Computer vision is one of the fastest-growing and successful AI fields. Combined with machine learning, automated systems on the factory floor are possible today. In this episode, I talk to Shahar Zuler, a data scientist and machine learning engineer at Siemens. We discuss the uses of computer vision in factories and explore exciting AI techniques that we are employing with our tools to design, realize and optimize factory equipment. We discuss the very latest technique called 6D pose estimation which stands to revolutionize robotics on the assembly line by solving age-old problems.
What you will learn:
- How AI is impacting the industrial industry
- The unique challenges of employing AI/ML in the industrial environment
- Different tasks of computer vision machine learning
- How to train an object detection model
- How synthetic images are used in ML model training
- How to validate synthetic data
- The benefits of partnerships between Siemens and their customers
Shahar Zuler – Guest
I am a Data Scientist and Machine Learning engineer at Siemens Digital Industries Software, where I connect between advanced robotics simulation and Artificial Intelligence. I love solving complex problems in creative ways. In my role, I develop solutions for different problems from the initial feasibility study, through a basic proof of concept until productization. I’m interested in how researchers take existing concepts and introduce them to new domains, and doing my best to stay up to date with recent Machine Learning work.
Thomas Dewey – Host
Thomas Dewey (BSEE) has over 20 years of electronic design automation (EDA) experience at Siemens EDA (formerly Mentor Graphics). He has held various engineering, technical, and marketing responsibilities at the company, supporting custom integrated circuit design and verification solutions. Since 2017, he has researched, consulted, and written about all aspects of artificial intelligence. His current role is AI technology leadership.