Harmonizing Heterogeneity: Unified Embedding Spaces for Generalist AI
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| Auteurs principaux: | , |
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| Format: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902049735573504 |
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| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | The pursuit of Generalist Artificial Intelligence (AI) necessitates systems capable of processing, understanding, and reasoning across a multitude of data modalities and task domains. A significant impediment to achieving this goal is the inherent heterogeneity of real-world data, spanning text, images, audio, video, sensor readings, and structured information, each traditionally requiring specialized models and distinct representation spaces. This paper posits that the development of unified embedding spaces offers a foundational solution to harmonize this pervasive heterogeneity. We propose a conceptual framework for constructing such spaces, enabling diverse data types to be projected into a common, semantically rich vector space where their interrelationships can be efficiently computed and leveraged. This approach facilitates seamless cross-modal understanding, enhances transfer learning capabilities across varied tasks and domains, and fosters the emergence of genuinely generalist AI agents. We delve into the methodological underpinnings, including advanced contrastive learning techniques, multimodal fusion architectures, and large-scale self-supervised pre-training strategies. Furthermore, we discuss the anticipated benefits, such as improved zero-shot and few-shot learning, robust knowledge transfer, and the capacity for novel multimodal reasoning, while also addressing the significant challenges inherent in their practical realization and evaluation. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17793172 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Harmonizing Heterogeneity: Unified Embedding Spaces for Generalist AI Revista, Zen IA, 10 The pursuit of Generalist Artificial Intelligence (AI) necessitates systems capable of processing, understanding, and reasoning across a multitude of data modalities and task domains. A significant impediment to achieving this goal is the inherent heterogeneity of real-world data, spanning text, images, audio, video, sensor readings, and structured information, each traditionally requiring specialized models and distinct representation spaces. This paper posits that the development of unified embedding spaces offers a foundational solution to harmonize this pervasive heterogeneity. We propose a conceptual framework for constructing such spaces, enabling diverse data types to be projected into a common, semantically rich vector space where their interrelationships can be efficiently computed and leveraged. This approach facilitates seamless cross-modal understanding, enhances transfer learning capabilities across varied tasks and domains, and fosters the emergence of genuinely generalist AI agents. We delve into the methodological underpinnings, including advanced contrastive learning techniques, multimodal fusion architectures, and large-scale self-supervised pre-training strategies. Furthermore, we discuss the anticipated benefits, such as improved zero-shot and few-shot learning, robust knowledge transfer, and the capacity for novel multimodal reasoning, while also addressing the significant challenges inherent in their practical realization and evaluation. |
| title | Harmonizing Heterogeneity: Unified Embedding Spaces for Generalist AI |
| url | https://doi.org/10.5281/zenodo.17793172 |