smart factory using digital twin

Siemens and Deloitte Pave The Way For Xcelerated Reshoring

Overcoming Localization/Reshoring Challenges For years, manufacturing companies have moved more and more work offshore. Recently, that trend seems to be…

Detection and mitigation of AI bias in industrial applications – Part 3: Mitigation strategies and examples

In parts one and two of this blog series, I explained the difference between desired and harmful biases and discussed…

Detection and mitigation of AI bias in industrial applications – Part 2: Key points and justifications

In my first blog about the detection and mitigation of AI bias, we covered the concept of trustworthy Artificial Intelligence…

Less is more: the need for smarter data in AI training

Over the last decade, the maturity of AI algorithms has increased in huge strides to the point where pretrained, off-the-shelf…

Detection and mitigation of AI bias in industrial applications – Part 1: Background and definitions

Traditional machine learning techniques optimize for accuracy only, not fairness or avoiding unexpected/harmful bias. Current advances in AI are both…

The Future Car – The verification and validation of autonomous vehicles – Transcript

In a recent episode of the Future Car podcast, I talked with Nand Kochhar, VP of Automotive and Transportation, and…

Trust in the model, but verify the result: Understanding the AI thought process

In a previous blog we looked at the issue of AI as a black box along with some of the…

Nemo's Garden underwater farming biospheres with a diver

Harvesting the benefits of the digital twin

Agriculture is fundamental to our modern lives, but providing the produce we need, at the scale necessary for our global…

Efficient training of AI Vision for factory automation part 3

In part 1 of this series, we took a look at the challenges of training Artificial Intelligence (AI) and Machine…