HOW DEEPMIND STRENGTHENS ECOLOGICAL RESEARCH USING MACHINE LEARNING?

15Apr - by aiuniverse - 0 - In Machine Learning

Source: analyticsinsight.net

In the past few years, machine learning has highlighted the strength of information technology in discovering the fundamentals of life and the environment. With the volume of data generated every day, it has become necessary to acknowledge smart analysis solutions to know more about the world we live in. In particular, machine learning aims to develop classifying languages simple enough to be understood easily by the human. Moreover, to get into layers of detailed study of ecology, Google’s DeepMind has collaborated with ecologists and conservationists to develop machine learning methods to help study the behavioral dynamics of an entire African animal community in the Serengeti National Park and Grumeti Reserve in Tanzania.

According to a DeepMind’s blog, the Serengeti-Mara ecosystem is globally unparalleled in its biodiversity, hosting an estimated 70 large mammal species and 500 bird species, thanks in part to its unique geology and varied habitat types. Around 10 years ago, the Serengeti Lion Research program installed hundreds of motion-sensitive cameras within the core of the protected area which is triggered by passing wildlife, capturing animal images frequently, across vast spatial scales, allowing researchers to study animal behavior, distribution, and demography with great spatial and temporal resolution.

This has allowed the team to collect and store millions of photos. To date, volunteers from across the world have helped to identify and count the species in the photos by hand using the Zooniverse web-based platform, which hosts many similar projects for citizen-scientists.

This comprehensive study has resulted in a rich dataset, Snapshot Serengeti, featuring labels and counts for around 50 different species. Moreover, to help researchers unlock this data with greater efficiency, DeepMind has used the Snapshot Serengeti dataset to train machine learning models to automatically detect, identify, and count animals.

DeepMind says, “Camera trap data can be hard to work with–animals may appear out of focus, and can be at many different distances and positions with respect to the camera. With expert input from leading ecologist and conservationist Dr. Meredith Palmer, our project quickly took shape, and we now have a model that can perform on par with, or better than, human annotators for most of the species in the region.” Most importantly, this method shortens the data processing pipeline by up to 9 months, which has immense potential to help researchers in the field.

In a more obvious manner the field work is quite challenging, and it is fraught with unexpected hazards such as failing power lines and limited or no internet access. DeepMind is preparing the software for deployment in the field and looking at ways to safely run its pre-trained model with modest hardware requirements and little Internet access. The company has worked closely with its collaborators in the field to be sure that its technology is used responsibly. Once in place, researchers in the Serengeti will be able to make direct use of this tool, helping provide them with up-to-date species information to better support their conservation efforts.

The DeepMind Science Team works to leverage AI to tackle key scientific challenges that impact the world. The company has developed a robust model for detecting and analyzing animal populations in-field data and has helped to consolidate data to enable the growing machine learning community in Africa to build AI systems for conservation which, it hopes, will scale to other parks. DeepMind says, “We’ll next be validating our models by deploying them in the field and tracking their progress. Our hope is to contribute towards making AI research more inclusive–both in terms of the kinds of domains we apply it to, and the people developing it. Hence, participating in meetings like Indaba is key for helping build a global team of AI practitioners who can deploy machine learning for diverse projects.”

Facebook Comments