: Deep Learning techniques are underlying many amazing accomplishments in artificial intelligence and machine learning. Their theory does not match empirical achievements, but the applicable results have largely been in favor of DL. In our recently published paper [1], we question this belief. In the context of autoencoding, i.e., nonlinear dimension encoding-decoding, we propose a new, additive model that strictly separates approximation of bias, linear behavior, and nonlinear behavior. With this approximation, we encountered no help or even need of deeper network structures to encapsulate nonlinear behavior. We also witnessed worse data reconstruction results when typical data-batch driven optimization techniques were applied to train the additive autoencoder. It would be really an interesting endeavor to address the underlying reasons of the observed behavior of our extensive set of empirical experiments. [1] Kärkkäinen, T., & Hänninen, J. (2023). Additive autoencoder for dimension estimation. Neurocomputing, Volume 551, 126520. Biography: Tommi Kärkkäinen (TK) received the Ph.D. degree in Mathematical Information Technology from the University of Jyväskylä (JYU), in 1995. Since 2002 he has been serving as a full professor of Mathematical Information Technology at the Faculty of Information Technology (FIT), JYU. TK has led 50 different R&D projects and has been supervising over 60 PhD students. He has published over 200 peer-reviewed articles and received the Innovation Prize of JYU in 2010. He has served in many administrative positions at FIT and JYU, currently leading a Research Division and a Research Group on Human and Machine based Intelligence in Learning. The main research interests include data mining, machine learning, learning analytics, and nanotechnology. He is a senior member of the IEEE.