This paper serves as an initial announcement of the availability of a corpus of articulatory data called mngu0. This corpus will ultimately consist of a collection of multiple sources of articulatory data acquired from a single speaker: electromagnetic articulography (EMA), audio, video, volumetric MRI scans, and 3D scans of dental impressions. This data will be provided free for research use. In this first stage of the release, we are making available one subset of EMA data, consisting of more than 1,300 phonetically diverse utterances recorded with a Carstens AG500 electromagnetic articulograph. Distribution of mngu0 will be managed by a dedicated “forum-style ” web site. This paper both outlines the general goals motivating the distribution of the data and the creation of the mngu0 web forum, and also provides a description of the EMA data contained in this initial release.

[W]e may again recall what Einstein stressed: that given a sufficiently powerful formal assumption, a fertile and comprehensive theory may... be constructed without prior attention to the detailed facts, or even before they are known. L. L. Whyte, Internal Factors in Evolution, 1965 We outline a formalism for structural, or symbolic, representation, the necessity of which has been acutely felt in all sciences, particularly biology, for quite some time now. At the same time, biology has been gradually edging to the forefront of sciences, although the reasons obviously have nothing to do with its state of formalization or maturity—which is quite primitive as compared, for example, to that of physics. Rather, the reasons have to do with the growing realization that the objects of biology are not only more important and interesting, but that they also more explicitly exhibit the evolving nature of all objects in the Universe. It is this view of objects as evolving structural processes that we aim to address here, in contrast to the ubiquitous mathematical view of objects as points in some abstract space. One can gain an initial intuitive understanding of the proposed representation by generalizing the (Peano) process of construction of natural numbers: replace the single structureless unit out of which a number is built by multiple structural ones. An immediate but critical consequence of the distinguishability/multiplicity of units in the construction process is that we can now see which unit was attached and when. Hence, the resulting representation for the first time embodies temporal structural information in the form of a formative, or generative, history.

Abstract. My objective is to explain why a completely inadequate focus on the two central and inseparable concepts—the concepts of class (of objects) and class representation—is responsible for the lack of adequate progress in machine learning, and AI in general. I suggest that the main reason for this lack of progress is reliance on conventional formalisms, mainly the vector space and logical, which cannot in principle support a satisfactory concept of class. On the other hand, the orientation towards new, class-oriented, representational formalisms—if the underlying informational hypothesis about the nature of classes in the universe is vindicated—would establish machine learning as a new kind of natural science. 1

What is a structural representation?