The Future of AI: Assessing the Strengths and Limitations of Deep Learning and Machine Learning

Abstract

Artificial Intelligence no longer requires an introduction and is an inherent part of modern advancements in technologies. The two driving paradigms are Machine Learning and Deep Learning, and this paper attempts to evaluate the strengths and weaknesses in these two approaches by comparing two different algorithms: Random Forest representing Machine Learning and Long Short-Term Memory representing Deep Learning. These algorithms were applied on two datasets: one simple, low-dimensional Iris dataset and another more complex one concerning brain tumor detection. This was done to establish how different complexities and structures of datasets influence the performance and computational demands of ML and DL algorithms.

The results of the experiments showed that RF, as one of the traditional ML algorithms, is quite efficient and relatively easy to implement. It ran smoothly on the Iris dataset, giving competitive accuracy when compared to low computation overhead, while LSTM, which belongs to the DL family, did much better in the brain tumor detection dataset. Although computationally expensive and more difficult to train, LSTM gave higher accuracy when dealing with complex datasets such as medical image data .

This study infers that while the DL algorithms like LSTM are resource-intensive and relatively complex, they tend to outperform the ML algorithms such as Random Forest when applied to complex high-dimensional data, but the ML algorithms are good in simpler tasks where the execution pace is rapid enough with sufficient accuracy. From this, the tradeoff between accuracy and complexity comes into view in selecting AI models based on the nature of the dataset.