Data Science and Deep Learning Leaders Create GPU Open Analytics Initiative
Continuum Analytics, H2O.ai and MapD Technologies create open common data frameworks for GPU in-memory analytics
Quantum Computing and Deep Learning Can Tackle “Unsolvable” Problems, Says IBM
“If companies can access a quantum computer through an API in the cloud, they can take advantage of the speed without that overwhelming overhead.”
Deep Learning Poised to Transform Medical Imaging
Will computers "see" better than radiologists?
The $1 Trillion Deep Learning Race for Smarter Cars
To learn to drive like a human, driverless cars will require massive amounts of data, programming, and computing power.
Making Machines See: Intel Agrees to Acquire Movidius
Deal is expected to bring low-power vision processing to IoT devices and autonomous machines.
Predictive Maintenance: Teaching Old Machines New Tricks
One of the key applications to real-time capabilities within the industrial world is predictive maintenance, which enables companies to address issues with machines and production systems before they break down and create bottlenecks. RTInsights Industry Insights editor Joe McKendrick discusses the collaboration between IBM and National Instruments on a cloud-based, predictive maintenance solution.
Deep Learning Algorithms May Add Value to Big Data
Big Data is already valuable but, according to a report on IBMÔÇÖs Big Data & Analytics Hub, it could become even more so if deep learning algorithms live up to their promises. Here, RT Insights founder and Executive Editor Les Yeamans explains how this technology can give computers the ability to recognize and respond to sensory patterns in a way that comes naturally to all sentient beings.
Modern Compliance Requires Modern Tools
Modern tools, including AI, machine learning, and real-time monitoring, allow organizations to flag anomalies, adapt as they grow, and enforce policies dynamically.
Turning Unstructured Data into a Manageable Enterprise Asset
For data analysts, engineers, and scientists, automation can support AI and machine learning initiatives by giving them increased control, reduced overhead, and a clear, policy-driven approach to managing unstructured data at scale.
The Importance of Validating AI Content
AI can be a robust and valuable tool, but it is still prone to errors, and as the amount of AI-generated content grows, validation becomes essential. The best way to minimize errors and bias is to validate the source data before using it for machine learning.