Table of Contents
Renowned influencer James Moyne joins Samantha Duchscherer in an engaging discussion exploring the importance of integrating additional information and advanced technologies like Artificial intelligence (AI) into the semiconductor industry’s scheduling and dispatching process. The comprehensive series consists of four parts focusing on various subjects such as the importance and advantages of data, AI, and human involvement. It also delves into the obstacles faced and offers insights into the role of digital twin technology.
In this second article of the series, the focus is on defining machine learning (ML) and AI. The article provides examples to illustrate their differences and similarities.
AI vs. ML
Different levels of AI
- Granular AI operates within defined boundaries or parameters, using machine learning algorithms to make precise predictions or decisions within a known space. It fills in gaps between data points to provide a more granular understanding of the system’s behavior.
- Exploratory AI goes beyond known boundaries, exploring unknown territories where existing models may not apply. It incorporates external knowledge and data from various sources to gain a broader understanding of complex problems.
- General AI, or artificial general intelligence (AGI), represents the highest level of AI sophistication. It mimics human-like intelligence (i.e., reasoning, problem solving, and adaptation), possessing a comprehensive understanding of the environment and the ability to adapt to a wide range of tasks.
Discussion
Chatbots and semiconductor examples
Sam: Transitioning to semiconductor examples, I’m assuming building an algorithm on two years of historical data to predict lot cycle time would be ML correct?
Sam: What about development of a reinforcement learning model to find the optimal dispatching parameters needed for an unseen event that would happen in a fab? Where would this example fall on the spectrum?
James: I would say that still, for me, falls into machine learning, although I’m sure there are people out there who would say, ‘No, no, this is artificial intelligence.’
However, in the case of an event that has never occurred before, I might talk to experts and go read books to identify what this type of failure might look like and what the risks and rewards are. When we try to build an algorithm on this new data we bring in, whether real or simulated, this is when we start to get into the intelligence aspect.
James: It is a spectrum from pure artificial to pure human. What will be needed in the long term is a full integration of human and AI; even AI systems need some kind of human checks and balances. The interaction between human and artificial intelligence has to become more asynchronous. For example, a human should be able to provide intelligence to an AI system as soon as that information is obtained and verified, e.g., without being prompted. Conversely, an AI system should know when and how to ask the human for help.
I have a saying for this, ‘No knowledge left behind.’
Conclusion
Machine learning and AI are part of a spectrum of technologies that perform similar functions. For this reason, people are often confused as to which one is at play. Ultimately, the distinction between AI and ML may vary depending on the context and perspective. Both fields continue to evolve, and the boundaries between them may become even more nuanced as new technologies and approaches emerge.
Up next, we’ll dive into the challenges and benefits associated with enabling AI and ML within the scheduling and dispatching framework.
About Dr. Moyne
Dr. James Moyne is an Associate Research Scientist at the University of Michigan. He specializes in improving decision-making by bringing more information into the scheduling and dispatching area. Dr. Moyne is experienced in prediction technologies such as predictive maintenance, model-based process control, virtual metrology, and yield prediction. He also focuses on smart manufacturing concepts like digital twin and analytics, working towards the implementation of smart manufacturing in the microelectronics industry.
Dr. Moyne actively supports advanced process control through his co-chairing efforts in industry associations and leadership roles, including the IMA-APC Council, the International Roadmap for Devices and Systems (IRDS) Factory Integration focus group, the SEMI Information and Control Standards committee, and the annual APC-SM Conference in the United States.
With his extensive experience and expertise, Dr. Moyne is highly regarded as a consultant for standards and technology. He has made significant contributions to the field of smart manufacturing, prediction, and big data technologies.
