Interdisciplinary One-Shot Learning: Mimicking Human Cognition for Data-Efficient Innovation Across Design, Engineering, Business, and Culture

Abstract

Machine learning has made great strides, but its progress is often held back by a need for massive datasets. This is a major hurdle in fields where data is scarce or expensive. Humans, on the other hand, can learn from just a few examples. This paper introduces a new one-shot learning framework that mimics this human ability. We built a model that learns similarity in a way that's similar to how people do it, by looking at the general appearance of things. This makes our model more transparent and easier to understand than many other AI systems. We also developed a new optimization algorithm to make the learning process more efficient. We show that our framework can be used to solve problems in a variety of fields, including design, engineering, business, and culture. Our experiments show that our model performs well even with very little data, and that it is also easy to interpret. This work is a step towards building more human-like AI, and it also opens up new possibilities for innovation in many different areas.