Since the emergence of the data age we have been taught the 4 Vs of good datasets: volume, velocity, variety, and veracity. These are the qualities that have given rise to the big data companies that have come to dominate the growing use of machine learning (ML) and artificial intelligence (AI). Those with the resources to enable the velocity, variety, and veracity of great volumes of data have been able to lead the data race with sheer problem-solving capacity. Recently, however, new approaches have emerged to disrupt the core principle of volume and give rise to a small data counter-revolution. The first is transfer learning, which transfers elements of big data models pre-trained for other tasks to tackle a new problem where data may be more difficult, or impossible, to collect. Google’s open-source BERT model, for example, has built up over 340 million parameters, while OpenAI’s closed GPT-3 is orders of magnitude bigger with 175 […]