Navigating the intersection of AI and human comprehension? Xplainable stands apart from traditional models, which often remain opaque "black boxes". Instead, we've deliberately constructed xplainable to be a clear, transparent model from its very foundation.
Our vision? To bridge the gap between technical experts and those not as technically inclined. We believe in fostering understanding across all professional realms, and with xplainable, our Python package, we're set to make this vision a reality. Now, we need your expertise and passion to harness this tool, ensuring that we successfully dissolve barriers and enable seamless communication between technical and non-technical stakeholders.
Thank you for your interest in joining our team. We value originality and clarity in getting to know our applicants better. Instead of a traditional cover letter, we would like to understand your problem-solving capabilities and creativity.
Your Task: We have provided a challenge below. Present your answer in any format that best represents your thought process and skills. Feel free to choose among white board sketches, hand-written drawings, code snippets, diagrams, or any other medium that best demonstrates your thinking.
Submission Instructions:
We're excited to see how you approach problems and your unique way of presenting solutions. Remember, it's not just about the right answer, but about the thought process you undertook to get there.
Good luck, and we look forward to seeing your application!
We have a multi-dimensional system wherein each dimension (or feature) can have one value selected at a time. These values are summed to generate a score, with the objective being to achieve a specific target score or the next closest possible value. The complexity arises from an interdependent locking function: locking a value in one feature can restrict the available values in other specific features. This leads to a reduced yet hierarchically-structured search space when trying to achieve the target score. Hover over the pink node to simulate a locked value.
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