The roadmap was probably planned early on, but the narrative could have been chosen later. I hadn't heard this story, but I wonder if both could be true. Not something decided early in the dev cycle. > I heard a story of some meeting where "no new features" was branded close to release.
Every OS since Lion has been replaced within a year. Tiger, Leopard and Snow Leopard had several years of life before they were replaced. I have another theory though, and it's not the price-Apple has plenty of money-it's the development cycle. Adding QuickLook to Finder doesn't do any of those things, it just allows you to use QuickLook. The program has also developed open-source tools to accelerate camera trap image analysis.> Rewriting Finder is huge and risky! When they replaced mDNSResponder with discoveryd it caused problems they eventually reverted it when Vint Cerf called Tim Cook to complain.īut it's not the size, it's the focus, right? Rewriting Finder, without adding any additional capabilities, reduces technical debt and makes Finder faster and more stable. The AI for Earth program connects researchers in environmental science with the AI and computing resources they need to accomplish their goals. Microsoft has devoted USD $50 million in grants to fund wildlife conservation. Microsoft AI for Earth invests in environmental science Using LIME, we can refine our classifier into a model that can detect the actual patterns of the leopard, without requiring human-annotated bounding boxes. To transform our classifier into something that could highlight the patterns of the leopard, we created a distributed implementation of the black box model interpretability technique, LIME. More explicitly, these methods require not just a leopard classifier, but a leopard detector. However, these tools often require well-behaved, cropped images of the target animal. To tackle this problem, we use tools like HotSpotter to identify individual leopards based on their spot patterns. More specifically, it is tough to distinguish between an ecosystem with many shy leopards, and one with a few curious leopards that like to take selfies. Simply classifying images of leopards is not enough to determine the number of leopards in the ecosystem. Lastly, we aggregate results over camera trap photo bursts to give the algorithm additional chances to spot a leopard in a batch of photos. Additionally, we add horizontal flips to our dataset to further improve robustness. We can use some of Bing’s collective intelligence by searching for images of leopards and images of empty hillsides to augment our dataset. First, we use the Azure Cognitive Services on Spark to embed large scale Bing Image Searches directly into Apache Spark. We augment our basic pipeline with several additional features to improve performance. Using Microsoft ML for Apache Spark, we can combine the accuracy and flexibility of deep models with the elastic scalability of Apache Spark to quickly featurise all images in the dataset and learn a classifier based on these features. In our workflow, we leverage ResNet50, a 50-layer deep convolutional network with residual connections that has been trained on the ImageNet classification challenge. To create a leopard classifier, we used a technique called transfer learning where we use a large general-purpose vision network for a more specific classification task. Deep Unsupervised Object Detection with Microsoft ML for Apache Spark These spots serve as social meeting points for leopards and play important roles in their communication. The team additionally created a live dashboard that highlights snow leopard hot spots. The Snow Leopard Trust used Microsoft AI to build a scalable image recognition program that is roughly 95% accurate in identifying snow leopards in camera trap photos.
Manually reviewing all images to find a snow-leopards could take thousands of hours of time.
Over 1 million images have been gathered, and camera traps add 500,000 images each year. Only about 5% of the pictures actually contain a leopard, which can be hard to spot due to their camouflage. Since the cameras trigger on any type of movement, most of the images are of goats, birds and grass blowing in the wind. Due to the cats’ remote habitat, expansive range and extremely elusive nature, researchers use motion-triggered camera traps to observe snow leopards in the wild. Despite their pivotal importance as this biome’s apex predator, we know very little about their numbers and behaviour. Snow Leopards are a highly threatened species, native to the steppes and mountainous terrain of Asia.