3. Implementation Future Stages

Stage 2: Basic Machine Learning Model

As the number of students rises past a 1000, the algorithm must adapt to keep up with the new dynamics of a growing company. At this point greenwood would have just about enough data to train a basic machine learning model. This would be a recommendation algorithm that utilises a simple machine learning technique used on low to medium amounts of data. This model will improve on the previous algorithm’s processing speed and match making procedure to generate better matches for each student. Depending on the amount of features present within the data, different kinds of machine learning models can be utilized.

Stage 3: Complex AI

Once Greenwood has a massive reach, we can further adapt the recommendation algorithm to use complex technical AI. This AI will provide exceptionally fast processing speeds and approach the dataset as objectively as possible, giving students the matches they truly deserve. Again, students will be able to filter out personal preferences from the recommended list of profiles.

Step 3.5: Potential Impact

In the long term, the AI will no longer just help in matchmaking, but also find new opportunities such as unexplored geographical areas, underutilised tools, and most effective mentorship methods. This could springboard a bunch of other methods to further Greenwood’s goal of increased representation of Black and Latinx communities in the business world.