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Introduction
xAIgent, developed by DBI Technologies Inc., is an advanced AI/ML technology that extracts key phrases, metadata, across various types of text based content, including email, HTML pages, other forms of unstructured content. The xAIgent technology automatically generates bullet point summaries in the form of key phrases, with contextual relevance, across any subject domain. Applications that include topic curation, search engine optimization (SEO), content management, knowledge management, document management, intelligent search, indexing, categorization, cataloguing, content tagging, Large Language Model (LLM) tuning, for instance can be benefactors of this technology.
The xAIgent technology is based upon a patent-backed AI/ML strategy that allows for precision content tagging removing the traditional process 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.
xAIgent continues to contribute for the efficiency of document management systems providing contextually accurate metadata for quicker and more accurate retrieval of specific subject matter content. It can also facilitate the automatic classification and clustering of documents for better content curation, organization and management (Doc-Tags[1].
History
The concept of xAIgent was conceived with the aim of bridging the gap between complex AI technologies and practical, real-world applications. The platform was 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, complex model training, and deployment tasks with ease. The core of xAIgent is an integrated patent established machine learning and artificial intelligent framework and API suitable for assisting natural language processing (NLP), Search Engine Indexing to Large Language Model (LLM) tuning. The platform, designed to be scalable, supports projects ranging from small-scale experiments to enterprise-level deployment across geographic regions. Supported languages: English, French, German, Japanese, Korean and Spanish.
Technology
The technology behind xAIgent includes a proprietary mix of deep learning algorithms, predictive analytics, and natural language processing techniques. Released on a cloud computing platform allowed for scalable processing and compute power of complex / high volume processes without imposing extensive hardware for users. xAIgent incorporates explainable AI (XAI) principles to make AI decisions transparent and understandable, which addressed one of the significant challenges for AI adoption.
Applications
xAIgent has been expoited in Knowledge Management sectors, content curation, content tagging systems including enterprise content and document management. The xAIgent technology is also used in fraud detection, homeland risk assessment, search engine optimization, document indexing[2], Search Engine Indexing, consumer document management Doc-Tags[3].
Impact
The introduction of xAIgent significantly impacted how business and government approach AI and data analytics for broader access to AI technologies. xAIgent enabled enities of all sizes to innovate and improve their services and operations by expressing keyphrase metadata from stores of unstructured data. xAIgent emphasised 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 keyphrases or metadata, it enables an integrated platform to handle diverse data types more efficiently, 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 can improve the quality of this input data by extracting relevant keyphrases. (see reference - Learning to Extract Keyphrases from Text below) This can lead to the development of more accurate and reliable Large Language models.
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 can play a significant role.
Accelerating Development and Deployment
The xAIgent technology demonstrated 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 enabling faster iteration and innovation.
Enabling More Complex Insights
The process of extracting high-quality keyphrases from complex data, the xAIgent technology allows for 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 for decision-making.
Supporting Explainability and Interpretability
xAIgent plays a role in enhancing the explainability and interpretability of AI models by identifying which features 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 ==
- ^ https://xaigent.net/Doc-Tags.aspx
- ^ Index-Docs_https://xaigent.net/DocIndexGenerator.aspx
- ^ https://xaigent.net/Doc-Tags.aspx
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 conumers 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.
- 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.
- Turney, P.D. (1999)
Learning to Extract Keyphrases from Text. NRC Technical Report ERB-1057, National Research Council Canada.
- Turney, P.D. (1997)
Extraction of Keyphrases from Text: Evaluation of Four Algorithms. NRC Technical Report ERB-1051, National Research Council Canada.
- 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