Training software that emulates brain networks to identify dog breeds or sports equipment is by now old news. But getting such an AI network to learn a process on its own that is innate to early child development is truly novel. In a paper published Wednesday in Science Advances, a neural network distinguished between different quantities of things, even though it was never taught what a number is. The neural net reprised a cognitive skill innate to human babies, monkeys and crows, among others. Without any training, it suddenly could tell the difference between larger and smaller amounts--a skill called numerosity, or number sense.

Pacemaker-ID is a simple application that analyzes and identifies Boston Scientific, Biotronik, Medtronic, and St. Jude pacemakers and defibrillators. See our abstract in the Journal of the American College of Cardiology.

AI technology has become increasingly sophisticated in recent years. So many products and services now rely on the technology to provide automation and intelligence that it is deeply and irrevocably intertwined with our everyday world. Whether through devices we use to enable convenience at home or in the way products we use all the time are manufactured, its impact is everywhere, driving innovation in just about every aspect of our lives. But there are missing pieces to this puzzle that still cause frustration for end-users and present significant challenges for researchers trying to improve how AI technology performs. A common sense approach Before his passing in 2018, Microsoft co-founder Paul Allen dedicated an admirable amount of time and resources to solving an essential challenge that seems to come up again and again: The fundamental lack of common sense in AI technologies.

Researchers from Alphabet-owned company DeepMind say a new AI can ingest a patient's medical history and predict, with 90 percent accuracy, whether they're going to need dialysis for acute kidney injury 48 hours before it occurs. "Currently we pick these things up too late and harm is caused to patients, and we think there's a real opportunity for these AI systems to be able to predict and prevent rather than just what currently happens, which is clinicians almost firefighting and running around problems that have already developed," DeepMind clinical lead Dominic King told Wired. The team fed health data from more than 700,000 Veterans Affairs hospital patients across the U.S. to their neural network. Their results were promising, according to a paper about the research published Wednesday in the journal Nature: the system can even tell doctors what piece of medical data tipped it off that a kidney crisis was imminent. But while the system is speedy, it's way too trigger-happy: it reported two false positives for every correctly identified kidney injury.

It's not who has the best algorithm that wins; It's who has the most data -- Andrew Ng. Image classification is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. In this blog I will be demonstrating how deep learning can be applied even if we don't have enough data. I have created my own custom car vs bus classifier with 100 images of each category.

For developers, advances in hardware and software for machine learning (ML) promise to bring these sophisticated methods to Internet of Things (IoT) edge devices. As this field of research evolves, however, developers can easily find themselves immersed in the deep theory behind these techniques instead of focusing on currently available solutions to help them get an ML-based design to market. To help designers get moving more quickly, this article briefly reviews the objectives and capabilities of ML, the ML development cycle, and the architecture of a basic fully connected neural network and a convolutional neural network (CNN). It then discusses the frameworks, libraries, and drivers that are enabling mainstream ML applications. It concludes by showing how general purpose processors and FPGAs can serve as the hardware platform for implementing machine learning algorithms. A subset of artificial intelligence (AI), ML encompasses a wide range of methods and algorithms.

Next-generation vehicles such as drones have a hard time landing. Drone controllers usually bring the drone near the ground and then drop it. How low the drone can be brought down depends on the aerodynamics of the drone and other reactions from the ground. Since drones of the future will be carrying medicines and other fragile instruments into mysterious landscapes or hilly areas, dropping the drone isn't always desirable. To address this problem of smooth landing, researchers at CalTech's Center for Autonomous Systems and Technologies (CAST), have imbibed neural networks into their approaches.

The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project's maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare. Originally developed at the National University of Singapore, SINGA joined ASF's incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.

To take a look at what the end goal in terms of end-to-end deep learning for visual SLAM might look like, take a look at gradSLAM from Krishna Murthy, a Ph.D. student in MILA, and collaborators at CMU. Their paper offers a new way of thinking of SLAM as made up of differentiable blocks. From the article, "This amalgamation of dense SLAM with computational graphs enables us to backprop from 3D maps to 2D pixels, opening up new possibilities in gradient-based learning for SLAM." We are seeing more and more practical successes of self-supervised learning for multi-view problems where geometry enables us to get away from strong supervision. Even the ConvNet-based point detector SuperPoint [7], which my team and I developed at Magic Leap, uses self-supervision to train more robust interest point detectors.

Data science is all about data, and I'm pretty sure you already knew that. But did you know that we use data science to make business decisions? I'm pretty sure you knew that as well. So what else is new here? Well, do you know how data science is used to make business decisions?