Post provided by Katarina Meramo Bats are extraordinary animals. They fly, echolocate, and navigate in absolute darkness, and produce some of the most complex acoustic signals in the mammalian world. They pollinate, disperse seeds, control insect populations, and quietly hold ecosystems together. Yet, despite their importance, monitoring bats – particularly across large spatial and temporal scales – remains remarkably challenging. Over the past decade, bioacoustic … Continue reading Teaching Models to Listen to Bats: The Story Behind BSG-BATS
We have now entered the era of artificial intelligence. In just a few years, the number of applications using AI has grown tremendously, from self-driving cars to recommendations from your favourite streaming provider. Almost every major research field is now using AI. Behind all this, there is one constant: the reliance, in one way or another, on deep learning. Thanks to its power and flexibility, this new subset of AI approach is now everywhere, even in ecology we show in ‘Applications for deep learning in ecology’.
But what is deep learning exactly? What makes it so special?
Deep Learning: The Basics
Deep learning is a set of methods based on representation learning: a way for machines to automatically detect how to classify data from raw examples. This means they can detect features in data by themselves, without any prior knowledge of the system. While some models can learn without any supervision (i.e. they can learn to detect and classify objects without knowing anything about them) so far these models are outperformed by supervised models. Supervised models require labelled data to train. So, if we want the model to detect cars in pictures, it will need examples with cars in them to learn to recognise them.
There’s more information below on the Featured Articles selected by the Senior Editor and all of our freely available papers (Practical Tools and Applications articles are always free to access for everyone upon publication, whether you have a subscription or not). Continue reading “Issue 10.10: Conservation, Molecular Techniques, Stats and More”
Understanding animal movement and population size is a challenge for researchers studying any megafauna species. Sea turtles though, add a whole additional level of complexity. These wide-ranging, swift, charismatic animals spend much of their time underwater and in remote places. When trying to track down and count turtles, this obstacle to understanding population size becomes a full-on barricade.
For more than a decade, citizen science projects have helped researchers use the power of thousands of volunteers who help sort through datasets that are too large for a small research team. Previously, this data generally couldn’t be processed by computers because the work required skills that only humans could accomplish.
Now, computer machine learning techniques that teach the computer specific image recognition skills can be used in crowdsourcing projects to deal with massively increasing amounts of data—making computers a surprising new partner in citizen science projects.
Methods Blog 