Dr. Michael May, Head of Core Technology, Data Analytics & AI at Siemens shares his insights on the evolution of industrial grade AI with key features like ethical, sustainable, and responsible AI, in an interview with Dinis Guarda. The interview at the AI With Purpose Summit 2024, Munich, Germany, is powered by Businessabc.net and citiesabc.com.
Dr. Michael May, an expert in AI, data analytics, and machine learning, is leading Siemens into a new era of technological innovation. As Head of Company Core Technology, Data Analytics & AI, Michael brings extensive experience and academic insight to drive Siemens’ initiatives in AI and industry. He has led significant projects funded by the European Commission and collaborated with over 120 universities.
At the AI With Purpose Summit 2024, Michael spoke on the panel discussion on Challenges and Opportunities of AI Governance. During the interview with Dinis, Michael highlights Siemens’ innovative strides in enhancing environmental efficiency through AI-driven simulations:
“Siemens is selling simulation software for example for the automotive industry or for the oil and gas industry. We use simulation to build better products which are more energy efficient. Before you build it, you do a lot of simulation to make sure that what you build is in fact what you wanted it to be. These simulation systems are costly in terms of computation time, so you need a lot of computation many hours, many days, many weeks, and in some cases until you have the results, because there’s very complex mathematics behind what is used for the simulation.
Now the question is wouldn’t it be maybe better if we can speed up the whole process for example by making it faster so that it consumes less energy because it doesn’t compute that much maybe it even finds solution that could not be found before and so in the end you get a product maybe more environmental friendly product.
This is what we have been doing so Siemens is now selling a product using AI which uses the AI to predict using simulation. Using these techniques, you can filter away many of the potential solutions that have to be calculated and then you focus just on the promising one. Maybe this is how we humans are good at thinking, so we don’t evaluate millions of possibilities but just a few because we know where to look, where to focus on the right part and there the AI helps us to focus on the right part.”
A sustainable and secure AI for all
As AI continues to reshape urban landscapes, Siemens recognises the imperative of addressing governance and ethical considerations. Dr. Michael May highlighted Siemens’ proactive approach to AI regulation, stating:
“AI regulation is a topic you must deal with, if you want or not. You will not be able to, in a short period from now, deliver products to customers which do not somehow give an answer as to how do I make it safe, how do I make it responsible, how do I meet the privacy requirements attached to that. You need some level of governance that you have to address. You need to have answers why your system is secure.
Siemens, for a couple of years now, is working on that – preparing for regulation, but also trying to seize the opportunities that give you to build systems which are industry grade, i.e. comply with the upcoming regulations.
If your customers know what you’re building is safe, and it does comply with all the regulations, then they might be inclined to buy your products or use your products with more confidence.
On the other side, of course, we have to see that we do not overdo it. If there’s too much of it, then it can become a burden for innovation. It can make the production process much more lengthy, more expensive.
We need to be absolutely clear about whether we need to have regulation which is good for innovation still and on the other side you keep things trustworthy. Build things or aim to build things which are safe and trustworthy even without external regulation.”
Generative AI is also known for its ability to hallucinate. As Michael explains:
“Hallucination is the number one problem we have to solve to make AI industry grade, or generative AI industry grade. Hallucination means that the machine can come up with what it thinks is correct but which in fact are hallucinations, so it dreams, or it hallucinates. This, of course, in a safety critical environment can be a big problem.”
He further elaborates the ways to tackle this problem:
“AI is not just generative AI. It is not just machine learning or deep learning but has many facets. There’s the one which is more, what is called inductive or probabilistic, based on inference, and which might sometimes be wrong. And then the more logic-based part that can give you these guarantees and one very practical way we do that is to bring in the domain knowledge.
Industrial companies have a lot which we structure in the form of what is called knowledge graphs. A Knowledge Graph is a formalism where you can model terminology knowledge, ontologies for example, and where you can also structure fact-based knowledge in a way that if your representation is correct then all the inferences you make from that are also correct because it’s based on logic and not so much on probabilistic.”
To instil trust and security in generative industrial grade AI, Michael says, Siemens emphasises the rule-based governance systems and application of knowledge graphs and logic-based things. He says:
“Explainable AI is a big topic these days and you can use that to increase the trust in AI. Then another pretty conventional thing is that you closely work with the domain experts and provide one layer of security or safety around the AI you’re building. So, you build AI, you use things like reinforcement learning which is a quite advanced methodology, which, for example, we have been using for years with our colleagues from Siemens energy to control the working of the gas turbine.
But then you work with the engineers from the domain and build some kind of virtual safety net around that to make sure that nothing can happen and which is understandable for the domain experts and that takes a lot of effort and interdisciplinary teams to make that work. This is sometimes in an academic environment and totally underestimated.
So you think you build an algorithm and once you’ve built the algorithm then the thing should work, but sometimes it’s a couple of years, maybe even, that you have to work on making a good idea which works in principle to really make it work in practice so that it’s safe to use.”
Navigating AI challenges and future prospects
Looking ahead, Siemens envisions a future where AI plays a pivotal role in building smarter cities worldwide. Dr. Michael May expressed optimism about AI’s evolving capabilities:
“The development will go on quite quickly and you can well extrapolate from now. I mean now we have these large language models, we have Vision models, we have first forms of multimodality, and I think step after step we will cover all the other modalities which are relevant like thinking in Time series, thinking in terms of space and geometries. So, it will be even more like our human brains. We will also have all these modalities we can talk, we can hear, we can see, can think about space and time, step by step will be incorporated into these multimodal foundation models and that will be a big big step forward in terms of capability. Not just adding one by one and one, but it’s exponentially more powerful if you have all the combinations of reasoning and seeing and speaking.
I’m always a friend of seeing AI as an intelligence amplifier, not as a replacement for a person. So, I think for the time to come, the most successful teams will be those that consist of humans and machines with different functionality, with different tasks and we need to learn. There’s a lot to learn for us how to take best advantage of the machines and I better do it myself.
AI, I think, will determine the future in the next few years as to how the industrial world will be working. I think it’s not about replacing humans. We see how difficult this is to do the last bit so the last percentage or milli percentage of automation for all our processes.”
With a driving passion to create a relatable content, Pallavi progressed from writing as a freelancer to full-time professional. Science, innovation, technology, economics are very few (but not limiting) fields she zealous about. Reading, writing, and teaching are the other activities she loves to get involved beyond content writing for intelligenthq.com, citiesabc.com, and openbusinesscouncil.org
