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…

From ER to factory floor: AI for a healthier future

I recently read an article about AI being deployed to hospitals and ERs to detect deadly infections and downward spirals…

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…

Analog chips for a digital age: How the past can bring AI into the future

There’s no doubt that the current time in history is “The Digital Age,” an age driven by the ubiquitous digital…

Lightning fast and durable printed parts

Getting a component quickly is paramount to many businesses and the global supply chain in general, as lost time waiting…