AI: Preventing a Frankenstein's monster

One of the key lessons taught by Mary Shelley's infamous story of Frankenstein's monster is that things aren't always greater than the sum of their parts, regardless of the quality of the parts themselves An altogether less visceral but equally composition-based process goes into building today's artificial intelligence (AI) platforms. One of the most powerful AI models used today is deep learning, a machine learning algorithm that identifies patterns in different sets of input data, and uses them to generate insights that help inform human decision-making. Deep learning applies vast layers of artificial neural networks to data, creating a'black box' of calculations that are impossible for humans to understand. Luckily for data scientists, preventing the creation of a'monster' when developing AI requires an understanding of data validity, rather than the supernatural. AI platforms built on deep learning assume that more data equals better accuracy.