Extractive AI

Extractive AI is a specialized branch of artificial intelligence focused on identifying and retrieving specific information from existing data sources. Unlike generative AI, which creates new content, extractive AI is designed to locate exact pieces of data within structured or unstructured datasets. By leveraging advanced natural language processing (NLP) techniques, extractive AI can understand human language to extract meaningful information from a variety of formats, such as text documents, images, audio files, and more.

At its core, extractive AI functions as an intelligent data miner. It sifts through vast quantities of information to find relevant snippets that match a user’s query or keywords. This capability makes extractive AI invaluable for tasks that require accuracy, transparency, and control over the extracted information. It ensures that users receive precise answers derived directly from trusted data sources.

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How Does Extractive AI Work?

Extractive AI operates through a combination of sophisticated NLP techniques and machine learning algorithms. The process involves several key steps:

1. Data Ingestion:
   • The system accepts various data formats, including text documents, PDFs, emails, images, and more.
   • Data is preprocessed to standardize formats and prepare for analysis.
2. Tokenization:
   • Text data is broken down into smaller units called tokens, such as words or phrases.
   • Tokenization facilitates the analysis of language structures.
3. Part-of-Speech Tagging:
   • Each token is labeled with its grammatical role (e.g., noun, verb, adjective).
   • This step helps in understanding the syntactic relationships between words.
4. Named Entity Recognition (NER):
   • The system identifies and classifies key entities within the text, such as names of people, organizations, locations, dates, and monetary values.
   • NER enables the extraction of specific information relevant to the query.
5. Semantic Analysis:
   • The system interprets the meaning and context of words and sentences.
   • It understands synonyms, antonyms, and contextual nuances.
6. Query Processing:
   • User inputs a query or keyword(s) specifying the information needed.
   • The system interprets the query to determine the search parameters.
7. Information Retrieval:
   • Using indexing and search algorithms, the system scans the data to find matches to the query.
   • Relevant data fragments are identified and extracted.
8. Result Presentation:
   • Extracted information is presented to the user in a clear and organized format.
   • The system may also provide the source or context from which the information was extracted.

This systematic approach allows extractive AI to deliver precise and accurate information directly sourced from existing data, ensuring reliability and trustworthiness.

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Difference Between Extractive AI and Generative AI

Understanding the distinction between extractive AI and generative AI is crucial for selecting the appropriate tool for specific applications.

• Extractive AI:
  • Function: Retrieves exact information from existing data sources.
  • Output: Provides precise data excerpts without generating new content.
  • Use Cases: Ideal for tasks requiring high accuracy and verifiable information, such as data extraction, summarization, and information retrieval.
  • Advantages: Ensures transparency, traceability, and reduces the risk of errors or “hallucinations.”
• Generative AI:
  • Function: Creates new content based on learned patterns from training data.
  • Output: Generates human-like text, images, or other media forms not directly pulled from existing data.
  • Use Cases: Suitable for content creation, language translation, chatbot responses, and creative applications.
  • Limitations: May produce inaccurate or nonsensical outputs due to the predictive nature of content generation.

While both technologies leverage AI and NLP, extractive AI focuses on accuracy and retrieval, whereas generative AI emphasizes creativity and generation of new content.=

Example 1: Invoice Data Extraction

A company processes over 1,000 invoices daily from various vendors, each with unique formats. Manually entering invoice data is labor-intensive and prone to errors.

• Automation of Data Entry:
  • The system automatically extracts essential invoice details like supplier name, invoice date, amounts, and line-item details.
• Maintain Table Structures:
  • Preserves the table formats of invoices, ensuring data integrity.
• Categorization:
  • Organizes extracted data into categories such as general information, supplier details, and line items.
• Benefits:
  • Accuracy: Achieves up to 99% data extraction accuracy.
  • Efficiency: Significantly reduces processing time.
  • Cost Savings: Lowers operational costs associated with manual data entry.

Example 2: Legal Document Analysis with Extractive AI

A law firm needs to review thousands of contracts to identify clauses related to confidentiality and non-compete agreements. Using extractive AI:

• Clause Identification:
  • The AI system scans contracts to extract clauses pertaining to confidentiality and non-compete terms.
• Risk Assessment:
  • Flags clauses that may pose compliance risks or conflicts with existing agreements.
• Summary Generation:
  • Provides summaries of key contractual obligations for quick reference.
• Benefits:
  • Time Savings: Reduces the time lawyers spend on manual document review.
  • Improved Accuracy: Minimizes the risk of overlooking critical clauses.
  • Enhanced Compliance: Supports adherence to legal and regulatory standards.

Example 3: Customer Support Enhancement

A tech company wants to improve its customer support experience. By deploying extractive AI:

• Knowledge Base Utilization:
  • Extracts answers from a vast repository of support documents.
• Quick Responses:
  • Provides customers with immediate, accurate answers to their inquiries.
• Agent Assistance:
  • Supplies support agents with relevant information during interactions.
• Benefits:
  • Improved Customer Satisfaction: Faster resolution of issues.
  • Reduced Workload: Decreases the volume of support tickets requiring human intervention.
  • Consistent Support Quality: Ensures accurate and uniform responses.

Research on Extractive AI

1. DiReDi: Distillation and Reverse Distillation for AIoT Applications
   Published: 2024-09-12
   Authors: Chen Sun, Qing Tong, Wenshuang Yang, Wenqi Zhang
   This paper discusses the efficiency of deploying edge AI models in real-world scenarios managed by large cloud-based AI models. It highlights the challenges in customizing edge AI models for user-specific applications and the potential legal issues arising from improper local training. To address these challenges, the authors propose the “DiReDi” framework, which involves knowledge distillation and reverse distillation processes. The framework allows edge AI models to be updated based on user-specific data while maintaining user privacy. The study’s simulation results demonstrate the framework’s capability to enhance edge AI models by incorporating knowledge from actual user scenarios.
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2. An open-source framework for data-driven trajectory extraction from AIS data — the $α$-method
   Published: 2024-08-23
   Authors: Niklas Paulig, Ostap Okhrin
   This research presents a framework for extracting ship trajectories from AIS data, crucial for maritime safety and domain awareness. The paper addresses technical inaccuracies and data quality issues in AIS messages by proposing a maneuverability-dependent, data-driven framework. The framework effectively decodes, constructs, and assesses trajectories, improving transparency in AIS data mining. The authors provide an open-source Python implementation, demonstrating its robustness in extracting clean and uninterrupted trajectories for further analysis.
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3. Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project
   Published: 2024-07-16
   Authors: David Moats, Chandrima Ganguly
   This commentary evaluates Open AI’s Democratic Inputs programme, which funds projects to enhance public participation in generative AI. The authors critique the programme’s assumptions, such as the generality of LLMs and equating participation with democracy. They advocate for AI participation that focuses on specific communities and concrete problems, ensuring these communities have a stake in the outcomes, including data or model ownership. This paper emphasizes the need for democratic involvement in AI design processes.
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4. Information Extraction from Unstructured data using Augmented-AI and Computer Vision
   Published: 2023-12-15
   Author: Aditya Parikh
   This paper explores the process of information extraction (IE) from unstructured and unlabeled data using augmented AI and computer vision techniques. It highlights the challenges associated with unstructured data and the need for efficient IE methods. The study demonstrates how augmented AI and computer vision can improve the accuracy of information extraction, thereby enhancing decision-making processes. The research provides insights into the potential applications of these technologies in various domains.
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