Photonics, lasers and the development of the electronics infrastructure behind AI applications are all part of what I was referring to as the technology stack required for successful adoption of AI solutions in industry, not just in manufacturing. It's important.
First, we can look at the need for sensors, the need for real-time command and control information that really requires edge solutions in a manufacturing application, to ensure that the latency is not expanded in a way that couldn't be used effectively in manufacturing processes. Integrating not only the data coming from the equipment machinery but also the software being used for materials handling for enterprise resource processing in manufacturing really shows how important it is to focus on the integration of technology. Today, we have robotics and vision systems, and many of these systems are already AI-enabled.
The important message I have is this. When looking at AI, don't think of AI simply as algorithms. We need to focus on how that is integrated into the technology stack that can deliver improved productivity results at the end of the day.