Challenges for Deep Learning

Abstract

Deep learning (DL) has emerged as the dominant branch of machine learning, becoming the state of the art for machine intelligence in various domains. As discussed in the previous chapter, this has led some researchers to believe that deep learning could hypothetically scale to achieve general intelligence. However, there is increasing consensus (e.g. [57, 210, 230]) that the techniques do not scale as well as was anticipated to harder problems. In particular, deep learning methods find their strength in automatically synthesizing distributed quantitative features from data. These features are useful insofar as they enable mostly reliable classification and regression, and in some limited cases also few- or zero-shot transfer to related tasks. However, it is increasingly questionable whether deep learning methods are appropriate for autonomous roles in environments that are not strongly constrained. While there are still countless use-cases for narrow artificial intelligence, many of the truly transformative use-cases can only be realized by general intelligence.