through pre-trained networks streamlines training by allowing efficient fine-tuning for specific applications, enhancing adaptability and performance across diverse tasks, particularly when acquiring extensive labeled data is impractical.

“Transfer learning, through the use of pre-trained networks, streamlines the training process by allowing models to be efficiently fine-tuned for specific applications, thereby enhancing adaptability and performance across diverse tasks, and proving particularly beneficial in scenarios where acquiring extensive labeled data is impractical or unfeasible [188], [189].”

• cites [188] Guo, Z., Tang, Y., Zhang, R., Wang, D., Wang, Z., Zhao, B., and Li, X. (2023, January 2–6). Viewrefer: Grasp the multi-view knowledge for 3d visual grounding. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France. sourceNot checked yet

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• cites [189] Pan, C., He, Y., Peng, J., Zhang, Q., Sui, W., and Zhang, Z. (2023, January 18–22). BAEFormer: Bi-Directional and Early Interaction Transformers for Bird’s Eye View Semantic Segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada. sourceNot checked yet

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Transfer learning allows models to apply knowledge acquired from one task to different yet related tasks, significantly reducing the need for large labeled datasets.

“Transfer learning emphasizes versatility and efficiency in AI training, allowing models to apply knowledge acquired from one task to different yet related tasks, which significantly reduces the need for large labeled datasets [186], [187].”

• cites [186] Du, N., Huang, Y., Dai, A.M., Tong, S., Lepikhin, D., Xu, Y., Krikun, M., Zhou, Y., Yu, A.W., and Firat, O. (2022, January 17–23). Glam: Efficient scaling of language models with mixture-of-experts. Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA.Not checked yet

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• cites [187] Dhivya, K., Kumar, S.N., Victoria, D.R.S., Sherly, S.I., and Durgadevi, G. (2025). Advanced Neural Networks for Multimodal Data Fusion in Interdisciplinary Research. Advanced Interdisciplinary Applications of Deep Learning for Data Science, IGI Global Scientific Publishing. sourceNot checked yet

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Autoencoders and GANs have notably expanded unsupervised learning's applicability, enabling more sophisticated data generation and representation learning.

“This method has seen significant advancements with the introduction of autoencoders [171], [172] and Generative Adversarial Networks (GANs) [173], [174], [175], which have notably expanded unsupervised learning's applicability, enabling more sophisticated data generation and representation learning capabilities.”

• cites [171] Li, Y., Fan, Q., Huang, H., Han, Z., and Gu, Q. (2023). A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition. Drones, 7. sourceNot checked yet

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• cites [172] Talaat, 2023, An improved fire detection approach based on YOLO-v8 for smart cities, Neural Comput. Appl. sourceNot checked yet

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• cites [173] Tamang, 2023, Enhancing covid-19 safety: Exploring yolov8 object detection for accurate face mask classification, Int. J. Intell. Syst. Appl. Eng.Not checked yet

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Recent surge in NLG capabilities, exemplified by GPT-3, has significantly enhanced sophistication and nuance of text generation, enabling AI systems to produce text closely mirroring human writing styles, broadening NLG's scope and applicability.

“The recent surge in NLG capabilities, exemplified by advanced models like GPT-3, has significantly enhanced the sophistication and nuance of text generation, which enable AI systems to produce text that closely mirrors human writing styles, thereby broadening the scope and applicability of NLG in various interactive and creative contexts [203], [55], [51].”

• cites [203] Ayanoglu, 2022, Machine learning in NextG networks via generative adversarial networks, IEEE Trans. Cogn. Commun. Netw. sourceNot checked yet

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• cites [55] Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., and Brockman, G. (2021). Evaluating Large Language Models Trained on Code. arXiv.Not checked yet

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• cites [51] Hochreiter, 1997, Long short-term memory, Neural Comput. sourceNot checked yet

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Such frameworks are crucial in ensuring AI systems are built with core emphasis on ethical considerations, fairness, and transparency, addressing bias mitigation, privacy and security, and AI ethics for accountability.

“Such frameworks are crucial in ensuring AI systems are built with a core emphasis on ethical considerations, fairness, and transparency, as they address critical aspects such as bias mitigation for fairness, privacy and security concerns for data protection, and AI ethics for accountability, thus responding to the evolving landscape where accountability in AI is of paramount importance [214], [15]”

• cites [214] Lin, 2023, Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook, Expert Syst. Appl. sourceNot checked yet

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• cites [15] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.Not checked yet

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Challenges in Creative AI involve finding suitable data representations, algorithms, and evaluation metrics to effectively assess and foster creativity.

“The challenges in this field involve finding suitable data representations, algorithms, and evaluation metrics to effectively assess and foster creativity [212], [213].”

• cites [212] Hassan, S.S., Park, Y.M., Tun, Y.K., Saad, W., Han, Z., and Hong, C.S. (2022, January 16–20). 3TO: THz-enabled throughput and trajectory optimization of UAVs in 6G networks by proximal policy optimization deep reinforcement learning. Proceedings of the ICC 2022-IEEE International Conference on Communications, Seoul, Republic of Korea. sourceNot checked yet

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• cites [213] Jayant, 2022, Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm, Adv. Neural Inf. Process. Syst.Not checked yet

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Creative AI spans text, art, music, and more, pushing boundaries of AI's creative potential across modalities including images, audio, and video, by generating artistic content, with commercial success like MidJourney and DALL-E.

“This emerging subdomain spans across text, art, music, and more, pushing the boundaries of AI's creative and innovative potential across various modalities including images, audio, and video, by engaging in the generation of artistic content, encompassing applications in idea generation, storytelling, poetry, music composition, visual arts, and creative writing, and has resulted in commercial”

• cites [210] Hafiz, 2023, Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks, Comput. Syst. Sci. Eng. sourceNot checked yet

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• cites [211] Alagha, 2022, Target localization using multi-agent deep reinforcement learning with proximal policy optimization, Future Gener. Comput. Syst. sourceNot checked yet

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• cites [212] Hassan, S.S., Park, Y.M., Tun, Y.K., Saad, W., Han, Z., and Hong, C.S. (2022, January 16–20). 3TO: THz-enabled throughput and trajectory optimization of UAVs in 6G networks by proximal policy optimization deep reinforcement learning. Proceedings of the ICC 2022-IEEE International Conference on Communications, Seoul, Republic of Korea. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Creative AI serves as a tool for automating and enhancing artistic processes and as a medium for exploring new forms of artistic expression, enabling creation of novel and diverse creative outputs.

“Creative AI serves not only as a tool for automating and enhancing artistic processes, but also as a medium for exploring new forms of artistic expression, enabling the creation of novel and diverse creative outputs [212].”

• cites [212] Hassan, S.S., Park, Y.M., Tun, Y.K., Saad, W., Han, Z., and Hong, C.S. (2022, January 16–20). 3TO: THz-enabled throughput and trajectory optimization of UAVs in 6G networks by proximal policy optimization deep reinforcement learning. Proceedings of the ICC 2022-IEEE International Conference on Communications, Seoul, Republic of Korea. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Conversational AI is dedicated to developing AI systems capable of smooth, natural, and context-aware human-computer interactions, focusing on dialogue modeling, question answering, user intent recognition, and multi-turn context tracking.

“This subdomain is dedicated to developing AI systems capable of smooth, natural, and context-aware human-computer interactions, by focusing on dialogue modeling, question answering, user intent recognition, and multi-turn context tracking [204], [205], [206], [207].”

• cites [204] Yan, 2022, Physical model informed fault detection and diagnosis of air handling units based on transformer generative adversarial network, IEEE Trans. Ind. Inform. sourceNot checked yet

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• cites [205] Zhou, 2023, Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution, Signal Process. Image Commun. sourceNot checked yet

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• cites [206] Ladosz, 2022, Exploration in deep reinforcement learning: A survey, Inf. Fusion sourceNot checked yet

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Development of DQN and PPO algorithms has significantly advanced reinforcement learning, improving efficacy and applicability in complex and dynamic environments.

“This training technique has undergone significant advancements, particularly with the development of Deep Q-Networks (DQN) [178], [179], [180] and Proximal Policy Optimization (PPO) algorithms [181], [182], [183].”

• cites [178] Zhu, 2022, Application of recurrent neural network to mechanical fault diagnosis: A review, J. Mech. Sci. Technol. sourceNot checked yet

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• cites [179] Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., and Zhang, H. (2023). Segrnn: Segment recurrent neural network for long-term time series forecasting. arXiv.Not checked yet

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• cites [180] Wei, Z., Zhang, X., and Sun, M. (2022, January 24–27). Extracting weighted finite automata from recurrent neural networks for natural languages. Proceedings of the International Conference on Formal Engineering Methods, ICFEM 2022, Madrid, Spain. sourceNot checked yet

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By optimizing decisions and policies through interactive feedback loops, reinforcement learning has established itself as a crucial tool for training AI systems in scenarios demanding high adaptability and precision in decision-making.

“By optimizing decisions and policies through interactive feedback loops, reinforcement learning has established itself as a crucial tool for training AI systems in scenarios that demand a high degree of adaptability and precision in decision-making [184], [185].”

• cites [184] Park, B., Go, H., Kim, J.Y., Woo, S., Ham, S., and Kim, C. (2025). Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts, Springer. sourceNot checked yet

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• cites [185] Di Sario, F., Renzulli, R., Tartaglione, E., and Grangetto, M. (2025). Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering, Springer. sourceNot checked yet

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NLU is central to enhancing comprehension and contextualization of human language in AI systems, involving capabilities such as semantic analysis, named entity recognition, sentiment analysis, textual entailment, and machine reading comprehension.

“NLU is central to enhancing the comprehension and contextualization of human language in AI systems, and involves key capabilities such as semantic analysis, named entity recognition, sentiment analysis, textual entailment, and machine reading comprehension [190], [191], [192], [193].”

• cites [190] Xu, 2023, Multimodal learning with transformers: A survey, IEEE Trans. Pattern Anal. Mach. Intell. sourceNot checked yet

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• cites [191] Molenaar, 2023, Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data, Comput. Hum. Behav. sourceNot checked yet

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• cites [192] Steyaert, 2023, Multimodal data fusion for cancer biomarker discovery with deep learning, Nat. Mach. Intell. sourceNot checked yet

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Recent advancements in supervised learning focus on sophisticated loss functions and regularization techniques to enhance performance and generalization, ensuring robustness across tasks and data types.

“Recent advancements have focused on developing sophisticated loss functions and regularization techniques, aimed at enhancing the performance and generalization capabilities of supervised learning models, ensuring they remain robust and effective across a wide range of tasks and data types [166], [167], [168].”

• cites [166] Ding, 2022, Convolutional transformer: An enhanced attention mechanism architecture for remaining useful life estimation of bearings, IEEE Trans. Instrum. Meas. sourceNot checked yet

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• cites [167] Ding, 2022, A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings, Mech. Syst. Signal Process. sourceNot checked yet

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• cites [168] Wang, G., Zhao, Y., Tang, C., Luo, C., and Zeng, W. (March, January 22). When shift operation meets vision transformer: An extremely simple alternative to attention mechanism. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.Not checked yet

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In finance and cybersecurity, AI's predictive analytics have transformed risk assessment and fraud detection, leading to more secure and efficient operations.

“In finance and cybersecurity, AI's predictive analytics have transformed risk assessment and fraud detection, leading to more secure and efficient operations [205], [19].”

• cites [205] Zhou, 2023, Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution, Signal Process. Image Commun. sourceNot checked yet

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• cites [19] Chen, 2022, Towards Understanding the Mixture-of-Experts Layer in Deep Learning, Adv. Neural Inf. Process. Syst.Not checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

Such innovations are crucial for understanding and leveraging complex structures in unstructured datasets, highlighting growing versatility and depth of unsupervised learning.

“Such innovations are crucial for understanding and leveraging the complex structures often inherent in unstructured datasets, highlighting the growing versatility and depth of unsupervised learning techniques.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

NLG encompasses challenges such as topic modeling, discourse planning, concept-to-text generation, style transfer, and controllable text generation.

“NLG encompasses challenges such as topic modeling, discourse planning, concept-to-text generation, style transfer, and controllable text generation [36], [202].”

• cites [36] Dwivedi, 2021, Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy, Int. J. Inf. Manag. sourceNot checked yet

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• cites [202] Yan, 2023, Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises, Robot. Comput.-Integr. Manuf. sourceNot checked yet

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NLG emphasizes training models to generate coherent, contextually-relevant, and creative text responses, critical in chatbots, virtual assistants, and automated content creation tools.

“NLG emphasizes the training of models to generate coherent, contextually-relevant, and creative text responses, a critical component in chatbots, virtual assistants, and automated content creation tools [199], [36], [200], [201].”

• cites [199] Li, 2022, Unsupervised person re-identification with multi-label learning guided self-paced clustering, Pattern Recognit. sourceNot checked yet

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• cites [36] Dwivedi, 2021, Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy, Int. J. Inf. Manag. sourceNot checked yet

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• cites [200] Nancy, P., Pallathadka, H., Naved, M., Kaliyaperumal, K., Arumugam, K., and Garchar, V. (2022, January 4–5). Deep Learning and Machine Learning Based Efficient Framework for Image Based Plant Disease Classification and Detection. Proceedings of the 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), Coimbatore, India. sourceNot checked yet

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NLU is fundamental in applications like sentiment analysis, language translation, information extraction, and more.

“NLU is fundamental in applications like sentiment analysis, language translation, information extraction, and more [194], [195], [196].”

• cites [194] Schiappa, 2023, Self-supervised learning for videos: A survey, ACM Comput. Surv. sourceNot checked yet

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• cites [195] Yu, 2023, Self-supervised learning for recommender systems: A survey, IEEE Trans. Knowl. Data Eng. sourceNot checked yet

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• cites [196] Bharti, 2023, A Label Efficient Semi Self-Supervised Learning Framework for IoT Devices in Industrial Process, IEEE Trans. Ind. Inform. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Ethical considerations and legal compliance have become increasingly crucial as AI technologies evolve and integrate into sectors, requiring focus on developing 'Ethical AI Frameworks'.

“As AI technologies rapidly evolve and become more integrated into various sectors, ethical considerations and legal compliance have become increasingly crucial, which requires a focus on developing 'Ethical AI Frameworks', a new category in our taxonomy reflecting the trend towards responsible AI development in generative AI [214], [215], [15], [216], [217].”

• cites [214] Lin, 2023, Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook, Expert Syst. Appl. sourceNot checked yet

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• cites [215] Luo, 2023, Human-in-the-loop reinforcement learning in continuous-action space, IEEE Trans. Neural Netw. Learn. Syst. sourceNot checked yet

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• cites [15] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Unsupervised learning is essential for uncovering patterns within unlabeled data, central to tasks like feature learning and clustering.

“Unsupervised learning is essential in AI for uncovering patterns within unlabeled data, a process central to tasks like feature learning and clustering [169], [170].”

• cites [169] Cai, H., Li, J., Hu, M., Gan, C., and Han, S. (2023, January 2–6). EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France. sourceNot checked yet

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• cites [170] Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., and Yuan, Y. (2023, January 18–22). EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada. sourceNot checked yet

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This domain represents a significant leap in AI's capability to engage in and contribute to creative endeavors, redefining intersection of technology and art.

“This domain represents a significant leap in AI's capability to engage in and contribute to creative endeavors, redefining the intersection of technology and art.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Advances in NLU have been crucial in improving AI's proficiency in interpreting and analyzing language across contexts, from conversational exchanges to intricate textual data.

“Advances in NLU have been crucial in improving AI's proficiency in interpreting and analyzing language across a spectrum of contexts, ranging from straightforward conversational exchanges to intricate textual data [190], [192], [193].”

• cites [190] Xu, 2023, Multimodal learning with transformers: A survey, IEEE Trans. Pattern Anal. Mach. Intell. sourceNot checked yet

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• cites [192] Steyaert, 2023, Multimodal data fusion for cancer biomarker discovery with deep learning, Nat. Mach. Intell. sourceNot checked yet

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• cites [193] Rani, 2023, Self-supervised learning: A succinct review, Arch. Comput. Methods Eng. sourceNot checked yet

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Bias Mitigation in AI systems is a critical endeavor to ensure fairness and representation, involving balanced data collection, algorithmic adjustments, and regularization techniques to minimize biases.

“Bias Mitigation in AI systems is a critical endeavor to ensure fairness and representation, which involves not only balanced data collection to avoid skewed perspectives but also involves implementing algorithmic adjustments and regularization techniques to minimize biases [218], [219].”

• cites [218] Guo, Z., Lin, K., Chen, X., and Chit, C.Y. (2022, January 11–13). Transfer learning for angle of arrivals estimation in massive mimo system. Proceedings of the 2022 IEEE/CIC International Conference on Communications in China (ICCC), Sanshui, Foshan, China. sourceNot checked yet

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• cites [219] Liu, 2022, Adaptive reconstruction of digital twins for machining systems: A transfer learning approach, Robot. Comput.-Integr. Manuf. sourceNot checked yet

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The need for rigorous approaches to uphold ethical integrity and legal conformity has never been more pressing, reflecting complexity and multifaceted challenges introduced by adoption of these technologies.

“The need for rigorous approaches to uphold ethical integrity and legal conformity has never been more pressing, reflecting the complexity and multifaceted challenges introduced by the adoption of these technologies [15].”

• cites [15] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.Not checked yet

  Not checked in the free run.

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Multimodal Learning focuses on combining language understanding with computer vision and audio processing to achieve richer, multi-sensory context awareness.

“Multimodal Learning in AI, a rapidly evolving subdomain, focuses on combining language understanding with computer vision and audio processing to achieve a richer, multi-sensory context awareness [114], [268].”

• cites [114] Fedus, W., Dean, J., and Zoph, B. (2022). A review of sparse expert models in deep learning. arXiv.Not checked yet

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• cites [268] Zhou, J., and Chen, F. (2022). AI ethics: From principles to practice. AI Soc. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Integration of varied methods highlights self-supervised learning's role in advancing AI's autonomous training capabilities.

“This integration of varied methods highlights selfsupervised learning's role in advancing AI's autonomous training capabilities.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Human Value Alignment concentrates on harmonizing AI models with human ethics and values to ensure decisions and actions mirror societal norms and ethical standards, involving integration of ethical decision-making and adaptation of AI outputs to conform with human moral values.

“This emerging aspect concentrates on harmonizing AI models with human ethics and values to ensure that their decisions and actions mirror societal norms and ethical standards, involving the integration of ethical decision-making processes and the adaptation of AI outputs to conform with human moral values [265], [89], [266].”

• cites [265] Hummel, 2021, Data sovereignty: A review, Big Data Soc. sourceNot checked yet

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• cites [89] Liga, 2023, Fine-tuning GPT-3 for legal rule classification, Comput. Law Secur. Rev. sourceNot checked yet

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• cites [266] Lukings, M., and Habibi Lashkari, A. (2022). Data Sovereignty. Understanding Cybersecurity Law in Data Sovereignty and Digital Governance: An Overview from a Legal Perspective, Springer. sourceNot checked yet

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Advanced learning techniques, including self-supervised learning, meta-learning, and fine-tuning, are at the forefront of AI research, enhancing autonomy, efficiency, and versatility of AI models.

“Advanced learning techniques, including self-supervised learning, meta-learning, and fine-tuning, are at the forefront of AI research, enhancing the autonomy, efficiency, and versatility of AI models.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Fine-tuning has two primary approaches: end-to-end fine-tuning, which adjusts all weights of encoder and classifier, and feature-extraction fine-tuning, where encoder weights are frozen to extract features for a downstream classifier.

“Its two primary approaches are end-to-end fine-tuning, which adjusts all weights of the encoder and classifier [260], [261], and feature-extraction fine-tuning, where the encoder weights are frozen to extract features for a downstream classifier [262], [263], [264].”

• cites [260] Susanto, H., Yie, L.F., Rosiyadi, D., Basuki, A.I., and Setiana, D. (2021). Data security for connected governments and organisations: Managing automation and artificial intelligence. Web 2.0 and Cloud Technologies for Implementing Connected Government, IGI Global. sourceNot checked yet

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• cites [261] Dilmaghani, S., Brust, M.R., Danoy, G., Cassagnes, N., Pecero, J., and Bouvry, P. (2019, January 9–12). Privacy and security of big data in AI systems: A research and standards perspective. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA. sourceNot checked yet

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• cites [262] McIntosh, T. (2022). Intercepting Ransomware Attacks with Staged Event-Driven Access Control. [Ph.D. Thesis, La Trobe University].Not checked yet

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Human value alignment is increasingly important in scenarios where AI interacts closely with humans, such as healthcare, finance, and personal assistants, to ensure decisions are technically sound and ethically and socially responsible.

“This is increasingly important in scenarios where AI interacts closely with humans, such as in healthcare, finance, and personal assistants, to ensure that AI systems make decisions that are not only technically sound, but also ethically and socially responsible, which means human value alignment is becoming crucial in developing AI systems that are trusted and accepted by society [89], [267].”

• cites [89] Liga, 2023, Fine-tuning GPT-3 for legal rule classification, Comput. Law Secur. Rev. sourceNot checked yet

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• cites [267] Hickok, 2021, Lessons learned from AI ethics principles for future actions, AI Ethics sourceNot checked yet

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Fine Tuning involves customizing pre-trained models to specific domains or user preferences, enhancing accuracy and relevance for niche applications.

“Fine Tuning: Involves customizing pre-trained models to specific domains or user preferences, enhancing accuracy and relevance for niche applications [60], [258], [259].”

• cites [60] Ansah, 2023, Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning, J. AI sourceNot checked yet

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• cites [258] Yan, J.N., Gu, Z., Lin, H., and Rzeszotarski, J.M. (2020, January 25–30). Silva: Interactively assessing machine learning fairness using causality. Proceedings of the 2020 Chi Conference on Human Factors in Computing Systems, Honolulu, HI, USA. sourceNot checked yet

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• cites [259] Bertino, E., Kantarcioglu, M., Akcora, C.G., Samtani, S., Mittal, S., and Gupta, M. (2021, January 26–28). AI for Security and Security for AI. Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy, Virtual. sourceNot checked yet

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Self-supervised learning employs self-prediction strategies inspired by NLP, using techniques like masking for input reconstruction, significantly enhanced by recent Vision Transformers developments.

“Further, it employs self-prediction strategies, inspired by NLP, using techniques like masking for input reconstruction, significantly enhanced by recent Vision Transformers developments [249], [250], [165].”

• cites [249] Vasconcelos, M., Cardonha, C., and Gonçalves, B. (2018, January 2–3). Modeling epistemological principles for bias mitigation in AI systems: An illustration in hiring decisions. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, New Orleans, LA, USA. sourceNot checked yet

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• cites [250] Yang, Y., Gupta, A., Feng, J., Singhal, P., Yadav, V., Wu, Y., Natarajan, P., Hedau, V., and Joo, J. (2022, January 1–3). Enhancing fairness in face detection in computer vision systems by demographic bias mitigation. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, Oxford, UK. sourceNot checked yet

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• cites [165] Pope, R., Douglas, S., Chowdhery, A., Devlin, J., Bradbury, J., Heek, J., Xiao, K., Agrawal, S., and Dean, J. (2023, January 4–8). Efficiently scaling transformer inference. Proceedings of the 6th Machine Learning and Systems, Miami, FL, USA.Not checked yet

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Privacy Preservation focuses on maintaining data confidentiality and integrity, employing strategies like anonymization and federated learning to minimize direct data exposure, especially as generative AI poses risks of user profiling.

“This domain focuses on maintaining data confidentiality and integrity, employing strategies like anonymization and federated learning to minimize direct data exposure, especially when the rise of generative AI poses risks of user profiling [238], [239].”

• cites [238] Xiao, Z. (2023). Seeing Us Through Machines: Designing and Building Conversational AI to Understand Humans. [Ph.D. Thesis, University of Illinois at Urbana-Champaign].Not checked yet

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• cites [239] Ko, H.K., Park, G., Jeon, H., Jo, J., Kim, J., and Seo, J. (2023, January 27–31). Large-scale text-to-image generation models for visual artists’ creative works. Proceedings of the 28th International Conference on Intelligent User Interfaces, Sydney, NSW, Australia. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Meta-learning involves mastering optimization process and is critical in situations with limited data availability, ensuring models can quickly adapt and perform across diverse tasks.

“This technique involves mastering the optimization process and is critical in situations with limited data availability, to ensure models can quickly adapt and perform across diverse tasks, essential in the current data-driven landscape [253], [254].”

• cites [253] Kong, Y. (2022, January 21–24). Are “intersectionally fair” ai algorithms really fair to women of color? A philosophical analysis. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea. sourceNot checked yet

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• cites [254] Tan, 2019, Assessing social and intersectional biases in contextualized word representations, Adv. Neural Inf. Process. Syst.Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI Ethics involves ensuring traceability, auditability, and transparency throughout model development lifecycle, employing practices such as algorithmic auditing, ethics boards, and documentation standards and model cards.

“Furthermore, AI Ethics involves ensuring traceability, auditability, and transparency throughout the model development lifecycle, employing practices such as algorithmic auditing, establishing ethics boards, and adhering to documentation standards and model cards [237], [236].”

• cites [237] Kusal, 2022, AI-based conversational agents: A scoping review from technologies to future directions, IEEE Access sourceNot checked yet

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• cites [236] Wan, Y., Wang, W., He, P., Gu, J., Bai, H., and Lyu, M.R. (2023, January 5–7). Biasasker: Measuring the bias in conversational ai system. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, San Francisco, CA, USA. sourceNot checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

These efforts are critical for maintaining integrity of AI systems and protecting user privacy in an evolving digital landscape.

“These efforts are critical for maintaining the integrity of AI systems and protecting user privacy in an evolving digital landscape.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

Continuous monitoring and bias testing are essential to identify and address biases that may emerge from AI's predictive patterns.

“Continuous monitoring and bias testing are essential to identify and address any biases that may emerge from AI's predictive patterns [220], [219].”

• cites [220] Liu, 2023, Logiqa 2.0—An improved dataset for logical reasoning in natural language understanding, IEEE/ACM Trans. Audio Speech Lang. Process. sourceNot checked yet

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• cites [219] Liu, 2022, Adaptive reconstruction of digital twins for machining systems: A transfer learning approach, Robot. Comput.-Integr. Manuf. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Meta-learning focuses on few-shot generalization, enabling AI to handle a wide range of tasks with minimal data, underlining its importance in developing versatile and adaptable AI systems.

“It focuses on few-shot generalization, enabling AI to handle a wide range of tasks with minimal data, underlining its importance in developing versatile and adaptable AI systems [255], [256], [254], [257].”

• cites [255] Cheng, L., Mosallanezhad, A., Sheth, P., and Liu, H. (2021). Causal learning for socially responsible AI. arXiv. sourceNot checked yet

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• cites [256] Correa, J.D., Tian, J., and Bareinboim, E. (February, January 27). Identification of causal effects in the presence of selection bias. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.Not checked yet

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• cites [254] Tan, 2019, Assessing social and intersectional biases in contextualized word representations, Adv. Neural Inf. Process. Syst.Not checked yet

  Not checked in the free run.

May combine two claims.

Adoption of these ethical initiatives remains uneven, highlighting ongoing need for comprehensive and consistent ethical practices in AI development and deployment.

“However, the adoption of these initiatives remains uneven, highlighting the ongoing need for comprehensive and consistent ethical practices in AI development and deployment [214].”

• cites [214] Lin, 2023, Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook, Expert Syst. Appl. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Compliance with stringent legal standards such as GDPR and CCPA is essential, necessitating purpose limitation and data minimization.

“Compliance with stringent legal standards within applicable jurisdictions, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), is essential, necessitating purpose limitation and data minimization [230], [231], [232].”

• cites [230] Rashkin, 2023, Measuring attribution in natural language generation models, Comput. Linguist. sourceNot checked yet

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• cites [231] Pandey, 2023, Natural Language Generation Using Sequential Models: A Survey, Neural Process. Lett. sourceNot checked yet

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• cites [232] Khan, J.Y., and Uddin, G. (2022, January 10–14). Automatic code documentation generation using gpt-3. Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, USA. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Self-supervised learning emphasizes autonomous model training using unlabeled data, reducing manual labeling efforts and model biases.

“This method emphasizes autonomous model training using unlabeled data, reducing manual labeling efforts and model biases [242], [165], [243].”

• cites [242] Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., and Luhman, E. (2024). Video Generation Models as World Simulators, OpenAI.Not checked yet

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• cites [165] Pope, R., Douglas, S., Chowdhery, A., Devlin, J., Bradbury, J., Heek, J., Xiao, K., Agrawal, S., and Dean, J. (2023, January 4–8). Efficiently scaling transformer inference. Proceedings of the 6th Machine Learning and Systems, Miami, FL, USA.Not checked yet

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• cites [243] Google DeepMind (2024). Veo 2: Google’s Advanced Video Generation Model, Google.Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI ethics focuses on fairness, accountability, and societal impact, addresses ethical challenges posed by AI's increasing complexity and potential misalignment with human values, and requires ethical governance frameworks, multidisciplinary collaborations, and technological solutions.

“The field of AI ethics focuses on fairness, accountability, and societal impact, addresses the surge in ethical challenges posed by AI's increasing complexity and potential misalignment with human values, and requires ethical governance frameworks, multidisciplinary collaborations, and technological solutions [214], [235], [15], [236].”

• cites [214] Lin, 2023, Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook, Expert Syst. Appl. sourceNot checked yet

  Not checked in the free run.

• cites [235] Ji, 2023, A systematic review of conversational AI in language education: Focusing on the collaboration with human teachers, J. Res. Technol. Educ. sourceNot checked yet

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• cites [15] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In AI data security, key requirements and challenges include ensuring data confidentiality, adhering to consent norms, and safeguarding against vulnerabilities like membership inference attacks.

“In AI data security, key requirements and challenges include ensuring data confidentiality, adhering to consent norms, and safeguarding against vulnerabilities like membership inference attacks [228], [229].”

• cites [228] Erdem, 2022, Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning, J. Artif. Intell. Res. sourceNot checked yet

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• cites [229] Qian, J., Dong, L., Shen, Y., Wei, F., and Chen, W. (2022). Controllable natural language generation with contrastive prefixes. arXiv. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Issues of data sovereignty and copyright emphasize the need for robust encryption, access control, and continuous security assessments.

“Additionally, issues of data sovereignty and copyright emphasize the need for robust encryption, access control, and continuous security assessments [233], [234].”

• cites [233] Dwivedi, 2023, “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy, Int. J. Inf. Manag. sourceNot checked yet

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• cites [234] Fu, 2022, Learning towards conversational AI: A survey, AI Open sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Meta-learning centers on equipping AI models with ability to rapidly adapt to new tasks and domains using limited data samples.

“Meta-learning, or 'learning to learn', centers on equipping AI models with the ability to rapidly adapt to new tasks and domains using limited data samples [251], [252].”

• cites [251] Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., and Hall, P. (2022). Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, NIST. NIST Special Publication. sourceNot checked yet

  Not checked in the free run.

• cites [252] Guo, W., and Caliskan, A. (2021, January 19–21). Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, Virtual. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Fine-tuning ensures generative models are more effectively adapted to specific user needs or domain requirements, making them more versatile and applicable across various contexts.

“This technique ensures that generative models are more effectively adapted to specific user needs or domain requirements, making them more versatile and applicable across various contexts.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

Ensuring compliance with privacy laws and implementing secure data handling practices are crucial, demonstrating continuous need for robust privacy preservation mechanisms.

“Ensuring compliance with privacy laws and implementing secure data handling practices are crucial in this context, demonstrating the continuous need for robust privacy preservation mechanisms.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

At their core, MoE models utilize a sparsity-driven architecture by replacing dense layers with sparse MoE layers comprising multiple expert networks, where each expert is dedicated to a specific subset of training data or task, and a trainable gating mechanism dynamically allocates input tokens to these experts, optimizing computational resources and adapting to task complexity.

“At their core, MoE models utilize a sparsity-driven architecture by replacing dense layers with sparse MoE layers comprising multiple expert networks, where each expert is dedicated to a specific subset of the training data or task, and a trainable gating mechanism dynamically allocates input tokens to these experts, thereby optimizing computational resources and effectively adapting to the task's”

• cites [94] Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., and Hinton, G. (2017). Regularizing neural networks by penalizing confident output distributions. arXiv.Not checked yet

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• cites [21] DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., and Zhang, C. (2024). DeepSeek-V3 Technical Report. arXiv.Not checked yet

  Not checked in the free run.

• cites [110] Vats, A., Raja, R., Jain, V., and Chadha, A. (2024). The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints, 2024080583. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Interactive and Cooperative AI aims to enhance capabilities of AI models to collaborate effectively with humans in complex tasks.

“This subdomain aims to enhance the capabilities of AI models to collaborate effectively with humans in complex tasks [274], [35].”

• cites [274] Misra, I., and Maaten, L.v.d. (2020, January 14–19). Self-supervised learning of pretext-invariant representations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual. sourceNot checked yet

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• cites [35] Maddigan, 2023, Chat2vis: Generating data visualisations via natural language using chatgpt, codex and gpt-3 large language models, IEEE Access sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Sparsely-Gated Mixture-of-Experts Layer comprises numerous simple feed-forward expert networks and a trainable gating network responsible for expert selection, facilitating dynamic and sparse activation of experts for each input instance while maintaining high computational efficiency.

“The Sparsely-Gated Mixture-of-Experts Layer, a key component of these models, comprises numerous simple feed-forward expert networks and a trainable gating network responsible for expert selection, which can facilitate the dynamic and sparse activation of experts for each input instance, maintaining high computational efficiency [293], [294], [110].”

• cites [293] Nguyen, M., Kishan, K., Nguyen, T., Chadha, A., and Vu, T. (2023, January 18–22). Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response Planning. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Turin, Italy. sourceNot checked yet

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• cites [294] Engelbach, M., Klau, D., Scheerer, F., Drawehn, J., and Kintz, M. (2023). Fine-tuning and aligning question answering models for complex information extraction tasks. arXiv. sourceNot checked yet

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• cites [110] Vats, A., Raja, R., Jain, V., and Chadha, A. (2024). The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints, 2024080583. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

A distinguishing characteristic of MoE models is flexibility in managing large datasets, capable of amplifying model capacity by over a thousand times while only experiencing minor reductions in computational efficiency.

“A distinguishing characteristic of MoE models is their flexibility in managing large datasets, capable of amplifying model capacity by over a thousand times while only experiencing minor reductions in computational efficiency [289], [292].”

• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

• cites [292] Kuang, W., Qian, B., Li, Z., Chen, D., Gao, D., Pan, X., Xie, Y., Li, Y., Ding, B., and Zhou, J. (2023). Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning. arXiv. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

Pursuit of AGI is a long-term aspiration, continually pushing boundaries of AI research and development.

“The pursuit of AGI is a long-term aspiration, continually pushing the boundaries of AI research and development.”

May combine two claims.

Experimental evidence suggests that routers learn to route inputs according to data clusters, demonstrating their potential in real-world applications.

“Experimental evidence suggests that routers learn to route inputs according to data clusters, demonstrating their potential in real-world applications [295], [289].”

• cites [295] Nguyen, T.T., Wilson, C., and Dalins, J. (2023). Fine-tuning llama 2 large language models for detecting online sexual predatory chats and abusive texts. arXiv. sourceNot checked yet

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• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

As we progress towards developing superintelligent systems, it becomes crucial to establish rigorous safety protocols and control mechanisms.

“As we progress towards developing superintelligent systems, it becomes crucial to establish rigorous safety protocols and control mechanisms [11].”

• cites [11] Morris, M.R., Sohl-dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., and Legg, S. (2023). Levels of AGI: Operationalizing Progress on the Path to AGI. arXiv.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Core aspects of Interactive and Cooperative AI involve advancing AI in explainability, understanding human intentions and behavior (theory of mind), and scalable coordination between AI systems and humans.

“Core aspects of this subdomain involve advancing AI in areas such as explainability [278], understanding human intentions and behavior (theory of mind) [279], [280], and scalable coordination between AI systems and humans, a collaborative approach crucial in creating more intuitive and interactive AI systems, capable of assisting and augmenting human capabilities in diverse contexts [281], [35].”

• cites [278] Patel, P., Kumari, N., Singh, M., and Krishnamurthy, B. (2021, January 5–9). Lt-gan: Self-supervised gan with latent transformation detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA. sourceNot checked yet

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• cites [279] Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 12–18). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, Virtual.Not checked yet

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• cites [280] He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 14–19). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Recent developments like Gemini model have set new benchmarks by demonstrating state-of-the-art performance in various multimodal tasks, including natural image, audio, and video understanding, and mathematical reasoning.

“Recent developments like Gemini model have set new benchmarks by demonstrating state-of-the-art performance in various multimodal tasks, including natural image, audio, and video understanding, and mathematical reasoning [112].”

• cites [112] Tian, Y., Xia, F., and Song, Y. (2024, January 11–16). Dialogue summarization with mixture of experts based on large language models. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

AGI involves not just replicating human intelligence but also crafting systems that can autonomously perform a variety of tasks, demonstrating adaptability and learning capabilities akin to humans.

“AGI is not just about replicating human intelligence, but also involves crafting systems that can autonomously perform a variety of tasks, demonstrating adaptability and learning capabilities akin to those of humans [282], [283].”

• cites [282] Pang, Y., Wang, W., Tay, F.E., Liu, W., Tian, Y., and Yuan, L. (2022, January 23–27). Masked autoencoders for point cloud self-supervised learning. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel. sourceNot checked yet

  Not checked in the free run.

• cites [283] Hospedales, 2021, Meta-learning in neural networks: A survey, IEEE Trans. Pattern Anal. Mach. Intell.Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Key areas of concern in AGI containment include mitigating representational biases, addressing distribution shifts, and correcting spurious correlations within AI models.

“Key areas of concern include mitigating representational biases, addressing distribution shifts, and correcting spurious correlations within AI models [11], [284].”

• cites [11] Morris, M.R., Sohl-dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., and Legg, S. (2023). Levels of AGI: Operationalizing Progress on the Path to AGI. arXiv.Not checked yet

  Not checked in the free run.

• cites [284] Vilalta, 2002, A perspective view and survey of meta-learning, Artif. Intell. Rev. sourceNot checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

MoE model architecture represents a pioneering advancement in transformer-based language models, offering unparalleled scalability and efficiency.

“The MoE model architecture represents a pioneering advancement in transformer-based language models, offering unparalleled scalability and efficiency (Fig. 4).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

This trend focuses on developing AI systems that can work alongside humans, improving user experience and efficiency across applications including productivity and healthcare.

“This trend focuses on developing AI systems that can work alongside humans, thereby improving user experience and efficiency across various applications, including productivity and healthcare [275], [276], [277].”

• cites [275] Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L. (November, January 27). S4l: Self-supervised semi-supervised learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.Not checked yet

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• cites [276] Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N. (2019, January 16–20). Self-supervised gans via auxiliary rotation loss. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

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• cites [277] Jenni, S., and Favaro, P. (2018, January 18–22). Self-supervised feature learning by learning to spot artifacts. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Core concept and structure of MoE models lie in dynamic routing and specialization capabilities, offering promising avenues for scaling up neural networks and enhancing efficiency and adaptability in various tasks, but robustness of router must be protected against adversarial attacks.

“The core concept and structure of MoE models lie in their dynamic routing and specialization capabilities, offering promising avenues for scaling up neural networks and enhancing their efficiency and adaptability in various tasks, but the robustness of the router must be protected against adversarial attacks [289], [298].”

• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

• cites [298] Gabriel, I., and Ghazavi, V. (2021). The challenge of value alignment: From fairer algorithms to AI safety. arXiv. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AGI Safety and Containment acknowledges potential risks associated with highly advanced AI systems, focused on ensuring these systems are technically proficient and ethically aligned with human values and societal norms.

“AGI Safety and Containment acknowledges the potential risks associated with highly advanced AI systems, focused on ensuring that these advanced systems are not only technically proficient but also ethically aligned with human values and societal norms [15], [32], [11].”

• cites [15] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.Not checked yet

  Not checked in the free run.

• cites [32] Kambhampati, 2024, Can large language models reason and plan?, Ann. N. Y. Acad. Sci. sourceNot checked yet

  Not checked in the free run.

• cites [11] Morris, M.R., Sohl-dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., and Legg, S. (2023). Levels of AGI: Operationalizing Progress on the Path to AGI. arXiv.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Structure of MoE involves alternating transformer layers with router layers containing gating networks for expert routing, leading to architecture allowing significant parameter scaling and advanced specialization in problem-solving.

“The structure of MoE involves alternating transformer layers with router layers containing gating networks for expert routing, leading to an architecture that allows significant parameter scaling and advanced specialization in problem-solving [291], [21].”

• cites [291] Wei, J., Bosma, M., Zhao, V.Y., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., and Le, Q.V. (2021). Finetuned language models are zero-shot learners. arXiv.Not checked yet

  Not checked in the free run.

• cites [21] DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., and Zhang, C. (2024). DeepSeek-V3 Technical Report. arXiv.Not checked yet

  Not checked in the free run.

May combine two claims.

MoE models face challenges in fine-tuning and require substantial memory for inference due to necessity of loading all experts into VRAM.

“However, they face challenges in fine-tuning and require substantial memory for inference due to the necessity of loading all experts into Video Random Access Memory (VRAM) [289], [290], [110].”

• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

• cites [290] Behnia, R., Ebrahimi, M.R., Pacheco, J., and Padmanabhan, B. (December, January 28). EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy. Proceedings of the 2022 IEEE International Conference on Data Mining Workshops (ICDMW), Orlando, FL, USA. sourceNot checked yet

  Not checked in the free run.

• cites [110] Vats, A., Raja, R., Jain, V., and Chadha, A. (2024). The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints, 2024080583. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

MoE models represent a significant innovation in neural network design, offering enhanced scalability and efficiency in training and inference.

“MoE models represent a significant innovation in neural network design, offering enhanced scalability and efficiency in training and inference [287], [288], [110].”

• cites [287] Baik, S., Choi, J., Kim, H., Cho, D., Min, J., and Lee, K.M. (2021, January 11–17). Meta-learning with task-adaptive loss function for few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montréal, BC, Canada. sourceNot checked yet

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• cites [288] Chen, Y., Liu, Z., Xu, H., Darrell, T., and Wang, X. (2021, January 11–17). Meta-baseline: Exploring simple meta-learning for few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montréal, BC, Canada. sourceNot checked yet

  Not checked in the free run.

• cites [110] Vats, A., Raja, R., Jain, V., and Chadha, A. (2024). The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints, 2024080583. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

AGI represents visionary goal of crafting AI systems that emulate comprehensive and multifaceted aspects of human cognition, focused on developing AI with capability for holistic understanding and complex reasoning closely aligning with depth and breadth of human cognitive abilities.

“AGI, representing the visionary goal of crafting AI systems that emulate the comprehensive and multifaceted aspects of human cognition, is a subdomain focused on developing AI with the capability for holistic understanding and complex reasoning that closely aligns with the depth and breadth of human cognitive abilities [282], [283], [32].”

• cites [282] Pang, Y., Wang, W., Tay, F.E., Liu, W., Tian, Y., and Yuan, L. (2022, January 23–27). Masked autoencoders for point cloud self-supervised learning. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel. sourceNot checked yet

  Not checked in the free run.

• cites [283] Hospedales, 2021, Meta-learning in neural networks: A survey, IEEE Trans. Pattern Anal. Mach. Intell.Not checked yet

  Not checked in the free run.

• cites [32] Kambhampati, 2024, Can large language models reason and plan?, Ann. N. Y. Acad. Sci. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Despite advancements, multimodal learning still confronts challenges such as refining architectures to handle diverse data types, developing comprehensive datasets, and establishing benchmarks for evaluating complex systems.

“Despite the advancements, the field of multimodal learning still confronts ongoing challenges, such as refining the architectures to handle diverse data types more effectively [269], [270], developing comprehensive datasets that accurately represent multifaceted information [269], [271], and establishing benchmarks for evaluating the performance of these complex systems [272], [273].”

• cites [269] Kroll, J.A. (2021, January 3–10). Outlining traceability: A principle for operationalizing accountability in computing systems. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual. sourceNot checked yet

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• cites [270] Oseni, A., Moustafa, N., Janicke, H., Liu, P., Tari, Z., and Vasilakos, A. (2021). Security and privacy for artificial intelligence: Opportunities and challenges. arXiv.Not checked yet

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• cites [271] Stahl, 2018, Ethics and privacy in AI and big data: Implementing responsible research and innovation, IEEE Secur. Priv. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Gemini's inherently multimodal design exemplifies seamless integration and operation across different information types.

“Gemini's inherently multimodal design exemplifies the seamless integration and operation across different information types [112].”

• cites [112] Tian, Y., Xia, F., and Song, Y. (2024, January 11–16). Dialogue summarization with mixture of experts based on large language models. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand. sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Objective of AGI containment is to prevent unintended societal consequences by aligning AI development with responsible and ethical standards.

“The objective is to prevent unintended societal consequences by aligning AI development with responsible and ethical standards.”

May combine two claims.

Expert capacity management strategies regulate the processing abilities of individual experts by setting thresholds on the number of tokens each can handle, effectively averting bottlenecks and ensuring more efficient model operation, leading to improved training processes and heightened performance during complex computational tasks.

“Concurrently, the integration of expert capacity management strategies, emerges as a crucial approach in MoE models to regulate the processing abilities of individual experts by setting thresholds on the number of tokens each can handle, effectively averting bottlenecks and ensuring a more efficient and streamlined model operation, leading to improved training processes and heightened performance”

• cites [293] Nguyen, M., Kishan, K., Nguyen, T., Chadha, A., and Vu, T. (2023, January 18–22). Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response Planning. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Turin, Italy. sourceNot checked yet

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• cites [303] Rahate, 2022, Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions, Inf. Fusion sourceNot checked yet

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• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

The integration of MoE and the pursuit of AGI are opening new pathways for research in multimodal models, crucial for enhancing the ability to process and synthesize information from multiple modalities.

“The integration of MoE and the pursuit of AGI are opening new pathways for research in multimodal models. These developments are crucial for enhancing the ability to process and synthesize information from multiple modalities, a key aspect for both specialized and generalized AI systems.”

May combine two claims.

MoE models are versatile in applications like multilingual tasks and coding, and demonstrated impressive capabilities in handling complex tasks due to their ensemble-like structure within a single framework.

“MoE models, versatile in applications like multilingual tasks and coding, demonstrated impressive capabilities in handling complex tasks due to their ensemblelike structure within a single framework [304], [305], [306].”

• cites [304] Che, L., Wang, J., Zhou, Y., and Ma, F. (2023). Multimodal federated learning: A survey. Sensors, 23. sourceNot checked yet

  Not checked in the free run.

• cites [305] Liang, P.P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., Chen, L., Wu, P., Lee, M.A., and Zhu, Y. (2021). Multibench: Multiscale benchmarks for multimodal representation learning. arXiv.Not checked yet

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• cites [306] Li, Y., Zhang, G., Ma, Y., Yuan, R., Zhu, K., Guo, H., Liang, Y., Liu, J., Yang, J., and Wu, S. (2024). OmniBench: Towards The Future of Universal Omni-Language Models. arXiv.Not checked yet

  Not checked in the free run.

May combine two claims.

MoE models face hurdles in fine-tuning and demand considerable VRAM for inference because all experts must be loaded.

“yet they face hurdles in fine-tuning and demand considerable VRAM for inference, owing to the requirement of loading all experts [289], [290], [110].”

• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

• cites [290] Behnia, R., Ebrahimi, M.R., Pacheco, J., and Padmanabhan, B. (December, January 28). EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy. Proceedings of the 2022 IEEE International Conference on Data Mining Workshops (ICDMW), Orlando, FL, USA. sourceNot checked yet

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• cites [110] Vats, A., Raja, R., Jain, V., and Chadha, A. (2024). The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints, 2024080583. sourceNot checked yet

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May combine two claims.

DeepSpeed-MoE introduces advanced parallelism modes like data parallelism, tensor-slicing for non-expert parameters, and expert parallelism for expert parameters, enhancing model efficiency, optimizing latency and throughput in MoE model inference, and offering scalable solutions in production environments using multiple GPU devices.

“DeepSpeed-MoE, for instance, introduces advanced parallelism modes like data parallelism, tensor-slicing for non-expert parameters, and expert parallelism for expert parameters, enhancing model efficiency, as their approach optimizes both latency and throughput in MoE model inference, offering scalable solutions in production environments using multiple Graphics Processing Unit (GPU) devices [287]”

• cites [287] Baik, S., Choi, J., Kim, H., Cho, D., Min, J., and Lee, K.M. (2021, January 11–17). Meta-learning with task-adaptive loss function for few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montréal, BC, Canada. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

While supervised learning requires adaptation to fit the MoE framework, it remains relevant for multimodal AI models that depend on labeled data; however, with the shift towards more autonomous learning methods in AGI, the dependence on extensive labeled datasets may diminish.

“While supervised learning requires adaptation to fit the MoE framework, it remains relevant for multimodal AI models that depend on labeled data. However, with the shift towards more autonomous learning methods in AGI, the dependence on extensive labeled datasets typically associated with supervised learning may diminish, leading to its potential decrease in significance.”

May combine two claims.

DeepSpeedMoE offered new architectural designs and model compression, decreasing MoE model size by approximately 3.7x and optimizing inference to achieve up to 7.3x better latency and cost efficiency.

“Innovations like DeepSpeedMoE [287] offered new architectural designs and model compression, decreasing the MoE model size by approximately 3.7x and optimizing inference to achieve up to 7.3x better latency and cost efficiency.”

• cites [287] Baik, S., Choi, J., Kim, H., Cho, D., Min, J., and Lee, K.M. (2021, January 11–17). Meta-learning with task-adaptive loss function for few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montréal, BC, Canada. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Transformers continue to be relevant for handling complex input sequences, but the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures, requiring transformers to evolve and integrate with these advanced systems.

“These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

MoE models support the relevance of NLU by enhancing its precision and depth through their ability to handle large, diverse datasets; in multimodal AI, NLU remains a critical component; with AGI's progress, NLU is expected to undergo significant expansion.

“MoE models support the relevance of NLU by enhancing its precision and depth through their ability to handle large, diverse datasets. In multimodal AI, NLU remains a critical component for comprehending language in diverse data formats. With AGI's progress, NLU is expected to undergo significant expansion, moving towards more advanced, human-like comprehension and interpretation capabilities.”

May combine two claims.

Router Zloss regularization techniques address expert imbalance in MoE models by fine-tuning the gating mechanism, ensuring a more equitable workload distribution across experts and fostering a stable training environment, thereby enhancing model performance and reducing training time and computational overhead.

“Developments in router Zloss regularization techniques plays a crucial role in addressing expert imbalance in MoE models by fine-tuning the gating mechanism, ensuring a more equitable workload distribution across experts and fostering a stable training environment, thereby enhancing model performance and reducing training time and computational overhead [301], [302].”

• cites [301] Bayoudh, 2021, A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets, Vis. Comput. sourceNot checked yet

  Not checked in the free run.

• cites [302] Hu, P., Zhen, L., Peng, D., and Liu, P. (2019, January 21–25). Scalable deep multimodal learning for cross-modal retrieval. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

These developments have marked a crucial shift in the large model landscape from dense to sparse MoE models, expanding the potential applications of AI by training higher-quality models with fewer resources.

“These developments have marked a crucial shift in the large model landscape, from dense to sparse MoE models, expanding the potential applications of AI by training higher-quality models with fewer resources.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

MoE models are at the forefront of emerging research in multimodality due to their ability to handle diverse data types, and for AGI they will require adjustments to integrate into systems exhibiting general intelligence.

“MoE models are at the forefront of emerging research in multimodality due to their ability to handle diverse data types. For AGI, these models will require adjustments to effectively integrate into systems exhibiting general intelligence, especially in areas beyond their initial specialization.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Transfer learning remains important in the MoE framework for leveraging knowledge across different experts; in multimodal contexts, it is becoming increasingly crucial; with AGI, it is expected to undergo significant changes.

“It remains important in the MoE framework for leveraging knowledge across different experts. In multimodal contexts, transfer learning is becoming increasingly crucial as it facilitates the transfer of learning between different modalities. With the evolution of AGI, this technique is expected to undergo significant changes to cater to broader and more generalized knowledge applications.”

May combine two claims.

Models like Mixtral and Switch Transformer, with over 1.6 trillion parameters, achieved computational efficiency equivalent to a 10 billion-parameter dense model, because they benefited from the sublinear scaling of MoE compute versus model size, leading to substantial accuracy gains within fixed compute budgets.

“Notably, models like Mixtral and Switch Transformer, with over 1.6 trillion parameters, achieved computational efficiency equivalent to a 10 billion-parameter dense model, because they benefited from the sublinear scaling of MoE compute versus model size, leading to substantial accuracy gains within fixed compute budgets [21], [289], [287], [110].”

• cites [21] DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., and Zhang, C. (2024). DeepSeek-V3 Technical Report. arXiv.Not checked yet

  Not checked in the free run.

• cites [289] Jamal, M.A., and Qi, G.J. (2019, January 16–20). Task agnostic meta-learning for few-shot learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. sourceNot checked yet

  Not checked in the free run.

• cites [287] Baik, S., Choi, J., Kim, H., Cho, D., Min, J., and Lee, K.M. (2021, January 11–17). Meta-learning with task-adaptive loss function for few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montréal, BC, Canada. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

These innovations open new directions in AI, shifting from dense to sparse MoE models, where training and deploying higher-quality models with fewer resources become more widely achievable.

“These innovations open new directions in AI, shifting from dense to sparse MoE models, where training and deploying higher-quality models with fewer resources become more widely achievable.”

May combine two claims.

DeepSpeed-MoE included model compression techniques, reducing model size by up to 3.7x while maintaining accuracy, and an end-to-end MoE training and inference solution, part of the DeepSpeed library, which was instrumental in serving large-scale MoE models with enhanced speed and cost efficiency.

“Moreover, DeepSpeed-MoE included model compression techniques, reducing model size by up to 3.7x while maintaining accuracy, and an end-to-end MoE training and inference solution, part of the DeepSpeed library, which was instrumental in serving large-scale MoE models with enhanced speed and costefficiency [287].”

• cites [287] Baik, S., Choi, J., Kim, H., Cho, D., Min, J., and Lee, K.M. (2021, January 11–17). Meta-learning with task-adaptive loss function for few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montréal, BC, Canada. sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim. May combine two claims.

Reinforcement learning continues to play a significant role in optimizing MoE model structures; in multimodality, it necessitates a strategic shift to manage complex interactions between modalities; in AGI, it is emerging as a crucial area for autonomous systems.

“This technique continues to play a significant role in optimizing MoE model structures. In the realm of multimodality, it necessitates a strategic shift to effectively manage complex interactions between different modalities. As for AGI, reinforcement learning is emerging as a crucial area, particularly in the development of autonomous systems that learn from their environment.”

May combine two claims.

Lina effectively tackled the all-to-all communication bottleneck by enhancing tensor partitioning, improving all-to-all communication and training step time, and optimizing resource scheduling during inference, leading to a reduction in training step time by up to 1.73 times and lowering the 95th percentile inference time by an average of 1.63 times compared to existing systems.

“The progression in distributed MoE training and inference, notably with innovations like Lina [299], has effectively tackled the all-to-all communication bottleneck by enhancing tensor partitioning, which not only improves all-to-all communication and training step time, but also optimizes resource scheduling during inference, leading to a substantial reduction in training step time by up to 1.73”

• cites [299] Nyholm, S. (2023). Responsibility gaps, value alignment, and meaningful human control over artificial intelligence. Risk and Responsibility in Context, Routledge. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

In the MoE architecture, unsupervised learning methods may need adjustments, particularly in managing dynamic task allocation; it remains crucial for understanding unlabeled data across various modalities; in AGI, unsupervised learning is expected to evolve beyond traditional techniques.

“In the MoE architecture, unsupervised learning methods may need adjustments, particularly in managing dynamic task allocation. It remains crucial for understanding unlabeled data across various modalities. In AGI, unsupervised learning is expected to evolve beyond traditional techniques, focusing on more advanced self-discovery and intrinsic learning mechanisms.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Advanced generative AI models, including MoE architectures and AGI systems, require significant processing power.

“Advanced generative AI models, including MoE architectures and AGI systems, require significant processing power [321].”

• cites [321] Akyürek, E., Damani, M., Qiu, L., Guo, H., Kim, Y., and Andreas, J. (2024). The Surprising Effectiveness of Test-Time Training for Abstract Reasoning. arXiv.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In finance, the use of AI for fraud detection and algorithmic trading underlines its efficiency and accuracy, while raising ethical concerns, particularly in automated decision-making processes, which may lack transparency and accountability.

“The use of AI for fraud detection and algorithmic trading in finance underlines its efficiency and accuracy, while at the same time, it raises ethical concerns, particularly in automated decision-making processes, which may lack transparency and accountability [323].”

• cites [323] Shen, L., Wu, Z., Gong, W., Hao, H., Bai, Y., Wu, H., Wu, X., Bian, J., Xiong, H., and Yu, D. (2022). Se-moe: A scalable and efficient mixture-of-experts distributed training and inference system. arXiv.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The rapid expansion of academic literature across various fields presents a significant challenge for researchers seeking to perform evidence syntheses over the increasingly vast body of available knowledge.

“Thus, the rapid expansion of academic literature across various fields presents a significant challenge for researchers seeking to perform evidence syntheses over the increasingly vast body of available knowledge [328].”

• cites [328] Wang, Y., Mukherjee, S., Liu, X., Gao, J., Awadallah, A.H., and Gao, J. (2022). Adamix: Mixture-of-adapter for parameter-efficient tuning of large language models. arXiv. sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Knowledge has continued to expand and disseminate exponentially.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

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.

“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.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

The safety and robustness of AGI systems in diverse environments also remain a significant research gap.

“The safety and robustness of AGI systems in diverse environments also remain a significant research gap.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Several open questions and research gaps remain, including ensuring the ethical alignment of advanced AI systems with human values and societal norms, a challenge compounded by their increasing autonomy.

“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.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Our analysis highlights a crucial paradigm shift, driven by innovations such as MoE, multimodal learning, and the pursuit of AGI.

“Our analysis highlights a crucial paradigm shift, driven by innovations such as MoE, multimodal learning, and the pursuit of AGI.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In healthcare, generative AI facilitates advancements in diagnostic imaging and personalized medicine, but also raises significant concerns regarding data privacy and the potential for misuse of sensitive health information.

“In healthcare, generative AI facilitates advancements in diagnostic imaging and personalized medicine, but also raises significant concerns regarding data privacy and the potential for misuse of sensitive health information [322].”

• cites [322] Rajbhandari, S., Li, C., Yao, Z., Zhang, M., Aminabadi, R.Y., Awan, A.A., Rasley, J., and He, Y. (2022, January 17–23). Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale. Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Unvetted preprints possibly perpetuating biases or errors within the scientific community and beyond.

“The potential consequences are significant, with unvetted preprints possibly perpetuating biases or errors within the scientific community and beyond.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

The situation is compounded by a finite pool of qualified reviewers, leading to delays, potential biases, and a burden on the scholarly community.

“This situation is compounded by a finite pool of qualified reviewers, leading to delays, potential biases, and a burden on the scholarly community.”

• AssumptionRests on an assumption: There is a finite pool of qualified reviewers.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

Despite generative AI's accuracy in interpreting policies and procedures, its impact is limited in replacing human judgment, especially in legal and political contexts where decision-makers might selectively use AIGC, leading to biased outcomes.

“Despite generative AI's accuracy in interpreting policies and procedures, its impact is limited in replacing human judgment. This is especially true in legal and political contexts where decision-makers might selectively use AIGC, leading to biased outcomes.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

The inability to thoroughly assess preprint research undermines the foundation of scientific reliability and validity.

“This is concerning, as the inability to thoroughly assess preprint research undermines the foundation of scientific reliability and validity.”

• AssumptionRests on an assumption: Practical constraints such as lack of technical expertise, computational resources, or access to proprietary datasets hinder rigorous evaluation of preprint research.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

A critical challenge in training and deploying large-scale AI models, particularly in multimodal and AGI systems executed on GPUs, lies in the substantial GPU and VRAM requirements.

“A critical challenge in training and deploying large-scale AI models, particularly in multimodal and AGI systems executed on GPUs, lies in the substantial GPU and VRAM requirements.”

• AssumptionRests on an assumption: VRAM often cannot be expanded easily on many platforms.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

The absence of established retraction mechanisms for preprints, akin to those for published articles, exacerbates the risk of persistent dissemination of flawed research.

“The absence of established retraction mechanisms for preprints, akin to those for published articles, exacerbates the risk of persistent dissemination of flawed research.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

In education, generative AI's role in creating personalized learning experiences offers immense benefits in educational accessibility and tailored instruction, but poses challenges in equitable access to technology, potential biases in AIGC, and could reduce demand for human educators.

“Generative AI's role in creating personalized learning experiences offers immense benefits in terms of educational accessibility and tailored instruction. However, it poses challenges in equitable access to technology, potential biases in AI-Generated Content (AIGC), and could reduce demand for human educators.”