Louis Mahon

Communication structures society, and is likewise shaped by relationships and shared tasks; yet, for most socially complex species, we know little of their full vocal repertoire and its functions. We investigated how communication structures the who, what, and when of social interactions in cooperative carrion crows – group-living birds who rely on coordinated behaviors, as in chick care. Leveraging machine learning to integrate large-scale data from crow-borne audio-loggers and nest cameras, we charted the vocal repertoire across 24 cooperative groups and mapped all discovered call types to behaviors and social context. We found that crows used a rich repertoire across three domains of joint behavior – flocking, chick care, and territorial display. Relatively quiet call types were abundant and included close-range calls that may coordinate chick care by announcing nest visits. Our study demonstrates how combining continuous-capture data and machine learning can reveal a holistic understanding of how vocalizations function across contexts.

Clustering is a widely used and powerful machine learning technique, but its effectiveness is often limited by the need to specify the number of clusters, k, or by relying on thresholds that implicitly determine k. We introduce k*-means, a novel clustering algorithm that eliminates the need to set k or any other parameters. Instead, it uses the minimum description length principle to automatically determine the optimal number of clusters, k*, by splitting and merging clusters while also optimising the standard k-means objective. We prove that k*-means is guaranteed to converge and demonstrate experimentally that it significantly outperforms existing methods in scenarios where k is unknown. We also show that it is accurate in estimating k, and that empirically its runtime is competitive with existing methods, and scales well with dataset size.

Data complexity is an important concept in the natural sciences and related areas, but lacks a rigorous and computable definition. This paper focusses on a particular sense of complexity that is high if the data is structured in a way that could serve to communicate a message. In this sense, human speech, written language, drawings, diagrams and photographs are high complexity, whereas data that is close to uniform throughout or populated by random values is low complexity. I describe a general framework for measuring data complexity based on dividing the shortest description of the data into a structured and an unstructured portion, and taking the size of the former as the complexity score. I outline an application of this framework in statistical mechanics that may allow a more objective characterisation of the macrostate and entropy of a physical system. Then, I derive a more precise and computable definition geared towards human communication, by proposing local compositionality as an appropriate specific structure. Experimental evaluation shows that this method can distinguish meaningful signals from noise or repetitive signals in auditory, visual and text domains, and could potentially help determine whether an extra-terrestrial signal contained a message.

This paper presents FASTFOOD, a rule-based natural language generation (NLG) program for cooking recipes. We consider the representation of cooking recipes as discourse representation, because the meaning of each sentence needs to consider the context of the others. Our discourse representation system is based on states of affairs and transtions between states of affairs, and does not use discourse referents. Recipes are generated by using an automated theorem-proving procedure to select the ingredients and instructions, with ingredients corresponding to axioms and instructions to implications. FASTFOOD also contains a temporal optimization module which can rearrange the recipe to make it more time efficient for the user, e.g. the recipe specifies to chop the vegetables while the rice is boiling. The system is described in detail, including the decision to forgo discourse referents and how plausible representations of nouns and verbs emerge purely as a by-product of the practical requirements of efficiently representing recipe content. A comparison is then made with existing recipe generation systems, NLG systems more generally, and automated theorem provers.