Infineon is also following the AI learning path. Artificial Intelligence is a key enabler of Digital Transformation. AI has been proven to be a useful tool in solving urgent problems. AI is a key competitive factor in many companies’ business strategies.
The smart control, multi-objective optimization and deep learning give further opportunities to the customers. By harnessing AI they can improve product quality and efficiency while reducing costs. As a leading supplier of advanced technologies, Infineon is committed to developing new applications for AI by leveraging its expertise in technology, platforms and market trends.
What does this all mean for Infineon ?
Nico Kelling was our guide to understanding these words and bringing life to them. He is the head of the AI Center of Excellence and oversees all aspects of AI within the Infineon group: finding use cases, delineating innovation processes, implementation, cultural change, and collaboration with employees.
My team is a guide and supporter. Our team leads a workforce that is already tech-savvy in regards to AI maturity. His team coordinates activities throughout the company. This includes co-innovation activities. Our team focuses on making sure that we are aligning with the company’s goals and understanding where the company is going. His team will be an active participant in our work, which is very insightful for any organization.
Tang yak Sukhai was a wonderful person to talk with about her experiences in AI. She had a great story to tell and gave some great insight on how companies are developing AI and its implications. She was able to give me some good information about what I can expect from the future of AI. Her company is already at a stage where they are deploying many different types of AI solutions in their business and she had great insights on how those systems are working in her organization.
What can AI do to make your job easier and produce better results?
Kelling’s co-innovation approach means that there is shared learning across departments. His team encourages collaboration, moderates and promotes a value-oriented approach. He needs to have his own ecosystem and innovative approaches that are universally applicable and understandable in order to do this. This includes collaboration with outside entities like startups or research institutions, and also exchange with other companies.
“If we can weave the technology broadly in European areas of expertise like mobility, industrial control and plant control or product development, then the foundation for prosperity and Competiveness will be laid for many decades to come.”
Kelling is optimistic and believes that AI will also benefit the environment: “Ultimately we can generate more with less AI. For example, chip design: We are already looking at how to achieve the same performance for less basic materials. How is this possible? “Chip design can be described as a multi-layered optimization problem. That’s why we are responding with AI.
The Educated Decision – Will humans become redundant?
These smart assistants will continue to be AI’s role. AI can be used to speed up data-based decisions and replace human beings. This is why Kelling believes AI is an ongoing learning process for the entire company. It is always trial and error, not something that is fixed. Each use case shows where AI can be used effectively. It has already shown its immense potential in chip design, predictive maintenance of production, and smart supply chain control.
What happens when the learning journey is over? A successful use case can lead to a standard procedure that can be used by others. Our success in the AI age is dependent on collaboration and sharing best practices.
Cultural change – traceability, data sharing
Infineon is a technology-based company. It would seem that there wouldn’t be any culture changes required to leverage AI to Infineon’s advantage. Kelling also surprises us here. He says that Infineon is a perfect place.
“Nevertheless, we must rethink how data sharing is done. This requires a shift. Data is a valuable resource. We must share and exchange data more effectively if we are to understand it. This changes many roles and decision-making structures within the company.
His team also has another goal. His team has another goal: to increase AI expertise at all levels and make AI transparent, transparent, and understandable.
Transparency is essential, especially since Infineon continues to innovate in human-machine interaction and its products. This is where AI cannot overwhelm, it must be understandable. “My conclusion is that technology must be brought in line with our values. An AI that is human-centric will eventually be accepted by customers and employees.”
In a upcoming Kopakt podcast, Nico Kelling will be having a deeper conversation about the transformation.
What are the 3 stages of artificial intelligence?
- This article is a short introduction to the 3 stages of artificial intelligence. It will help you understand the current state of AI, how it works and what it can do for you.
- The first stage of AI is called “deep learning”. This stage uses algorithms to learn from large amounts of data and then applies that knowledge to new tasks. In this process, the artificial intelligence learns from its own experience and does not need any external inputs or training data (the same way that humans learn).
- The second stage of AI is called “supervised learning”. This stage uses algorithms to teach itself from simple examples that are given by humans (for example, a picture).
How artificial intelligence is transforming?
AI writing assistants are not only being used in the workplace, but also in education. They are used to create content for different subjects and they can be used to create content for various types of subjects such as: Journals/Switchboards Scheduled Time/Quarterly Profiles Advertising campaigns for different brands and organizations for various purposes.
What are the five features of artificial intelligence?
AI is a term that has been around for a while. It is the use of computers to do tasks that humans would normally do. There are many different types of artificial intelligence, such as deep learning and machine learning. They are becoming more and more popular in the workplace, particularly in the digital marketing world.
Deep learning is a type of artificial intelligence that uses large amounts of data to build artificial neural networks that can learn new things by themselves. This type of AI has been used for years in research labs to train computers to perform complex tasks like speech recognition and image recognition. Deep learning has also been applied to other fields including mathematics, physics and medicine where it can be used for training machines with human-like reasoning capabilities.
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