NVIDIA Launches Development Platform For Metaverse
NVIDIA has announced new updates to its metaverse development platform, including an avatar creator and AI which generates faces from audio
Topic: Artificial intelligence technologies
Looks at issues related to artificial intelligence technologies, including cognitive computing, deep learning, and machine learning. Considers also supervised and unsupervised learning and natural language processing.
Conversational AI: Improved Service at Lower Cost
Gartner predicts that conversational AI will reduce contact center agent labor costs by $80 billion by 2026.
Even Partial Automation Can Accelerate a Digital Transformation Journey
Those hesitant to fully automate should consider partial automation strategies that, while keeping a human involved, can still deliver significant benefits.
Maximizing Software Quality with Artificial Intelligence
Testing solutions that use artificial intelligence help development teams more easily analyze and understand where and what to fix and gives them the ability …
Real-time Analytics News for Week Ending September 3
In this week's real-time analytics news: Teradata announced a complete cloud analytics and data platform, now with next-generation, cloud-native deployment and …
Why AutoML Should Become a Key Tool for Enterprises
With the potential to democratize AI and ML, AutoML is the answer many enterprises across industry verticals have been seeking to take AI projects from pilots …
How Hybrid Cloud Can Be Used For Analytics and Machine Learning
A hybrid cloud approach lets businesses essentially pick and choose the various aspects of their analytics and ML workloads that they want to keep on-premises …
Quantum’s Disruptive Promise: Niche to Mainstream
To take quantum computing from its niche existence to the mainstream, we need to learn how to isolate quantum systems better from their environment and learn …
AI in Cybersecurity Report Predicts Market Growth
As cybersecurity challenges evolve and become more complex, AI provides a critical service for protecting assets.
3 Considerations for Adding Real-Time ML to Applications
Data that is ready for machine learning will be observable, supported by real-time infrastructure, and primarily processed with streaming technologies.