This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: In recent years, numerous attempts have been made to use artificial intelligence to decipher the communication of bats, whales, birds and other animals. However, a new study led by a team of researchers from Tel Aviv University points to a fundamental problem with this approach: AI models focus on the physical properties of a sound, but this does not mean they understand the meaning attributed to it by the animal receiving it.
According to the researchers, sounds that are acoustically similar do not necessarily carry similar meanings, while sounds that appear different may convey the same information to the receiver. Therefore, classifying sounds according to their acoustic similarity, as is done in most studies, may create a misleading picture of the communication system and the meaning of the messages it conveys. The study, published in the scientific journal Current Biology, was conducted by Mor Taub, Inbal Arnon, Amiyaal Ilany, Mirjam Knörnschild, Yoav Ram and Yossi Yovel.
The research team included scientists from Tel Aviv University, the Hebrew University of Jerusalem, the University of Edinburgh, the Museum für Naturkunde—Leibniz Institute for Evolution and Biodiversity Science, and Humboldt-Universität zu Berlin. To investigate the problem, the researchers used a unique communication system: the vocalizations of human toddlers who have not yet fully developed speech. Unlike animal vocalizations, in this case the researchers can determine, at least to some extent, how the humans to whom the vocalizations are directed interpret them.
The recordings included vocalizations made in three contexts: distress, calling to a specific person—the mother or father—and requesting food. The researchers analyzed the recordings using a classical acoustic method and two state-of-the-art deep neural networks: one trained on animal vocalizations and another trained on adult human speech. The models were asked to group the vocalizations according to their characteristics.
The results showed that the deep neural networks performed better than the classical acoustic method, but even they failed to classify the toddlers' vocalizations according to their meaning. In some cases, they grouped together vocalizations carrying different messages; in others, they separated different vocalizations intended to convey the same message. The models also failed to identify how a sequence of vocalizations expressed increasing urgency, a distinction that the human ear perceives naturally.
According to the researchers, reliably deciphering animal communication will require combining AI tools with behavioral observations, playback experiments and sometimes measurements of brain activity. Every species has its own unique perceptual world, and understanding what animals are "saying" therefore requires more than analyzing sound alone: It also requires examining how they hear the sound and respond to it. Yovel concludes, "In recent years, there has been growing excitement about the possibility of using artificial intelligence to decode animal communication, but our study shows that these promises should be treated with caution.
Extract — continue reading at the source.
