Using a novel professional certification survey, the study focuses on assessing the vocational skills of two highly cited AI models, GPT-3 and Turbo-GPT3.5. The approach emphasizes the importance of practical readiness over academic performance by examining the models' performances on a benchmark dataset consisting of 1149 professional certifications. This study also includes a comparison with human test scores, providing perspective on the potential of AI models to match or even surpass human performance in professional certifications. GPT-3, even without any fine-tuning or exam preparation, managed to achieve a passing score (over 70% correct) on 39% of the professional certifications. It showcased proficiency in computer-related fields, including cloud and virtualization, business analytics, cybersecurity, network setup and repair, and data analytics. Turbo-GPT3.5, on the other hand, scored a perfect 100% on the highly regarded Offensive Security Certified Professional (OSCP) exam. This model also demonstrated competency in diverse professional fields, such as nursing, licensed counseling, pharmacy, and aviation. Turbo-GPT3.5 exhibited strong performance on customer service tasks, indicating potential use cases in enhancing chatbots for call centers and routine advice services. Both models also scored well on sensory and experience-based tests outside a machine's traditional roles, including wine sommelier, beer tasting, emotional quotient, and body language reading. The study found that OpenAI's model improvement from Babbage to Turbo led to a 60% better performance on the grading scale within a few years. This progress indicates that addressing the current model's limitations could yield an AI capable of passing even the most rigorous professional certifications.

The research creates a professional certification survey to test large language models and evaluate their employable skills. It compares the performance of two AI models, GPT-3 and Turbo-GPT3.5, on a benchmark dataset of 1149 professional certifications, emphasizing vocational readiness rather than academic performance. GPT-3 achieved a passing score (>70% correct) in 39% of the professional certifications without fine-tuning or exam preparation. The models demonstrated qualifications in various computer-related fields, such as cloud and virtualization, business analytics, cybersecurity, network setup and repair, and data analytics. Turbo-GPT3.5 scored 100% on the valuable Offensive Security Certified Professional (OSCP) exam. The models also displayed competence in other professional domains, including nursing, licensed counseling, pharmacy, and teaching. Turbo-GPT3.5 passed the Financial Industry Regulatory Authority (FINRA) Series 6 exam with a 70% grade without preparation. Interestingly, Turbo-GPT3.5 performed well on customer service tasks, suggesting potential applications in human augmentation for chatbots in call centers and routine advice services. The models also score well on sensory and experience-based tests such as wine sommelier, beer taster, emotional quotient, and body language reader. The OpenAI model improvement from Babbage to Turbo resulted in a median 60% better-graded performance in less than a few years. This progress suggests that focusing on the latest model's shortcomings could lead to a highly performant AI capable of mastering the most demanding professional certifications. We open-source the benchmark to expand the range of testable professional skills as the models improve or gain emergent capabilities.