From bonobos and rats to tickling robots, research is finally cracking the secrets of why we're ticklish, and what that reveals about our brains In a grey-walled room in the Dutch city of Nijmegen, a strange activity is underfoot. Wearing a cap covered in sensors and positioning themselves into a chair, a person places their bare feet over two holes in a platform. Beneath this lies a robot, which uses a metal probe to begin to tickle their soles. Here, at Radboud University's Touch and Tickle lab, volunteers are being mercilessly tickled in the name of science. "We can manipulate how strong the stimulation is, how fast and where it is going to be applied on your foot," says Konstantina Kilteni, who runs the lab, of the robot tickling experiment.

Passive acoustic monitoring is a sustainable method of monitoring wildlife and environments that leads to the generation of large datasets and, currently, a processing backlog. Academic research into automating this process is focused on the application of resource intensive convolutional neural networks which require large pre-labelled datasets for training and lack flexibility in application. We present a viable alternative relevant in both wild and captive settings; a transparent, lightweight and fast-to-train associative memory AI model with Hopfield neural network (HNN) architecture. Adapted from a model developed to detect bat echolocation calls, this model monitors captive endangered black-and-white ruffed lemur (Varecia variegata) vocalisations. Lemur social calls of interest when monitoring welfare are stored in the HNN in order to detect other call instances across the larger acoustic dataset. We make significant model improvements by storing an additional signal caused by movement and achieve an overall accuracy of 0.94. The model can perform 340 classifications per second, processing over 5.5 hours of audio data per minute, on a standard laptop running other applications. It has broad applicability and trains in milliseconds. Our lightweight solution reduces data-to-insight turnaround times and can accelerate decision making in both captive and wild settings.

Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks. This article presents a comprehensive review of large-scale pretrained bioacoustic foundation models and systematically investigates their transferability across multiple bioacoustic classification tasks. We overview bioacoustic representation learning including major pretraining data sources and benchmarks. On this basis, we review bioacoustic foundation models by thoroughly analysing design decisions such as model architecture, pretraining scheme, and training paradigm. Additionally, we evaluate selected foundation models on classification tasks from the BEANS and BirdSet benchmarks, comparing the generalisability of learned representations under both linear and attentive probing strategies. Our comprehensive experimental analysis reveals that BirdMAE, trained on large-scale bird song data with a self-supervised objective, achieves the best performance on the BirdSet benchmark. On BEANS, BEATs$_{NLM}$, the extracted encoder of the NatureLM-audio large audio model, is slightly better. Both transformer-based models require attentive probing to extract the full performance of their representations. ConvNext$_{BS}$ and Perch models trained with supervision on large-scale bird song data remain competitive for passive acoustic monitoring classification tasks of BirdSet in linear probing settings. Training a new linear classifier has clear advantages over evaluating these models without further training. While on BEANS, the baseline model BEATs trained with self-supervision on AudioSet outperforms bird-specific models when evaluated with attentive probing. These findings provide valuable guidance for practitioners selecting appropriate models to adapt them to new bioacoustic classification tasks via probing.

We consider the problem of detecting, isolating and classifying elephant calls in continuously recorded audio. Such automatic call characterisation can assist conservation efforts and inform environmental management strategies. In contrast to previous work in which call detection was performed at a segment level, we perform call detection at a frame level which implicitly also allows call endpointing, the isolation of a call in a longer recording. For experimentation, we employ two annotated datasets, one containing Asian and the other African elephant vocalisations. We evaluate several shallow and deep classifier models, and show that the current best performance can be improved by using an audio spectrogram transformer (AST), a neural architecture which has not been used for this purpose before, and which we have configured in a novel sequence-to-sequence manner. We also show that using transfer learning by pre-training leads to further improvements both in terms of computational complexity and performance. Finally, we consider sub-call classification using an accepted taxonomy of call types, a task which has not previously been considered. We show that also in this case the transformer architectures provide the best performance. Our best classifiers achieve an average precision (AP) of 0.962 for framewise binary call classification, and an area under the receiver operating characteristic (AUC) of 0.957 and 0.979 for call classification with 5 classes and sub-call classification with 7 classes respectively. All of these represent either new benchmarks (sub-call classifications) or improvements on previously best systems. We conclude that a fully-automated elephant call detection and subcall classification system is within reach. Such a system would provide valuable information on the behaviour and state of elephant herds for the purposes of conservation and management.

If an unexpected meow, peculiar pose, or unusual twitch of the whiskers leaves you puzzling over what your cat is trying to tell you, artificial intelligence may soon be able to translate. Scientists are turning to new technology to unpick the meanings behind the vocal and physical cues of a host of animals. "We could use AI to teach us a lot about what animals are trying to say to us," said Daniel Mills, a professor of veterinary behavioural medicine at the University of Lincoln. Previous work, including by Mills, has shown that cats produce a variety of facial expressions when interacting with humans, and this week researchers revealed felines have a range of 276 facial expressions when interacting with other cats. "However, the facial expressions they produce towards humans look different from those produced towards cats," said Dr Brittany Florkiewicz, an assistant professor of psychology at Lyon College in Arkansas who co-authored the new work.

Rats not only enjoy being tickled by humans, but they do happy little jumps when watching other rats get tickled, a new study shows. In experiments in Germany, rats were filmed while watching an experimenter tickle another rat on the other side of a transparent barrier. When rats saw others get tickled, they experienced something known as'Freudensprünge' – a German term meaning'to jump for joy'. What's more, the mere observation of others being tickled induced'laughter' in the rats, inaudible to the human ear. Researchers in Germany made rats watch others being tickled and studied their responses.

A dolphin handler makes the signal for "together" with her hands, followed by "create". The two trained dolphins disappear underwater, exchange sounds and then emerge, flip on to their backs and lift their tails. They have devised a new trick of their own and performed it in tandem, just as requested. "It doesn't prove that there's language," says Aza Raskin. "But it certainly makes a lot of sense that, if they had access to a rich, symbolic way of communicating, that would make this task much easier."

Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a need to annotate very long recordings but the expert annotator time is limited. This paper presents an overview of the second edition of the few-shot bioacoustic sound event detection task included in the DCASE 2022 challenge. A detailed description of the task objectives, dataset, and baselines is presented, together with the main results obtained and characteristics of the submitted systems. This task received submissions from 15 different teams from which 13 scored higher than the baselines. The highest F-score was of 60% on the evaluation set, which leads to a huge improvement over last year's edition. Highly-performing methods made use of prototypical networks, transductive learning, and addressed the variable length of events from all target classes. Furthermore, by analysing results on each of the subsets we can identify the main difficulties that the systems face, and conclude that few-show bioacoustic sound event detection remains an open challenge.

Every lion has its own unique roar, one that lets the'kings of the jungle' recognise each other and could be used to track population movements, a study has found. Researchers from Oxford used machine learning to analyse the roars of various lions -- picking out the distinguishing frequency that can be used to tell them apart. According to the experts, lions' calls are usually issued in a set -- with one or two soft moans followed by several loud, full-throated roars and finishing with grunts. Previous research had suggested that lions could distinguish their peer's roars from each other -- allowing them to identify distant friends and hostile neighbours. However, it had not previously been clear what aspects of the calls' structure allowed them to discriminate between those made by different individuals.

In April, it was reported that 69-year‑old Tom Patterson, an American who fell gravely ill with an antibiotic-resistant acinetobacter infection, had been brought out of a two-month coma by an injected cocktail of bacteriophages, tiny viruses that specifically attack and kill bacteria. The story is a testament to Patterson's wife (Steffanie Strathdee, a scientist), who searched for alternative therapies when conventional treatments failed, to his physician, Robert Schooley, who used an untested treatment, and to a large band of phage scientists, led by Ryland Young of Texas A&M University and Theron Hamilton of the US Naval Academy. Their long-term, and sometimes unfashionable, research work meant that phages were available in their labs for the rescue attempt. Because a mixed-phage cocktail was used, no one is sure what tipped the balance, but, importantly, it worked. The Eliava Institute in Tbilisi, Georgia has dispensed phage therapy for years, but it was little tried in the west until recently.