limitations of current AI applications and the need for a more collaborative and reliable knowledge management system is essential. The current state of AI applications rely heavily on the data they have been trained on, which is often sourced from publicly available datasets or proprietary sources. This can limit the depth and breadth of knowledge accessible to these AI systems. Additionally, the lack of collaboration and verification mechanisms makes it difficult to combine outside experts’ non-published data.
While AI has made significant advancements in various domains, including natural language processing and image generation, there are still challenges when it comes to integrating expert knowledge and verifying information.
AI systems need to integrate more wide-ranging sources of information, both public and private, to provide more comprehensive and reliable knowledge delivery. This would indeed be a step towards realizing the full potential of AI for research, the environment, and society as a whole.
Combining different sources like Google search results, scholarly databases, Wikipedia, YouTube, Private Expert Silos, and various document types can potentially offer a wider range of information. This diverse set of sources can help increase the breadth and depth of knowledge available to the AI system.
Integrating data from private silos and collaborative expert groups with the permission of the owners, could further enhance the system’s capabilities by incorporating specialized or domain-specific knowledge that may not be publicly or otherwise available. Collaborative groups and organizations, sharing their private and public information silos, can contribute to a more collaborative and dynamic knowledge ecosystem.
The use of probabilistic algorithms and linguistic models can aid in information retrieval, data linking, and analysis, allowing the AI system to process and understand the integrated knowledge effectively.
Creating a truly collaborative and reliable knowledge
management system requires the participation and cooperation of various stakeholders, including researchers, experts,
organizations, and the public. Establishing standards, protocols, and mechanisms for data sharing, verification, and ethical guidelines can contribute to the development of such a system.
ChatUnified foresees its release 2 as the new architecture in Data Warehousing.
In summary, while the current state of AI applications may have limitations in terms of collaboration, reliability, and access to expert knowledge, efforts are underway to address these challenges and create a more collaborative and trustworthy knowledge management system. By fostering open data sharing, verification mechanisms, and multidisciplinary collaborations, we can pave the way for AI systems that benefit research, the environment, and society as a whole.
ChatUnified delivers this all. See the video and try it for yourself by May 26th.