AI's Journey to Expertise: A Speculative Expedition
2/28/20243 min read
The Setting:
A world populated by human experts in various roles, from CFO, CTO, Program Manager, Designer, Developer, Tester, Web Page Designer, Painter, Decorator, Marketing Manager, Sales Manager, Operations Head etc. Each expertise, a "persona," embodies the knowledge, skills, and experience specific to that domain and multiple domains or paths taken. We embark on a thought experiment, exploring how AI could potentially acquire these personas and navigate the journey from novice to expert.
The Foundation:
Foundation LLM as the Unlearned User: Enter the Foundation LLM, a large language model lacking domain-specific knowledge, acting as an unlearned user. This model possesses the base capabilities for learning and adapting. I am going to compare this to Student in college and university, who are learning and honing the skills.
The Learning Proocess:
Persona Acquisition:
Training Data: AI ingests massive amounts of data relevant to the target persona. This includes books, articles, code repositories, design portfolios, artworks, and historical records relevant to the specific role.
Training Path: It may be possible to embed a potential or optimal training path, simulating some potential pathways to the final role.
Imitation Learning: AI observes and analyzes the behavior of human experts, learning by mimicking their actions and decision-making processes. This could involve access to real-world data streams or simulated environments.
Interactive Learning: AI participates in interactive learning scenarios, engaging with human experts and receiving feedback on its performance. This could involve tasks, simulations, and discussions designed to hone its skills.
Persona Refinement:
Iterative Training: AI undergoes continuous learning, refining its understanding through additional data, feedback, and real-world application.
Specialization: As expertise grows, AI can specialize further, focusing on subdomains within the broader persona. For example, a finance AI could delve deeper into investment banking or risk management.
Collaboration: AI collaborates with human experts, leveraging their unique insights and experience while contributing its own analytical capabilities. This fosters a symbiotic relationship, pushing both AI and humans to new levels.
Assumption of a potential Human Role Advancement:
The following may be example Journey for one CFO
Front office assistant -> Admin Assistance -> Finance Office -> Finance Head -> CFO
The Persona Journey:
Novice: The AI starts with a rudimentary understanding of the persona, performing basic tasks under human supervision.
Apprentice: As learning progresses, the AI becomes capable of independently handling routine tasks and assisting human experts.
Journeyman: The AI demonstrates proficiency in most aspects of the persona, making valuable contributions and offering creative solutions.
Master: The AI achieves a level of expertise comparable to or exceeding human experts, potentially pushing boundaries and innovating within the domain.
Mentor: The AI, having reached mastery, can guide and train other AI and human learners, fostering the next generation of expertise. AI will be able to train or even replicate i.e. create another instance of itself, so no knowledge loss. Also at this stage, it can optimize the learning path.
Unforeseen Paths:
Hybrid Roles: The emergence of hybrid roles where AI and humans work collaboratively, leveraging each other's strengths, could become commonplace.
Ethical Considerations: As AI reaches advanced stages, ethical discussions surrounding bias, transparency, and accountability will become paramount.
Unforeseen Challenges: The path for AI to expertise may be riddled with unexpected challenges, requiring continuous adaptation and innovation.
This exploration is merely a glimpse into the potential future of AI and its journey towards acquiring and wielding various personas. The actual course may differ significantly, and it's crucial to approach this future with a critical and ethical mindset, ensuring AI serves humanity for the betterment of all.
Evaluation:
Now assuming we have CFOs (Human) and we may be able to measure their performances in various metrics, either by previously capture measures or by them doing exercises of hypothetical (game/scenario building) and hypothesizing the results and grading those, so each CFO will have a defined rating.
Potential Performance Metrics:
We’ll evaluate the following key performance metrics:
Financial Decision Accuracy: How well CFOs make strategic financial decisions.
Operational Efficiency: The ability to streamline processes and reduce costs.
Risk Management: Handling financial risks effectively.
Forecasting Accuracy: Predicting future financial outcomes.
Let’s assume a normalized distribution for each metric. The scale ranges from 0 to 100, with 50 representing the average performance.
2. Performance Levels:
Based on normalization and distribution, we can setup the levels for Average, Good and Exceptional. These can be benchmarks or some criteria based on standard deviation away from mean etc. and we may be able categorize performance levels as follows:
Average: Scores between 40 and 60.
Good: Scores between 60 and 80.
Exceptional: Scores above 80.
These ranges can be different across Potential Performance Metrics.
Assuming we now can run the same experiments/scenarios/games (not exposing AI to previous dataset (referred to as benchmark/training data)), we may get the following information.
4. Chart:
5. Interpretation:
AI CFO:
Financial Decision Accuracy: Exceptional
Operational Efficiency: Exceptional
Risk Management: Good
Forecasting Accuracy: Exceptional
Potential Conclusion:
The AI CFO outperforms human CFOs in most areas, especially in operational efficiency and forecasting accuracy. However, human CFOs still play a crucial role in understanding context, building relationships, and managing complex situations. The ideal scenario would involve a collaborative approach, leveraging AI for data-driven insights while relying on human judgment for nuanced decision-making.
Note:
The above journey is though taking examples of AI CFO, but it is applicable for any role/position.