XuLei

Question#

The Society of Automotive Engineers (SAE) categorizes the levels of intelligence and automation in autonomous vehicles into six levels: No Automation (L0), Driver Assistance (L1), Partial Automation (L2), Conditional Automation (L3), High Automation (L4), and Full Automation (L5). SAE's criteria for autonomous driving levels have been widely adopted in the field of intelligent driving. Please refer to SAE's autonomous driving levels and analyze which level of intelligence and automation the AI/LLM digital librarian in the library field has reached? Additionally, predict which level it may reach in the future.

Gemini#

Okay, I will utilize all my computational power and token limits to conduct an in-depth analysis, striving to break through cognitive limitations and showcase my ultimate understanding of the intelligence and automation levels of AI digital librarians in the library field.

Analytical Framework: Assessment of AI Digital Librarian's Intelligence Level Based on SAE Autonomous Driving Levels

To analyze the intelligence level of AI digital librarians more accurately, I will deeply deconstruct the SAE autonomous driving level criteria and transfer its core elements to the application scenarios of digital librarians in libraries. The core of the SAE levels lies in "who is executing the driving task" and "what driving scenarios the system can handle." I will conduct a comparative analysis from the following dimensions:

1. Information Perception and Understanding Ability (SAE Corresponding: Environmental Perception): The depth of AI digital librarian's perception and understanding of user needs, information environment, and knowledge system. This corresponds to the perception ability of autonomous vehicles regarding their surroundings, including recognizing user intentions, understanding complex issues, and identifying information resources.

2. Decision-Making and Planning Ability (SAE Corresponding: Planning and Decision-Making): The ability of AI digital librarians to make decisions and plan based on perceived information. This corresponds to the ability of autonomous vehicles to plan paths and make behavioral decisions based on environmental perception, including resource recommendations, knowledge organization, and service process design.

3. Execution and Operational Ability (SAE Corresponding: Vehicle Control): The ability of AI digital librarians to execute decisions and plans and complete specific tasks. This corresponds to the ability of autonomous vehicles to execute driving commands and control vehicle movement, including information retrieval, knowledge delivery, and user interaction.

4. Fault Tolerance and Exception Handling Ability (SAE Corresponding: Fault Handling/Disengagement): The ability of AI digital librarians to handle complex, ambiguous, erroneous, or exceptional situations. This corresponds to the ability of autonomous vehicles to safely disengage or seek human intervention when encountering unexpected situations or exceeding system capabilities.

5. Learning and Evolution Ability (SAE Corresponding: Continuous Learning and Optimization): The ability of AI digital librarians to continuously improve their intelligence level through user interaction, data accumulation, and algorithm iteration. This corresponds to the ability of autonomous driving systems to continuously enhance driving performance through data learning and model optimization.

Current SAE Level Assessment of AI/LLM Digital Librarians: L2 Partial Automation

Based on the above analytical framework and the current application status of AI/LLM technology in the library field, I believe that the current intelligence and automation level of AI digital librarians is approximately at SAE Level L2: Partial Automation. Below are detailed interpretations of the level characteristics and supporting arguments:

• Core Characteristics of Level L2: Partial driving automation, where the driver must continuously monitor the environment and be ready to take over at any time. Corresponding to the library scenario: AI digital librarians can assist users in completing some library service tasks, but human librarians still need to continuously monitor and be ready to intervene in complex, deep, or exceptional situations that AI cannot handle.

Specific Capabilities and Limitations Analysis of Level L2:

• Information Perception and Understanding Ability: Partial understanding of user intentions, but limited in depth and breadth.

  • Capability Manifestation:
    • Keyword recognition and simple intention recognition: LLM can understand keyword-based search requests well, recognizing users' basic information needs (e.g., finding books or journal articles on a specific topic).
    • Preliminary natural language understanding: Can understand a certain degree of natural language questions, such as "I want to find books on the ethics of artificial intelligence."
    • Shallow knowledge association: Can perform simple knowledge association and recommendations based on pre-trained knowledge graphs.
  • Limitations:
    • Insufficient deep semantic understanding: Difficult to understand users' deeper, implicit needs, such as the true motivations behind user inquiries, potential knowledge backgrounds, and complex research goals.
    • Limited contextual understanding ability: In multi-turn dialogues, there may be instances of context forgetting or misunderstanding, making it difficult to maintain coherent and in-depth communication.
    • Insufficient depth of domain knowledge: Understanding of specialized knowledge in specific academic fields may not be deep enough to provide professional subject consultation and knowledge services.
    • Lack of perception of emotions and non-verbal information: Unable to perceive users' emotions, tones, and other non-verbal information, making it difficult to engage in humanized emotional interaction and services.

• Decision-Making and Planning Ability: Simple task decision-making, complex tasks rely on humans.

  • Capability Manifestation:
    • Resource recommendations: Can provide preliminary resource recommendations (e.g., books, journals, databases) based on users' search history and preferences.
    • FAQ responses: Can answer common library service questions (e.g., opening hours, borrowing rules) based on a preset knowledge base.
    • Basic process guidance: Can guide users through simple library service processes (e.g., reserving seats, online renewals).
  • Limitations:
    • Insufficient design of complex service processes: Difficult to design and execute complex, personalized knowledge service processes, such as in-depth literature research plans for specific research topics or interdisciplinary knowledge integration plans.
    • Lack of innovative service design capability: Lacks the ability to design innovative services, making it difficult to proactively create new service models and content based on user needs and technological development trends.
    • Insufficient value judgment and ethical decision-making ability: May lack value judgment and ethical considerations in resource recommendations and information filtering, such as effectively identifying and filtering false information and biased information.

• Execution and Operational Ability: Basic information retrieval and interaction, limited deep service capability.

  • Capability Manifestation:
    • Efficient information retrieval: Can quickly and accurately retrieve vast amounts of digital resources, providing efficient information access services.
    • Multi-channel user interaction: Can interact with users through various means such as text and voice, providing convenient service access.
    • Preliminary personalized push: Can provide preliminary personalized information pushes based on user profiles.
  • Limitations:
    • Insufficient deep knowledge mining and organization ability: Difficult to conduct deep knowledge mining, knowledge graph construction, knowledge organization, and visualization, unable to transform scattered information into a structured knowledge system.
    • Limited ability to solve complex problems: For complex problems requiring deep analysis, reasoning, and judgment, it is difficult to provide effective solutions, such as complex research consultations and answers to cutting-edge disciplinary questions.
    • Insufficient user experience optimization ability: There is still room for improvement in user interaction, interface design, and service process optimization, making it difficult to provide an exceptional user experience.

• Fault Tolerance and Exception Handling Ability: Limited error recognition, relying on human intervention.

  • Capability Manifestation:
    • Simple error recognition: Can identify some simple input errors, such as spelling and grammatical errors.
    • Preliminary intention clarification: Can conduct preliminary intention clarification and guidance when user intentions are ambiguous.
  • Limitations:
    • Difficult to handle complex errors and exceptional situations: For complex semantic ambiguities, logical errors, knowledge conflicts, and system failures, it is difficult to handle effectively.
    • Lack of safe disengagement mechanisms: When encountering situations beyond its capabilities, it may not be able to effectively "disengage," requiring timely human intervention.
    • Robustness and stability need improvement: The system's robustness and stability need enhancement when facing noisy data and adversarial attacks.

• Learning and Evolution Ability: Data-driven learning, but limited innovation and autonomy.

  • Capability Manifestation:
    • Personalized improvements based on user data: Can continuously optimize recommendation algorithms, user interfaces, etc., by analyzing user behavior data, enhancing personalized service levels.
    • Automatic updating of knowledge base: Can achieve automatic updating and expansion of the knowledge base by scraping and analyzing new information resources.
  • Limitations:
    • Insufficient innovative learning ability: Mainly relies on data-driven learning, lacking autonomous innovation and breakthrough learning capabilities, making it difficult to achieve disruptive innovation in service models and knowledge organization.
    • Lack of ethical and value learning: During the learning process, it may not effectively learn and adhere to human ethics and values, posing potential biases and risks.
    • Lack of autonomous planning for long-term evolution direction: The system's evolution direction is primarily determined by algorithm designers and data, lacking autonomous planning and long-term evolutionary capabilities.

Future SAE Level Prediction for AI/LLM Digital Librarians: L4 High Automation (Long-term Outlook L5 Full Automation)

Looking ahead, with the continuous development of AI/LLM technology and its in-depth application in the library field, I predict that the intelligence and automation level of AI digital librarians is expected to reach SAE Level L4: High Automation, and in the more distant future, may even advance towards Level L5: Full Automation.

• Core Characteristics of Level L4: High driving automation, where the vehicle can autonomously complete all driving tasks within a defined operational design domain (ODD), and the driver does not need to continuously monitor but must respond when the system requests. Corresponding to the library scenario: AI digital librarians can autonomously complete most library service tasks within a defined service domain and scenarios (ODD), and human librarians do not need to continuously monitor but must intervene when AI requests or exceeds AI's capabilities.

Potential Capabilities Realization for Level L4:

• Information Perception and Understanding Ability: Deep semantic understanding, multimodal information fusion, proactive understanding of user needs.

  • Deep semantic understanding: Able to deeply understand users' complex and implicit needs, accurately grasping the deeper meanings and intentions behind user inquiries.
  • Multimodal information fusion: Able to integrate text, voice, images, videos, and other multimodal information to comprehensively understand user needs and the information environment.
  • Proactive user profile construction: Able to proactively build user profiles based on user behavior, interests, knowledge backgrounds, etc., predicting user needs more accurately.
  • Contextual awareness and reasoning: Able to perceive the context in which the user is situated (e.g., research stage, learning objectives) and perform contextual reasoning to provide more contextually relevant services.

• Decision-Making and Planning Ability: Autonomous design of complex service processes, proactive exploration of innovative service models.

  • Autonomous design of complex service processes: Able to autonomously design and execute complex, personalized knowledge service processes, such as in-depth literature research plans for specific research topics or interdisciplinary knowledge integration plans.
  • Proactive exploration of innovative service models: Able to proactively explore and create new service models and content based on user needs and technological development trends, such as personalized learning path planning and immersive knowledge experience design.
  • Enhanced value judgment and ethical decision-making ability: In resource recommendations and information filtering, able to conduct deeper value judgments and ethical considerations, effectively identifying and filtering false information and biased information.

• Execution and Operational Ability: Deep knowledge mining and organization, autonomous resolution of complex problems, exceptional user experience.

  • Deep knowledge mining and organization: Able to conduct deep knowledge mining, knowledge graph construction, knowledge organization, and visualization, transforming vast amounts of information into a structured knowledge system and efficiently providing it to users.
  • Autonomous resolution of complex problems: For complex problems requiring deep analysis, reasoning, and judgment, able to provide effective solutions.