Boris Scharinger, AI Strategist At Siemens Digital Industries, Discusses Industrial-Grade AI With Dinis Guarda At AI With Purpose Summit 2024
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Boris Scharinger, an AI Strategist at Siemens Digital Industries, discusses on the Dinis Guarda YouTube podcast how industrial AI is reshaping urban environments with sustainability. Emphasising the company’s strategic approach to talent and technology, he highlights in this podcast episode, powered by Businessabc.net and citiesabc.com, the role of events like AI With Purpose Summit 2024 in shaping the future of technology.
Boris Scharinger is the Senior Innovation Manager and Technology Strategist in the CTO Office at Siemens Digital Industries. Based in Nuremberg, Germany Boris responsibilities include innovation management, the development of technology strategies and roadmaps, and technology scouting. His areas of expertise encompass startups, quantum computing, industrial AI, scaling artificial intelligence, ecosystems, and the data economy.
The AI With Purpose Summit, powered by the Siemens AI Lab, aims to bring together leading experts, industry leaders, and policymakers to discuss the pressing challenges and promising opportunities of integrating AI into various industrial sectors. Its third edition took place on June 10th and 11th, 2024 at the House of Communication in Munich, Germany.
“Let's make AI industrial-grade together. We need to solve several challenges. All together to finally make it a lot more ubiquitous than it is today. And Siemens works in many areas on that topic. Last but not least, hosting the AI with Purpose Summit as a conference each year, now for the third consecutive time, brings together practitioners from industry to join us in that mission."
AI With Purpose Summit 2024: A collaborative effort to ensure an Industrial-grade AI
At AI With Purpose Summit 2024 event by Siemens in Germany, Boris is a panellist on “Challenges and Opportunities of AI Governance”. He emphasises the critical transition from proof of concept to mature products and discusses the challenges and solutions in making AI industrial-grade.
“It's important to understand that generative AI is something that is new in a way that it tackles and addresses high skill, high labour cost value points. For example, if I have a lawyer and the lawyer is writing, text, right, contractual, drafts, contract drafts, then generative AI can really support that lawyer. A solid portion of the repetitive work that you used to do can now be replaced or supported, at least by generative AI. But you still need to be skilled.
Generative AI will find its way into many daily places, and we need to make sure that everyone understands how that works and gets upskilled to leverage generative AI for the respective area of responsibility and tasks.
We really need to transform industry to become a lot more powerful and resource efficient, and for this huge transformation of industry in the next 10 to 15 years.”
He also stresses the importance of collaboration, ethical AI, and cybersecurity. Speaking about the Charter of Trust established by Siemens, he told Dinis:
“The Charter of Trust is an organisation that was first based on the understanding that cybersecurity is today such a huge challenge that no company can master that challenge by themselves. We have asymmetrical threats on this planet, so a company cannot fight back on its own. So, governance needs to be made aware of. We need legislation to fight against it and so on and so forth. So, Siemens decided to create a foundation that really takes care of things and shapes the future of cybersecurity for society and industry.
The Charter of Trust just has recently extended their work into the area of trustworthy AI. This is an invitation for companies working together and making sure we teach and educate politicians in what type of regulation we need, in what places. We also explain to them what they should be lobbying for by becoming more international on AI legislation and not just have a very strong European focus.
On the other side, we have a consortium of different companies who worked on what we call the AI trust label, which is really an assessment methodology. How would an assessment look to come to a conclusion that my AI fulfils many criteria of explainability, of privacy, of trustworthiness, of avoiding biases?
If AI were robust and reliable, we all would be driven by autonomous cars, right? But I have not been driving in an autonomous car to this place this morning. And the conclusion is, if I can not run a car, how am I supposed to run my factory on AI? So there is a long way to go with specific AI and safety and industry. I mean, hey, that's our quality promise from a Siemens perspective. But it's also our shout into the ecosystem.”
Industrial AI: Enhancing industrial efficiency
Industrial AI is designed to meet the rigorous requirements and standards of the most demanding industrial environments. As Boris explains it to Dinis:
“The digital transformation of industry and AI is a strong tool. When I look into AI today, then AI is actually designed and shaped a lot from the consumer space, from the search engines, and from the social media platforms of this word. And in these environments, you can allow for a certain level rate. Yeah, in many cases 90% is good enough. However, in an industrial production process, I cannot have a robot that works in 90% of the cases. I need to have a robotic solution gripping something, for example, and putting it somewhere on a conveyor belt that really must work for me, 24 by seven must work 99.9%, and for example, it must be able to process a thousand picks per hour. Now these industrial tasks require robust reliability. This is what's essential for industrial AI. Industrial AI, in a sense, is the application field. It's the application of AI in industrial use cases, our quality ambition. So AI meets the tough requirements that you have in industrial processes."
Highlighting the role of simulation and digital twin technology in enhancing the overall efficiency and optimising industrial processes, Boris says:
"We would like to be able to explore the best possible designs, the best ways of production by simulation first before we then push the button and real resources of this real planet are consumed. So it is optimising designs. It is optimising processes because you do it in simulation first.
We really want to design the production line and the pacing of the production line and the different products that are processed by this production line. That's what we need to simulate to really be able to later on, understand energy efficiency, overall equipment effectiveness, such things.”
Boris Scharinger also explains that predictive maintenance is another critical area where Siemens leverages AI to enhance industrial efficiency.
"There is a lot of potential in the area of predictive maintenance. Because, by having a strong data ingestion process, by having good data coming from the shop floor, being fed into your system. By having the right data, ingesting the right data, by combining a strong foundation and physics understanding of shop floor equipment and automation systems and now blending that together with AI technology, predictive maintenance is an area which is extremely important for us.”
Siemens: At the forefront of shaping the future of Industrial AI
Looking ahead, Boris Scharinger envisions a future where AI continues to drive efficiency and sustainability in industrial processes. He believes that collaboration and continuous learning are key to staying at the forefront of technological innovation:
“One of the things that I experienced with AI is that, once you have achieved one goal and said, look at this fantastic invention, look at what the technology can do for you and you bring it to your customer, and they say, wow, that's fantastic, that's great. But then there's also the next minute when they ask you, okay, Boris, so what comes next? What else can I do? And so the anticipation of customer requirements and the anticipation of market requirements, this is a continuous process, and this is what it takes if you are in such a field that is so fastly developing as AI is.”
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
