Design patterns have been in use in the software engineering world for quite some time.

A design pattern implies that there is a “best practice” for constructing and coding a model that can be reapplied across a wide range of cases, such as image classification, object detection and tracking, facial recognition, image segmentation, super resolution and style transfer.

Design patterns are coming to neural network design.

The introduction of design patterns also helped advance convolutional neural networks (as well as other network architectures) by aiding other researchers in understanding and reproducing a model’s architecture.

Udemy published an article on Wednesday outlining the key findings of its 2021 Workplace Learning Trends Report.

The report detailed training courses that have seen a surge in demand on Udemy for Business, the company’s online learning platform.

The company also analyzed organizational “learning behaviors” to identify six trends Udemy believes will “define the workplace in 2021.”

“While much of this year has been uncertain, one thing has become undoubtedly clear,” said Shelley Osborne, VP of learning at Udemy, via email. “The future of work many of us have been talking about is no longer an eventuality — it’s our current reality. Around the world and across industries, organizations are fundamentally rethinking every aspect of how we work. As we look ahead to what the workplace will look like in 2021 and beyond, our recent report shows that continued upskilling and learning agility will be required to keep pace with our ‘new normal.'”

Voice assistants were first integrated into smartphones several years ago, and consumers have become increasingly reliant on voice technology to simplify and automate the tasks in their daily lives like booking an appointment or making an online purchase.

Dr. Ryan Chen, general manager of the Computing and Artificial Intelligence Technology Group at MediaTek discussed with The Manila Times (TMT) where the innovation in voice recognition technology is headed and how it could make a real difference.

Rendering is a complex process. Its differentiation cannot be uniquely defined; thus, a straightforward integration into neural networks is impossible.

Differentiable rendering (DR) constitutes a family of techniques that tackle such integration for end-to-end optimization by obtaining useful gradients of the rendering process.

Nvidia and Aalto University introduce a modular primitive to provide high-performance primitive operations for rasterization-based differentiable rendering. The proposed modular primitive uses highly optimized hardware graphics pipelines to deliver better performance than previous differentiable rendering systems.