Tina Baumgartner

Artificial intelligence (AI) will transform manufacturing – it’s just a matter of time. While few manufacturers have broadly adopted AI on the shopfloor yet, the advantages of AI for applications such as visual inspection and predictive maintenance are just too convincing to ignore. One of the biggest road blocks to AI adoption is fear of […]

If you have, like me, been in Silicon Valley long enough – or even just followed the tech news – you have been through your share of hype cycles. Remember the early dot com craziness? The idea that our lives would completely happen online and nobody would ever try to find a romantic partner at

Do you want to take a guess how long a person can focus on a repetitive task such as visual inspection? Let me second guess your guess and say “go lower!” Here’s the data: one study found that even after 20 to 30 minutes a phenomenon called “vigilance decrement” occurs which means individuals experience a

In a recent blog we wrote about the important differences between mature AI and generative AI when it comes to applications in manufacturing. A quick reminder: mature – or classical AI – is robust, stable, explainable and fixable and at this point in their development classical AI models are pretty much deterministic. In other words,

A while ago we wrote a blog about how classic AI e.g. machine Learning (ML) is ready for prime time on the shopfloor while generative AI will need a little while longer to be mature enough to be deployed in manufacturing itself. However, if you work in and with AI everyday, there is no way

We live in the age of big data. Whether it is social media platforms, retail and e-commerce, telecommunications, healthcare or manufacturing an estimated 2.5 exabytes – or 2.5 quintillion bytes – of data is generated each and every day. The ability to crunch and make sense of these data was long the limiting factor. However,

How AI is disrupting our industry and what can we do about it? – this was the question Authority Magazin asked AI professionals and of course we were only too happy to talk about the changes, challenges and opportunities we see for AI in manufacturing. The article is in form of an interview and you

In our short series of challenges facing the forward-looking manufacturing professional ready to adopt AI, we have so far talked about data and change management challenges. Now it’s time to talk about technology challenges our customers have encountered and how to address them. Here are the four most relevant ones: Technology Challenge 1: Integration with

Artificial intelligence (AI) is the future of manufacturing. Many of the issues that continue to plague the industry – too expensive, too much scrap, unplanned downtimes, lack of personnel for both specialized and routine tasks – can be addressed with this new powerful technology. AI has come a long way over the last few years