In both 2016 and 2018, a census of the highly-endangered Grevy's zebra population was enabled by the Great Grevy's Rally (GGR), a citizen science event that produces population estimates via expert and algorithmic curation of volunteer-captured images. A complementary, scalable, and long-term Grevy's population monitoring approach involves deploying camera trap networks. However, in both scenarios, a substantial majority of zebra images are not usable for individual identification due to poor in-the-wild imaging conditions; camera trap images in particular present high rates of occlusion and high spatio-temporal similarity within image bursts. Our proposed filtering pipeline incorporates animal detection, species identification, viewpoint estimation, quality evaluation, and temporal subsampling to obtain individual crops suitable for re-ID, which are subsequently curated by the LCA decision management algorithm. Our method processed images taken during GGR-16 and GGR-18 in Meru County, Kenya, into 4,142 highly-comparable annotations, requiring only 120 contrastive human decisions to produce a population estimate within 4.6% of the ground-truth count. Our method also efficiently processed 8.9M unlabeled camera trap images from 70 cameras at the Mpala Research Centre in Laikipia County, Kenya over two years into 685 encounters of 173 individuals, requiring only 331 contrastive human decisions.

This paper combines deep learning techniques for species detection, 3D model fitting, and metric learning in one pipeline to perform individual animal identification from photographs by exploiting unique coat patterns. This is the first work to attempt this and, compared to traditional 2D bounding box or segmentation based CNN identification pipelines, the approach provides effective and explicit view-point normalisation and allows for a straight forward visualisation of the learned biometric population space. Note that due to the use of metric learning the pipeline is also readily applicable to open set and zero shot re-identification scenarios. We apply the proposed approach to individual Grevy's zebra (Equus grevyi) identification and show in a small study on the SMALST dataset that the use of 3D model fitting can indeed benefit performance. In particular, back-projected textures from 3D fitted models improve identification accuracy from 48.0% to 56.8% compared to 2D bounding box approaches for the dataset. Whilst the study is far too small accurately to estimate the full performance potential achievable in larger-scale real-world application settings and in comparisons against polished tools, our work lays the conceptual and practical foundations for a next step in animal biometrics towards deep metric learning driven, fully 3D-aware animal identification in open population settings. We publish network weights and relevant facilitating source code with this paper for full reproducibility and as inspiration for further research.

Imagine a world where a farmer's smart phone predicts the perfect day to harvest. Or a governor can dial up exactly how to enhance food security prior to a hurricane. It would take seamless access to a highly technical artificial intelligence (AI) infrastructure, but Ohio State's Dhabaleswar K. (DK) Panda is working to get us there. "If you look at AI, it's become very important but it is limited to only advanced technical people," said Panda, professor of computer science and engineering at Ohio State. "How do we take it to the masses? We want to create a plug-and-play AI that will be democratized so anybody can use it."

A new 30-foot tower has sprouted on the edge of The Ohio State University Airport, but it has nothing to do with directing the thousands of planes that take off and land there each year. Instead, this tower is the focal point of an Ohio State research project that will explore using artificial intelligence and a variety of sensors to monitor environmental conditions on a minute-to-minute basis. A key part of the project is the use of machine learning to interpret the data as it is collected, said Tanya Berger-Wolf, director of Ohio State's Translational Data Analytics Institute (TDAI) and the leader of the project. "This is a unique opportunity for our researchers to help understand environmental conditions in urban areas, such as carbon emissions, noise and air pollution, and how it changes in real time," Berger-Wolf said. "We will use artificial intelligence and machine learning models to take all the complex information we collect and get insight out of the data, such as the impact of emissions from the airplanes on the local environment."

From developing drug treatments to predicting the next hotspot, artificial intelligence may help researchers, healthcare workers, and everyday people offset the impact of the coronavirus. As the worldwide fight against coronavirus COVID-19 continues, companies and governments around the world are pulling out all the stops in an effort to stave off the pandemic's worst impacts. One tool in that toolbox that might prove particularly useful is artificial intelligence (AI). Even though AI has been around since the 1960s, it's only been in the past few years that its adoption outside of science labs and research institutions has really taken off. Perhaps the most common application of AI people have come into contact with today are virtual assistants like Apple's Siri and Amazon's Alexa, which rely on natural language processing (NLP) algorithms to understand human speech.

From developing drug treatments to predicting the next hotspot, artificial intelligence may help researchers, healthcare workers, and everyday people offset the impact of the coronavirus. As the worldwide fight against coronavirus COVID-19 continues, companies and governments around the world are pulling out all the stops in an effort to stave off the pandemic's worst impacts. One tool in that toolbox that might prove particularly useful is artificial intelligence (AI). Even though AI has been around since the 1960s, it's only been in the past few years that its adoption outside of science labs and research institutions has really taken off. Perhaps the most common application of AI people have come into contact with today are virtual assistants like Apple's Siri and Amazon's Alexa, which rely on natural language processing (NLP) algorithms to understand human speech.

We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches. Here, we present a new framework to take into account the social media bias when using this data source to provide wildlife population size estimates. We show that, surprisingly, this is a learnable and potentially solvable problem.

Time series data is common in a wide range of disciplines including finance, biology, sociology, and computer science. Analyzing and modeling time series data is fundamental for studying various problems in those fields. For instance, studying time series physiological data can be used to discriminate patients’ abnormal recovery trajectories and normal ones (Hripcsak, Albers, and Perotte 2015). GPS data are useful for studying collective decision making of groupliving animals (Strandburg-Peshkin et al. 2015). There are different methods for studying time series data such as clustering, regression, and anomaly detection. In this proposal, we are interested in structured prediction problems in time series data. Structured prediction focuses on prediction task where the outputs are structured and interdependent, contrary to the non-structured prediction which assumes that the outputs are independent of other predicted outputs. Structured prediction is an important problem as there are structures inherently existing in time series data. One difficulty for structured prediction is that the number of possible outputs can be exponential which makes modeling all the potential outputs intractable.

Rather than leading to the violent downfall of humankind, artificial intelligence is helping people around the world do their jobs, including doctors who diagnose sepsis in patients and scientists who track endangered animals in the wild, experts said Thursday (Oct. Advancements in the field of artificial intelligence (AI) haven't always been met with enthusiasm. Famed astrophysicist Stephen Hawking warned on several occasions that a fully developed AI could destroy the human race, and Hollywood sci-fi movies are rife with fierce robots battling humans for control. But at yesterday's conference -- attended by the country's leading researchers, innovators, entrepreneurs and students -- scientists explained how newly developed AI is accelerating research and improving lives. Here is a look at five AI inventions that are already redefining technology.

Rather than leading to the violent downfall of humankind, artificial intelligence is helping people around the world do their jobs, including doctors who diagnose sepsis in patients and scientists who track endangered animals in the wild, experts said Thursday (Oct. Advancements in the field of artificial intelligence (AI) haven't always been met with enthusiasm. Famed astrophysicist Stephen Hawking warned on several occasions that a fully developed AI could destroy the human race, and Hollywood sci-fi movies are rife with fierce robots battling humans for control. But at Thursday's conference -- attended by the country's leading researchers, innovators, entrepreneurs and students -- scientists explained how newly developed AI is accelerating research and improving lives. Here is a look at five AI inventions that are already redefining technology.