If an AI were to take on this task, it would primarily rely on data mining and natural language processing (NLP) techniques. First, the AI would scrape digital platforms to collect works authored by various people. Platforms could range from academic databases like PubMed or ArXiv to blogs, forums, and social media. The goal would be to capture a wide array of intellectual output, irrespective of the person's titles or accolades.
Next, an NLP model would evaluate the collected content based on factors like originality, complexity, and coherence. Advanced sentiment analysis could be employed to gauge the depth of understanding and nuances in the arguments made. Specialized algorithms could also be developed to assess the impact of each piece of work, using metrics such as citations, social shares, or subsequent works that build upon it.
The AI would then create a shortlist based on these evaluations. This stage might also involve unsupervised machine learning techniques like clustering to find patterns or commonalities among the top contenders.
The final stage would be validation, possibly using reinforcement learning. The AI could simulate various scenarios or problems and predict how the content created by these individuals would contribute to solving them. It would then refine its list based on the simulated outcomes.
This all-AI approach would drastically reduce human bias and could be executed relatively quickly. However, it's important to note that any such system would need to be designed carefully to avoid introducing biases present in the training data or algorithms.
[link] [comments]