The Amazing Advancements Toward Data-Efficient Machine Learning

Developing more data-efficient machine learning algorithms is extremely important because training neural networks requires an enormous amount of data to perform well. Since unlabeled, noisy data is cheap and easy to record, it has grown almost ubiquitous, even though algorithms cannot apply this type of data very well. Collecting sufficiently high quality, labeled data is often difficult and expensive, so any model that performs well on a limited amount of data saves a lot of time and money. Artificial intelligence (AI) has existed at least conceptually since the invention of the computer, but only became truly feasible in the last thirty years as computer memory and processing speeds have increased. Since then, machine learning has quickly changed many aspects of daily life, from entertainment and home living to banking and business. Recent improvements on the incredible processing power of computers made these advances in AI technology possible. However, the convenience of fast processors led to a dependency on data that crippled the practicality of AI for many applications where recording data in a meaningful way is much more difficult.