Why is it that we still get a tiny thrill from checking the mailbox each day? Rationally, we know what's in there: bills we don't want, catalogs we never ordered, and that bulky Valpak stuffed with coupons we'll never use. But somehow, despite the noise, there's a quiet hope we might find something meaningful. And every once in a while, we do. In a society obsessed with social media, texts, AI, speed and automation, the handwritten thank-you note has become an endangered species.

This paper presents an automated one-shot bird call classification pipeline designed for rare species absent from large publicly available classifiers like BirdNET and Perch. While these models excel at detecting common birds with abundant training data, they lack options for species with only 1-3 known recordings-a critical limitation for conservationists monitoring the last remaining individuals of endangered birds. To address this, we leverage the embedding space of large bird classification networks and develop a classifier using cosine similarity, combined with filtering and denoising preprocessing techniques, to optimize detection with minimal training data. We evaluate various embedding spaces using clustering metrics and validate our approach in both a simulated scenario with Xeno-Canto recordings and a real-world test on the critically endangered tooth-billed pigeon (Didunculus strigirostris), which has no existing classifiers and only three confirmed recordings. The final model achieved 1.0 recall and 0.95 accuracy in detecting tooth-billed pigeon calls, making it practical for use in the field. This open-source system provides a practical tool for conservationists seeking to detect and monitor rare species on the brink of extinction.

The company behind Roomba robovacs told investors earlier this week that revenue was substantially down and it's struggling to pay its debts. Amazon was briefly tapped to acquire the robot company iRobot, but the threat of a European Commission investigation led to the retailer terminating the deal -- apparently happy enough to pay off the 94 million termination fee. That, however, isn't enough to tackle the 200 million loan iRobot took out to survive long enough for Amazon to come to the rescue. It's extra rough when the company announced, just the week before, a bunch of new models, including a new Roomba that can compact debris and dust, so it only needs to be emptied every few weeks. At the same time, rival robot vacuum cleaners are getting more versatile, more complicated and more intriguing.

Machine learning analysis of data gathered by acoustic recording devices is a promising new tool for monitoring the marbled murrelet and other secretive, hard-to-study species, research by Oregon State University and the U.S. Forest Service has shown. The threatened marbled murrelet is an iconic Pacific Northwest seabird that's closely related to puffins and murres, but unlike those birds, murrelets raise their young as far as 60 miles inland in mature and old-growth forests. "There are very few species like it," said co-author Matt Betts of the OSU College of Forestry. "And there's no other bird that feeds in the ocean and travels such long distances to inland nest sites. This behavior is super unusual and it makes studying this bird really challenging."

The post-2020 global biodiversity framework needs ambitious, research-based targets. Estimating the accelerated extinction risk due to climate change is critical. The International Union for Conservation of Nature (IUCN) measures the extinction risk of species. Automatic methods have been developed to provide information on the IUCN status of under-assessed taxa. However, these compensatory methods are based on current species characteristics, mainly geographical, which precludes their use in future projections. Here, we evaluate a novel method for classifying the IUCN status of species benefiting from the generalisation power of species distribution models based on deep learning. Our method matches state-of-the-art classification performance while relying on flexible SDM-based features that capture species' environmental preferences. Cross-validation yields average accuracies of 0.61 for status classification and 0.78 for binary classification. Climate change will reshape future species distributions. Under the species-environment equilibrium hypothesis, SDM projections approximate plausible future outcomes. Two extremes of species dispersal capacity are considered: unlimited or null. The projected species distributions are translated into features feeding our IUCN classification method. Finally, trends in threatened species are analysed over time and i) by continent and as a function of average ii) latitude or iii) altitude. The proportion of threatened species is increasing globally, with critical rates in Africa, Asia and South America. Furthermore, the proportion of threatened species is predicted to peak around the two Tropics, at the Equator, in the lowlands and at altitudes of 800-1,500 m.

Although increasing threats on biodiversity are now widely recognised, there are no accurate global maps showing whether and where species assemblages are at risk. We hereby assess and map at kilometre resolution the conservation status of the iconic orchid family, and discuss the insights conveyed at multiple scales. We introduce a new Deep Species Distribution Model trained on 1M occurrences of 14K orchid species to predict their assemblages at global scale and at kilometre resolution. We propose two main indicators of the conservation status of the assemblages: (i) the proportion of threatened species, and (ii) the status of the most threatened species in the assemblage. We show and analyze the variation of these indicators at World scale and in relation to currently protected areas in Sumatra island. Global and interactive maps available online show the indicators of conservation status of orchid assemblages, with sharp spatial variations at all scales. The highest level of threat is found at Madagascar and the neighbouring islands. In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island. Recent advances in deep learning enable reliable mapping of the conservation status of species assemblages on a global scale. As an umbrella taxon, orchid family provides a reference for identifying vulnerable ecosystems worldwide, and prioritising conservation actions both at international and local levels.

Energetic bear cubs play with rehabilitation staff after arriving at a wildlife center. African conservationists are hoping that artificial intelligence (AI) powered cameras could help aid in the protection of endangered species, such as the forest and savannah elephants. "We must urgently put an end to poaching and ensure that sufficient suitable habitat for both forest and savanna elephants is conserved," Dr. Bruno Oberle, Director-General of the International Union for the Conservation of Nature (IUCN), said when discussing the potential new technology. The cameras, developed in collaboration between Dutch tech start-up Hack the Planet and British scientists at Stirling University, will be able to detect different animal species and humans in real time and provide live alerts to local villages and rangers, Stirling wrote in a press release. A pilot test of the tech, which works with satellites and a range of networks including Wi-Fi, long-rage radio and cellular coverage, immediately labeled images and sent out warnings calling for help.

There are thousands of species on Earth that we still don't know much about -- but we now know that they are already teetering on the edge of extinction. A new study used machine learning to figure out just how threatened these lesser-known species are, and the results were grim. Some species of animals and plants are labeled "data deficient" because conservationists haven't been able to gather enough information about them to understand how they live or how many of them are left. It turns out that those "data deficient" species are unfortunately even more threatened than other species that are more well known (to scientists, at least). The data from this study came from the International Union for Conservation of Nature (IUCN), which maintains a global "Red List" that ranks species based on how threatened they are.

It may seem intuitive that AI and deep learning can speed up workflows -- including novel drug discovery, a typically years-long and several-billion-dollar endeavor. But professors Artem Cherkasov and Olexandr Isayev were surprised to find that no recent academic papers provided a comprehensive, global research review of how deep learning and GPU-accelerated computing impact drug discovery. In March, they published a paper in Nature to fill this gap, presenting an up-to-date review of the state of the art for GPU-accelerated drug discovery techniques. Cherkasov, a professor in the department of urologic sciences at the University of British Columbia, and Isayev, an assistant professor of chemistry at Carnegie Mellon University, join NVIDIA AI Podcast host Noah Kravitz this week to discuss how GPUs can help democratize drug discovery. In addition, the guests cover their inspiration and process for writing the paper, talk about NVIDIA technologies that are transforming the role of AI in drug discovery, and give tips for adopting new approaches to research.

A small mouse stands on two paws as it lifts its nose to sniff the air; colorful fish with shifting patterns drift underwater; flowers bloom and sway on tall, delicate stems. The painterly flora and fauna of Sofia Crespo's Critically Extant are based on real, critically endangered species like the Perote Deer Mouse, the Mekong Giant Catfish, or Parakaempferia synantha (a plant in the ginger family). Crespo trained AI algorithms on millions of open source images of approximately ten thousand species, using the resulting models to generate visual representations of animals and plants that -- despite being endangered -- have little or no presence on social media or in the broader public discourse. The generated representations appear slightly uncanny in form, in part due to the limitations of AI, but primarily due to the lack of man-made input available. How can an algorithm perfectly recreate an animal when it has too few examples of what that animal looks like?