Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their widespread adoption in affordable vehicles. Camera-based detectors are cheaper alternatives but often suffer inferior performance compared to their LiDAR-based counterparts due to inherent depth ambiguities in images. In this work, we seek to improve monocular 3D detectors by leveraging unlabeled historical LiDAR data. Specifically, at inference time, we assume that the camera-based detectors have access to multiple unlabeled LiDAR scans from past traversals at locations of interest (potentially from other high-end vehicles equipped with LiDAR sensors). Under this setup, we proposed a novel, simple, and end-to-end trainable framework, termed AsyncDepth, to effectively extract relevant features from asynchronous LiDAR traversals of the same location for monocular 3D detectors. We show consistent and significant performance gain (up to 9 AP) across multiple state-of-the-art models and datasets with a negligible additional latency of 9.66 ms and a small storage cost.

To solve the most pressing scientific problems, scientists today often face enormous hurdles when it comes to gathering the data they need to embark on research. Enter Ramkumar Hariharan, a data scientist and computational biologist at Northeastern University in Seattle. A scientist and an engineer, Hariharan's current research is centered around an emerging scientific field called geroscience, or the "study of aging as it relates to age-related diseases." Hariharan has been trying to understand the reasons why some cancer patients respond better to certain kinds of immunotherapies. To do so requires lots of information about the patients themselves, the specific forms of cancer and the drugs used to treat patients.

Advances in machine learning, data management, and cloud computing are having a significant impact on the market for drone-based mapping and intelligence gathering. Even as satellite-based imaging gains steam, drones appear to be extending their lead closer to Earth. We are in the midst of a renaissance in drone-based aerial intelligence. From counting the number of koalas in the Australian outback to detecting enemy combatants inside of buildings, drones seem to be everywhere at the moment. The surge in drone use is great news for Krishnan Hariharan, the CEO of Kespry, a 30-person California drone AI startup.

The term last-mile problem comes from the telecom industry, which observed that it costs inordinately more to build and manage the last-mile of infrastructure to the home than to bring infrastructure to the hub city or residential perimeter. Businesses are starting to discover a similar last-mile delivery problem in AI: It is much harder to weave AI technologies into business processes that actually run companies than it is to build or buy the AI and machine learning (ML) models that promise to improve those processes. "The path to deploying ML is still expensive," said Ian Xiao, manager at Deloitte Omnia, Deloitte Canada's AI consulting practice. He estimates that most companies deploy only between 10% and 40% of their machine learning projects depending on their size and technology readiness. In fact, the last-mile problem is a bit of a misnomer when applied to AI deployment in the enterprise.

You are free to share this article under the Attribution 4.0 International license. Researchers have developed a Minecraft modification that uses artificial intelligence to help players improve their in-game architecture skills. Minecraft is a popular 3D video game where players build and navigate their own digital environments. The modification will tell players whether their buildings fit into certain architectural styles and offer ideas for how the structures could be improved. "One of the things that's important to learn when you're a kid and throughout life is creativity, abstraction--how to envision what you want and then create it," says senior author Ross Knepper, assistant professor of computer science at Cornell University.