content provided by pleora

Artificial intelligence (AI) is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications deployment remains a challenge. 

Part of the technology trepidation stems from uncertainty around the terms and definitions of ‘AI’ and ‘machine learning’. Organizations are also unsure how to deploy new AI capabilities alongside existing infrastructure and processes. This is especially true in inspection systems, where there are significant investments in cameras, specialized sensors, and analysis software with well-established processes for end-users. The cost and complexity of algorithm training is also a concern for businesses evaluating AI. 

At its most basic, AI is the ability for a machine to perform cognitive functions that we associate with our human mind, such as recognizing and learning. Machine learning, a subset of AI, involves coding a computer to process structured data and make decisions without constant human supervision. Once programmed with machine learning capabilities, a system can choose between types of answers and predict continuous values. Machine learning programs become progressively better as they access more data, but still require human oversight to correct their mistakes. 

While AI is often seen as an emerging technology it already surrounds us in our consumer lives, particularly home sensor network systems. A “smart” thermostat, for example, uses a combination of user-inputted data and monitored human activity to determine when we’re home, away, or inactive to set its estimated ideal temperature. Occasionally, the homeowner still needs to manually correct thermostat settings.

Machine learning still requires human input to make informed decisions and needs further programming to fix mistakes. Deep learning goes one step further, with algorithms that use a wider range of structured and unstructured data to make independent decisions and can learn from mistakes and adapt without requiring human programming. Autonomous applications, including emerging passenger vehicles to factory robotics, use deep learning to navigate consistently changing situations.