From the paper: >> In this paper, we present an attempt at an architecture which operates on an explicit higher-level semantic representation, which we name a

From the paper: >> In this paper, we present an attempt at an architecture which operates on an explicit higher-level semantic representation, which we name a “concept”. I wonder if the many authors of the paper know that what they call "concept" is what all of machine learning and AI has also called a "concept" for many decades, and not a new thing that they have just named from scratch. For instance, classes of "concepts" are the target of learning in Leslie Valiant's "A Theory of the Learnable", the paper that introduced Probably Approximately Correct Learning (PAC-Learning). Quoting from its abstract: ABSTRACT: Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming. We give a precise methodology for studying this phenomenon from a computational viewpoint. It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learned using it in a reasonable (polynomial) number of steps. Although inherent algorithmic complexity appears to set serious limits to the range of concepts that can be learned, we show that there are some important nontrivial classes of propositional concepts that can be learned in a realistic sense From: https://web.mit.edu/6.435/www/Valiant84.pdf https://web.mit.edu/6.435/www/Valiant84.pdf Or take this Introduction to Chapter 2 in Tom Mitchell's "Machine Learning" (the original ML textbook, published 1997): This chapter considers concept learning: acquiring the definition of a general category given a sample of positive and negative training examples of the category. From: https://www.cs.cmu.edu/~tom/mlbook.html https://www.cs.cmu.edu/~tom/mlbook.html (clink link in "the book"). I mean I really wonder some times what is going on here. There's been decades of research in AI and machine learning but recently papers look like their authors have landed in an undiscovered country and are having to invent everything from scratch. That's not good. There are pitfalls that all the previous generations have explored thoroughly by falling in them time and again. Those who don't remember those lessons will have to find that out the hard way.