  Deep neural networks have triggered a revolution in artificial intelligence,
having been applied with great results in medical imaging, semi-autonomous
vehicles, ecommerce, genetics research, speech recognition, particle physics,
experimental art, economic forecasting, environmental science, industrial
manufacturing, and a wide variety of applications in nearly every field. This
sudden success, though, may have intoxicated the research community and blinded
them to the potential pitfalls of assigning deep learning a higher status than
warranted. Also, research directed at alleviating the weaknesses of deep
learning may seem less attractive to scientists and engineers, who focus on the
low-hanging fruit of finding more and more applications for deep learning
models, thus letting short-term benefits hamper long-term scientific progress.
Gary Marcus wrote a paper entitled Deep Learning: A Critical Appraisal, and
here we discuss Marcus' core ideas, as well as attempt a general assessment of
the subject. This study examines some of the limitations of deep neural
networks, with the intention of pointing towards potential paths for future
research, and of clearing up some metaphysical misconceptions, held by numerous
researchers, that may misdirect them.
