OpenAI’s newly introduced internal scale aims to evaluate its AI systems’ progress and capabilities systematically:
Level Capabilities Level 1 Engages in simple conversational tasks, similar to current chatbots like ChatGPT Level 2 Solves basic problems at the level of a PhD holder Level 3 Takes actions on behalf of users, demonstrating practical utility Level 4 Creates novel solutions and innovations, exhibiting creativity and adaptability Level 5 AGI – Performs tasks equivalent to entire organizations, surpassing human-level performance across various tasksThis scale, ranging from Level 1 to Level 5, seeks to track the progression towards achieving Artificial General Intelligence (AGI) — the holy grail of AI development where machines exhibit human-like cognitive abilities.
Understanding OpenAI’s five levels of AI developmentHere’s a detailed breakdown of how each level is defined and the criteria used to assess the power of AI systems:
Level 1: Basic conversational AIAI systems at this level can engage in simple conversational tasks, akin to current chatbots like ChatGPT.
Assessment criteria:
AI systems at this level are capable of solving basic problems at the level of a person with a PhD.
Assessment criteria:
AI agents at this level can take autonomous actions on behalf of users.
Assessment criteria:
AI systems at this level can create new innovations and exhibit creativity and adaptability.
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The final level represents AI that can perform the work of entire organizations, surpassing human-level performance in most economically valuable tasks.
Assessment criteria:
To ensure the accuracy and reliability of its AI power scale, OpenAI plans to conduct rigorous internal evaluations of its AI systems through several key methods.
Benchmark testing involves standardized tests designed to measure specific capabilities and performance metrics aligned with each level’s criteria. These tests provide a consistent framework for evaluating AI systems, ensuring objective assessments and identifying areas for improvement.
Expert review engages domain experts to assess the AI systems’ performance in specialized fields. These experts ensure thorough and accurate evaluations, validating that the AI meets high standards required for each level.
Real-world scenarios test AI systems in practical applications to validate their effectiveness and reliability. This approach allows OpenAI to observe how systems perform in dynamic environments, ensuring robustness and practical utility.
User feedback involves collecting and analyzing feedback from users interacting with AI systems. This feedback provides insights into practical utility and user satisfaction, highlighting strengths and areas for improvement.
By combining these methods, OpenAI aims to thoroughly evaluate and verify its AI systems, ensuring they meet the criteria for each level of the power scale and driving progress towards achieving Artificial General Intelligence (AGI).
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