| Sumario: | Artificial intelligence (AI) has rapidly progressed, with exciting opportunities that drive enthusiasm for significant projects. A sensible and sustainable approach would be to start building an AI footprint with smaller, machine learning (ML)-based initiatives using artificial neural networks before progressing to more complex deep learning (DL) approaches using convolutional neural networks. Several strategies and examples of entry-level projects are outlined, including mock potential projects using convolutional neural networks toward which we can progress. The examples provide a narrow snapshot of potential applications designed to inspire readers to think outside the box at problem solving using AI and ML. The simple and resource-light ML approaches are ideal for problem solving, are accessible starting points for developing an institutional AI program, and provide solutions that can have a significant and immediate impact on practice. A logical approach would be to use ML to examine the problem and identify among the broader ML projects which problems are most likely to benefit from a DL approach.
|
|---|