Information Knowledge Management (IKM) plays a crucial role in enhancing the credibility, expertise, accessibility, referencing, & proactivity of Artificial Intelligence (AI) systems. By incorporating effective IKM practices, AI can leverage reliable information and collaborate with experts, resulting in more accurate and informed decision-making.

Information Knowledge Management is indeed an important component in the development and application of artificial intelligence. AI systems rely on data and information to learn, make decisions, and provide accurate responses. Let’s break down the points you mentioned:

Credibility: Effective IKM ensures that the information used by AI is credible and reliable. By establishing a framework for verifying sources, validating data, and assessing the quality of information, IKM helps maintain the credibility of AI systems.

Expert Collaboration: IKM facilitates collaboration between AI developers and domain experts. Experts provide valuable insights, domain-specific knowledge, and feedback to improve the AI algorithms and models. This collaboration ensures that AI systems are better equipped to handle complex tasks and generate accurate results.

Quick Access: IKM enables quick and efficient access to relevant information. By organizing and indexing vast amounts of data, IKM helps AI systems retrieve information in a timely manner. This is particularly important in real-time applications where AI needs to process and respond to data rapidly.

Fully Referenced: IKM ensures that AI systems have access to a comprehensive and well-referenced knowledge base. By maintaining a repository of relevant information, including citations, sources, and references, IKM enhances the transparency and traceability of AI-generated insights and recommendations.

Proactive Information Pipeline: IKM establishes an active information pipeline, constantly updating and expanding the knowledge available to AI systems. It facilitates the continuous acquisition of new data, integration of emerging research, and adaptation to evolving knowledge landscapes. This proactive approach helps AI systems stay relevant and up-to-date.

Contact AIUnited for information on their AI IKM Application at 847-440-4439

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The AI Collective Collaboration Repository, developed by AIUnited, aims to advance AI by providing a comprehensive platform for information knowledge management and collaboration. Here are the key features and capabilities of the repository:

Real-Time Retrieval and Storage: The repository enables real-time retrieval and storage of information from various sources, including general internet search results, scholarly resources, Wikipedia, YouTube, Twitter, TikTok, Instagram, documents (such as PDFs, Word files, Excel spreadsheets), personal private silos, and group silos (both public and private).

Repository Types: The repository offers different types of repositories to cater to various needs. These include the AIUnited Central Collaborative repository, which serves as a central hub for collective collaboration. Additionally, there are personal repositories (public and private), group repositories (public and private), and third-party repositories (public and private).

Match Search Requests: The repository employs probabilistic matching algorithms to match search requests against the stored information from all the sources mentioned in section 1. This allows users to efficiently find relevant information.

Customizable Search Results: Users can specify the desired number of returned results and rankings for their search queries. They have the option to update either the requested results or retrieve all possible research results for specific data sources.

Data Source Security: All non-public information sources in the repository adhere to the respective data source’s security and authorization protocols. AIUnited ensures that appropriate security parameters are in place to protect sensitive information.

AIUnited Controls: AIUnited has control over search parameters, data source selection, and search value requests. Users can interact with the repository by typing their queries or using voice commands.

Search Result Reports: AIUnited generates comprehensive reports that include all the search results found across the various sources mentioned in section 1. The report provides supporting links, documents, and source locations. Users can easily copy or download the report by clicking on an icon.

By offering these features, the AI Collective Collaboration Repository aims to facilitate efficient information retrieval, collaboration, and knowledge management in the field of AI and beyond.

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AIUnited 847-440-4439

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AIUnited, founded by Steven Meister, believes that Information Knowledge Management Algorithms & applications (IKM) will play a crucial role in creating successful AI & AI digital agents. We believe our IKM and AI models will enhance delivery time, accuracy, and provide a comprehensive knowledge base for the field of artificial intelligence.

Bill Gates, a prominent figure in the technology industry, has expressed his belief that the company that develops a personal digital agent will be the winner in the field of AI.

We disagree with the notion that the need for platforms like Google will become obsolete in the context of AI, information knowledge management, and personal digital agents. Meister believes that these systems will still require access to vast amounts of historical, current, and future data, which are typically collected and stored by platforms like Google.

According to us, the work currently performed by platforms such as Google and Edge in gathering and organizing information, data, and metadata will remain crucial in enabling AI systems to function effectively. These platforms play a significant role in indexing and organizing the vast amount of information available on the internet, making it easily accessible for various applications.

In our perspective, without platforms like Google, alternative methods would need to be developed to gather, organize, and store data comprehensively. The absence of such platforms would likely require a distributed effort by various entities to ensure that the necessary data is available for AI systems, information management, and personal digital agents to utilize effectively.

AIUnited and Steven Meister, state it is worth noting that the development of effective AI digital agents requires a combination of various technologies and approaches, including natural language processing, machine learning, deep learning, and information knowledge management (a key prerequisite). Building a successful digital agent involves not only the underlying algorithms and models but also data availability, user experience, and the ability to adapt and learn from user interactions.

The field of AI is rapidly evolving, and many companies and researchers are actively working on advancing the capabilities of AI systems. It will be interesting to see how the industry progresses and which companies or approaches ultimately achieve success in creating effective personal digital agents.

AIUnited feels Collaboration between IKM and AI models should lead to improved performance and a more extensive knowledge base. However, the success of AIUnited or any other company in this domain would depend on a range of factors, including the quality of their algorithms, the availability of relevant data, user experience, and the ability to adapt and learn from user interactions, both individually and collaboratively. AIUnited and Steven Meister feels we are far along in this delivery.

https://www.goldmansachs.com/intelligence/pages/bill-gates-on-the-ai-revolution.html

Steven Meister and AIUnited 847-440-4439

Protecting customer and company data is crucial in today’s digital landscape. Encryption plays a vital role in ensuring data security, particularly end-to-end encryption. By encrypting data throughout its entire journey, from the sender to the recipient, you can prevent unauthorized access and make the data useless to anyone who doesn’t possess the decryption key.

While encryption won’t directly prevent ransomware attacks or system takeovers, it can render the stolen data useless to the attackers. This is because encrypted data requires the decryption key to be accessed and utilized. By implementing strong encryption measures, you can mitigate the risk of exposing sensitive information to unauthorized individuals, thereby reducing the chances of identity theft, fraud, and other related issues for your customers.

In addition to encryption, maintaining regular data backups is a crucial aspect of data protection. By backing up your data frequently, you ensure that you have copies of important information stored separately from your primary systems. In the event of a ransomware attack or system failure, having recent backups allows for a faster recovery process. The frequency of backups should be determined based on the scale of your data and the availability requirements of your systems. It’s important to strike a balance between backup frequency and the potential impact on system performance.

By combining encryption and regular data backups, you can significantly enhance your ability to recover from a ransomware catastrophe and minimize the potential damage caused by data breaches. However, it’s also important to implement robust cybersecurity measures, such as network monitoring, intrusion detection systems, employee training, and regular software patching, to reduce the risk of such incidents occurring in the first place.

Contact BDR for end to end encryption, CCPA/CPRA/GDPR & HIPAA (21^st Century Cures Act / FHIR) deliverables. 847-440-4439 https://www.bbc.com/news/technology-65669839  https://youtu.be/GIZiVElQ_VY https://youtu.be/845PxQ4tlhA

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.