Over the past few decades, several definitions of AI have surfaced. In its simplest form, AI combines computer science and robust datasets to enable problem-solving. But is AI technically just a model or should AI, machine learning and deep learning be categorized differently?
- Myth versus reality: what AI is and what it isn’t, what it can do and what it can’t
- The distinctions between artificial intelligence, machine learning, deep learning and neural networks
- Why those distinctions matter in terms of both determining the requisite risk considerations and identifying the most commercially viable use cases