The principle of maximum entropy, developed more than six decades ago, provides a systematic approach to modeling inference, and data analysis grounded in the principles of information theory, Bayesian probability and constrained optimization. Since its formulation, criticisms about the consistency of that method and the role of constraints have been raised. Among these, the chief criticism is that maximum entropy does not satisfy the principle of causation, or similarly, that maximum entropy updating is inconsistent due to an inadequate representation of causal information. We show that these criticisms rest on misunderstanding and misapplication of the way constraints have to be specified within the maximum entropy method. Correction of these problems eliminates the seeming paradoxes and inconsistencies critics claim to have detected. We demonstrate that properly formulated maximum entropy models satisfy the principle of causation.

Several recent projects have introduced digital machines to the kitchen, yet their impact on culinary culture is limited. We envision a culture of Digital Gastronomy that enhances traditional cooking with new interactive capabilities, rather than replacing the chef with an autonomous machine. Thus, we deploy existing digital fabrication instruments in traditional kitchen and integrate them into cooking via hybrid recipes. This concept merges manual and digital procedures, and imports parametric design tools into cooking, allowing the chef to personalize the tastes, flavors, structures and aesthetics of dishes. In this paper we present our hybrid kitchen and the new cooking methodology, illustrated by detailed recipes with degrees of freedom that can be set digitally prior to cooking. Lastly, we discuss future work and conclude with thoughts on the future of hybrid gastronomy.

In this paper, we present an abstract model of visualization and inference processes, and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.