Technical Perspective: Machine Learning in Computer Security is Difficult to Fix

During an interview in 2017, Andrew Ng--one of the most renowned computer scientists in the field of artificial intelligence (AI)--was reported to say: "Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years." Indeed, over the last decade, we have observed a rebirth of interest in AI and, more specifically, in its machine learning (ML) subfield, which is aimed at designing algorithms that learn from examples. This has been fueled by the availability of large volumes of data over the Internet, the increased computing power of today's hardware and cloud infrastructures, and the algorithmic improvements in deep learning and neural networks, which have shown tremendous progress in dealing with text, audio, image, and video data. Their success has been even reinforced more recently by the advent of foundational and generative AI models that can generate realistic text, images, and videos with impressive quality. For these reasons, AI and ML have been fostering important advancements in healthcare, automotive, robotics, recommendation systems, chatbots, and many other applications.