AI Levels of Autonomy

We've had levels of autonomous for self-diving vehicles for years, let's make one for general AI autonomous systems

Just as autonomous vehicles are categorized into distinct autonomy levels, we also ought to distinguish the complexities and sophistication of AI automation through a similar layered model. Just like the advancing AI levels for self-driving cars, general use cases for AI automation are increasingly becoming more intricate, capable, and autonomous. Consequently, they need a structured format akin to those adopted for self-driving vehicles. This will help facilitate comprehension, design, and development of these future systems. Self-driving vehicles range from Level 0 (no automation) to Level 5 (fully automated).

What could this look like for AI automation?

Below is my proposal:

Level 0: No Automation

• Description: The system performs simple tasks entirely driven by human input, without any understanding or contextual awareness.

• Capabilities: No autonomous agent. Manual task execution, no autonomous decision-making or contextual understanding.

• Use Cases:

  • Calculator

Level 1: Assisted Automation

• Description: Provides suggestions and completions with minimal understanding. Requires significant human oversight and intervention. Capable of assisting with straightforward tasks.

• Capabilities: Simple suggestions and completions, minimal contextual understanding, requires human confirmation.

• Use Cases:

  • Grammar and spell checking in documents

  • Basic email drafting with suggestions

  • Auto-suggestions in search engines

  • Assisted data entry with suggestions

  • Predictive text in messaging apps

Level 2: Partial Automation

• Description: Handles more complex queries and tasks with some contextual understanding. Requires human intervention for nuanced or ambiguous tasks. Capable of basic interactions and limited decision-making.

• Capabilities: Contextual understanding of queries, basic conversational interactions, limited autonomous decision-making.

• Use Cases:

  • Customer support chatbots for common queries

  • Personal assistants for scheduling and reminders

  • Simple data analysis and visualization

  • Automated product recommendations in e-commerce

  • Language translation with human oversight

Level 3: Conditional Automation

• Description: Understands context and can perform a variety of tasks with moderate complexity. Requires occasional human supervision for complex or highly specific tasks. Capable of managing multi-turn conversations and executing predefined workflows.

• Capabilities: Moderate contextual understanding, multi-turn conversational capabilities, execution of predefined workflows with conditional logic.

• Use Cases:

  • Virtual tutors for educational purposes

  • Content creation (articles, reports) with editing

  • Project management assistance (task tracking, deadline reminders)

  • Interactive voice response (IVR) systems in call centers

  • Automated financial advising for routine investments

Level 4: High Automation

• Description: High contextual awareness and decision-making capabilities. Can perform most tasks independently with minimal human intervention. Able to handle complex interactions, provide detailed explanations, and adapt to new information dynamically.

• Capabilities: High-level contextual understanding, advanced decision-making, dynamic adaptation to new information, minimal human intervention required.

• Use Cases:

  • Advanced customer service with complex query resolution

  • Technical support for diagnosing and solving issues

  • Complex data analysis and predictive modeling

  • Autonomous driving with human oversight in specific scenarios

Level 5: Full Automation

• Description: Fully autonomous in understanding, generating, and acting upon diverse and complex inputs. Capable of reasoning, learning, and adapting in real-time without human intervention. Performs a wide range of tasks across different domains seamlessly.

• Capabilities: Full autonomy, real-time reasoning and learning, seamless task performance across diverse domains without human intervention.

• Use Cases:

  • Autonomous research assistants conducting independent studies

  • Strategic decision-making systems in business and finance

  • Autonomous content creation for various media (articles, videos)

  • Complex system control (e.g., smart cities, industrial automation)

  • Fully autonomous vehicles in all driving conditions

  • Real-time disaster response and management systems

  • Autonomous medical diagnosis and treatment planning