A new system of algorithms enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles. The work brings researchers a step closer to building robots that can perform search and rescue missions or collect information in places that are too dangerous or difficult for humans.
Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on “edge devices” that work independently from central computing resources.
A team led by the University of California San Diego has developed a new system of algorithms that enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles.
A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm’s privacy loss remain open — even in the seemingly simple setting of smooth convex losses over …
Read more “Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss”
Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing …
Read more “FLAIR: Federated Learning Annotated Image Repository”
Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is …
Read more “Two-Layer Bandit Optimization for Recommendations”
A functional data ecosystem must incorporate notions of Ontology in order to be scalable and sustainable. Editor’s note: This is the first post in the Palantir RFx Blog Series, which breaks down some of the key pillars of a data ecosystem using language commonly found in formal solicitations such as RFIs and RFPs. Each post …
Read more “Ontology: Finding meaning in data (Palantir RFx Blog Series, #1)”
This series tackles the rarely simple and often messy solicitation process. We explore how organizations can better evaluate digital transformation software. Welcome to the RFx Blog Series, which explores the question: how should commercial organizations evaluate digital transformation software? In this series, we use language commonly found in formal solicitations, including specific questions and functional …
Read more “Evaluating Software (Palantir RFx Blog Series, #0)”
There are over 30 million people in America who have diabetes, and people with diabetes need to remain vigilant about their health. They need the extra attention and resources provided by their healthcare systems because, unfortunately, around 38% to 40% of people with diabetes end up visiting the ER due to complications. Healthcare organizations – …
Read more “Trustworthy AI helps provide equitable preventative care for diabetics”