Today, a lot of data is available in the form of raw unstructured data: Data without meaning. The field of machine learning takes such data, structures it, and uses it to produce meaningful models that can solve real-world problems. For example, today, most of the data collected comes in the form of text. Every day more than 18 billion pieces of textual data are generated across the globe, the overall meaning of which is simply impossible for humans to understand. This is where the need for machine learning architectures comes into play. Using natural language processing (NLP) architectures, we can train models on this data to ultimately perform various tasks, such as sentiment analysis, text generation, translation, etc. My research seeks to use such natural language processing models to expose software vulnerabilities within code. Ultimately, it is to prevent malicious agents from gaining access and performing unauthorized actions due to flaws in code left by software developers.
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