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Draft:XAIgent

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  • Comment: Written inappropriately. Qcne (talk) 16:38, 19 April 2024 (UTC)
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Introduction
Doc Tags (talk) 17:23, 4 June 2024 (UTC) xAIgent, developed by DBI Technologies Inc. [1]is an advanced Artificial Intelligence (AI) and Machine Learning (ML) tailored technology engineered to extract key phrases / metadata from any subject domain text-based content. xAIgent is capable of extracting keyphrases regardless of subject domain. xAIgent is a self contained AI strategy that the extracted keyphrase results are in context of the target content without external bias. xAIgent has been used across various types of text based content: email, HTML, text files, Microsoft Word files, as well, other forms of unstructured content. The xAIgent technology extracts bullet point summaries as lists of key phrases from the target content, where each keyphrase listed has unbiased contextual relevance. xAIgent is used in enterprise applications that include topic curation, search engine optimization (SEO), content management, knowledge management, document management, intelligent search, indexing, categorization, cataloguing, content tagging including Large Language Model (LLM) tuning.

The xAIgent technology [2] is based upon a patent-backed AI/ML strategy that allows for precision keyphrase metadata extraction without traditional processes of manual training of the AI per subject domain. Language support includes: English, French, Japanese, German, Spanish and Korean. Key phrases (metadata) can then used to elevate a content's relevance by adding the metadata keyphrases as attributes of a content's file structure.

History
xAIgent was engineered with the aim of bridging gaps between complex AI technologies and practical, real-world applications. The platform, developed by DBI Technologies Inc, head quartered in Winnipeg, Manitoba by their team comprising AI researchers, data scientists, and software engineers.

Development
The development of xAIgent focused on creating a developer centric platform that could handle large-scale data processing. The core of xAIgent is an integrated patent established machine learning and artificial intelligent framework. The available API exposed for assisting natural language processing (NLP), Search Engine Indexing, document tagging and Large Language Model (LLM) tuning. The platform was designed to be scalable and supports projects ranging from small-scale experiments to enterprise-level deployments across geographic regions. xAigent supported languages include: English, French, German, Japanese, Korean and Spanish.[3]

Technology
The xAIgent technology includes a proprietary mix of deep learning algorithms, predictive analytics, and natural language processing techniques.[4] Released on a cloud computing platform designed for scalable processing and compute power for complex / high volume processes without imposing extensive hardware overhead. xAIgent incorporates explainable AI (XAI) principles to make AI decisions transparent and understandable, which addressed one of the significant challenges for AI adoption.[1]

Applications
xAIgent powers solutions including: Knowledge Management, knowledge curation, content tagging, content management systems, document management systems and search engine optimization. The xAIgent technology is also used in fraud detection, homeland security risk assessment (DSA), search engine optimization (SEO Assistant), document indexing (The Document Index Generator) and Search Engine Indexing.

Impact
The introduction of xAIgent significantly impacted how business and government approached AI and data analytics for broader access to AI technologies. xAIgent enabled enitities of all sizes to innovate and improve their services and operations by expressing keyphrase metadata from stores of unstructured data. xAIgent emphasized explainability and interpretability to foster greater trust in AI solutions, facilitating broader adoption of AI technologies across industries.

User
xAIgent can significantly improve the ability to process and analyze large volumes of unstructured data by automatically identifying and extracting relevant keyphrase metadata in context of the content itself that enables the integrated platform to handle diverse data types effectively, across a range of applications.

Improving Model Accuracy and Performance
The quality of data fed into machine learning models has a profound impact on their accuracy and performance. xAIgent improves the quality of this input data by exposing relevant keyphrase metadata ahead of data inclusion that gives targeted categorization of the Large Language Model data with increased validity of LLM results. (see reference - Learning to Extract Keyphrases from Text below)

Expanding Application Domains
For application domains that rely heavily on the analysis of unstructured data, such as natural language processing (NLP), indexing, automated content curation, research and intelligent search, xAIgent enhances these processes by securing contextually relevant metadata.

Accelerating Development and Deployment
The xAIgent technology demonstrates how to streamline the data preparation phase of AI project workflows, fine tuning Large Language Models (LLM's) for a reduction in time and effort required to develop and deploy authentic AI models. The acceleration for authenticating the data used by LLM's enhanced the overall productivity of users providing faster iteration and innovation.

Enabling More Complex Insights
The process of extracting high-quality keyphrases from complex data, the xAIgent technology extracts deeper, contextual insights. This capability has been particularly valuable in fields including market analysis, social media monitoring, and sentiment analysis, where understanding subtle nuances can be critical in decision-making.

Supporting Explainability and Interpretability
xAIgent plays a role in enhancing the explainability and interpretability of AI models by identifying which data are most influential in model predictions. Transparency has been crucial for applications in sensitive areas like finance and healthcare, where authentic data has become the basis for AI decisions and essential for trust compliance.

== References ==

  1. ^ "XAIgent.net - making content findable".
  2. ^ https://xaigent.net
  3. ^ Mathieu, J. (1999). "Dr" (PDF). Adaptation of a Keyphrase Extractor for Japanese Text: 182-189.
  4. ^ Turney, Peter (2000). "Dr". Learning Algorithms for Keyphrase Extraction: 303–336. Retrieved 4 June 2012.

References The genesis of xAIgent rose from a thesis exploring the application of artificial intelligence and machine learning. Specifically, how the growing proliferation of information and intellectual property, primarily via the World Wide Web, could be refined and sourced with certainty and relevance. The application of artificial intelligence married with the theories of machine learning would prove effective.

We experience the great results of Dr. Turney's research efforts, providing developers and consumers with tools for better sourcing of information and most importantly its contextual meaning. The scientific research that went into the creation of the xAIgent Technology is found in the following published documentation:

  • Turney, P.D. (2000)

Learning algorithms for keyphrase extraction. Information Retrieval, 2 (4): 303-336. https://extractor.com/IR2000.pdf

  • Mathieu, J. (1999)

Adaptation of a keyphrase extractor for Japanese text. Proceedings of the 27th Annual Conference of the Canadian Association for Information Science (CAIS-99), Sherbrooke, Quebec, pp. 182-189. https://extractor.com/CAIS99.pdf

  • Turney, P.D. (1999)

Learning to Extract Keyphrases from Text. NRC Technical Report ERB-1057, National Research Council Canada. https://extractor.com/ERB-1057.pdf

  • Turney, P.D. (1997)

Extraction of Keyphrases from Text: Evaluation of Four Algorithms. NRC Technical Report ERB-1051, National Research Council Canada. https://extractor.com/ERB-1051.pdf

  • Answering Subcognitive Turing Test Questions: A Reply to French

http://extractor.com/subcognitive.pdf

  • Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL

http://extractor.com/turney-ecml2001.pdf

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