Now that we are well underway into 2020, many predictions already exist for what the top research tracks and what greatest new ideas may emerge in the next decade. Predictions tend to be based on the best guesses or gut reactions from practitioners and subject-matter experts in the field.
While experience drives expertise in visions for the future, data scientists remain experimentalists at their core. So, it should sound reasonable that predictions for the next important movements in AI and machine learning should be based on collectible data. With machine learning-themed papers continuing to be churned out at a rapid clip from researchers around the world, monitoring those papers that capture the most attention from the research community seems like an interesting source of predictive data.
The following list presents a prediction of what might come to pass in the field of artificial intelligence and machine learning. From graph machine learning, advancing convolutional neural networks, semisupervised learning, generative models, and dealing with anomalies and adversarial attacks, the science will likely become more efficient, work at larger scales, and begin performing better with less data soon as we progress into the 2020s.