My work pictured by AI – Kinkini Bhadra

What is this work about? Current successes of machine learning architectures are based on computationally expensive algorithms and prohibitively large amounts of data. We need to develop tasks and data to train networks to reach more complex and more compositional skills. In this paper, we illustrate Blackbird’s language matrices (BLMs), a novel grammatical task modelled on intelligence tests usually based on visual stimuli. The dataset is generatively constructed to support investigations of current models’ linguistic mastery and their ability to generalize them. We present the logic of the task, the method to automatically construct data on a large scale, and the architecture to learn them. Through error analysis and several experiments on variations of the dataset, we demonstrate that this language task and the data that instantiate it provide a new challenging testbed to understand generalization and abstraction.

The first word that came to mind when seeing the AI-generated picture? Goofy. 

What is this work about?  Over the last twenty years researchers have become more and more interested in the way non-human animals communicate in order to explore what such findings could potentially tell us about the development of our own language.  Through applying methods borrowed from computational linguistics, we were able to show that the very social common marmoset monkey strings calls together to form larger sequences up to nine calls of length. Superficially similar to the way we combine meaningful units, like words, into phrases or sentences, marmosets seem to follow a similar set of rules when stringing their calls together to form larger structures. We can conclude that the vocal systems of non-human animals might be built up in more complex ways than what we previously thought.

The first word that came to mind when seeing the AI-generated picture? Complexity.

What is this work about? Voice timbre – the unique acoustic information in a voice by which its speaker can be recognized – is particularly critical in mother-infant interaction. Vocal timbre is necessary for infants to recognize their mothers as familiar both before and after birth, providing a basis for social bonding between infant and mother. The exact mechanisms underlying infant voice recognition are unknown. Here, we show – for the first time – that mothers’ vocalizations contain more detail of their vocal timbre through adjustments to their voices known as infant-directed speech (IDS) or baby talk, resulting in utterances in which individual recognition is more robust. Using acoustic modelling (k-means clustering of Mel Frequency Cepstral Coefficients) of IDS in comparison with adult-directed speech (ADS), we found across a variety of languages from different cultures that voice timbre clusters in IDS are significantly larger to comparable clusters in ADS. This effect leads to a more detailed representation of timbre in IDS with subsequent benefits for recognition. Critically, an automatic speaker identification Gaussian-mixture model based on Mel Frequency Cepstral Coefficients showed significantly better performance when trained with IDS as opposed to ADS. We argue that IDS has evolved as part of a set of adaptive evolutionary strategies that serve to promote indexical signalling by caregivers to their offspring which thereby promote social bonding via voice and acquiring language.

Comment about the picture from the author? The study is about ‘voice recognition’ and the advantage that infant-directed speech offers in learning a voice. I am not sure someone would conclude this from looking at the pictures.

What is this work about? Joint actions require various coordination mechanisms in order to achieve a successful joint outcome. To understand how joint action evolved in humans, research requires a comparative approach by investigating whether other animals have similar motoric and mental coordination skills that would facilitate their joint actions. Common marmosets are cooperative breeders, just like humans, and thus their parental care system forms an ideal model to further investigate the different proximate mechanisms of joint action. This study focusses on infant transfers in marmosets, a highly important and risky joint action, for which both parents are required to coordinate efficiently. How marmosets exactly achieve a successful transfer, and what the relevant traits are, is unknown. To this end, we analyzed motor coordination during transfers, including micro-analyses of coordination signals such as touch and mutual gaze between parents. All our data was collected in captive housing as first stage of this project and we are developing a protocol for research in the field, to further understand how ecological conditions impact this behavior.

The first word that came to mind when seeing the AI-generated picture? Alliance.