Issue link: https://iconnect007.uberflip.com/i/1504794
64 SMT007 MAGAZINE I AUGUST 2023 duction environment is difficult without exten- sive data, configuration, and deep technical know-how. Let's get into our way-back machine for a moment. Technologies evolve over time and AI is no exception. Explain the evolution of artificial intelligence and how different generations of artificial intelligence have evolved over time. Expert systems have been around since the late 1950s. A human programmer would code their expertise or that of a human expert into a sys- tem. It typically translates into logical state- ments (e.g., "if-then-else") representing spe- cific rules in the decision-making process. However, it's hard for humans to explain their reasoning for a decision, which makes defining concrete rules extremely challenging. Further- more, predicting every variation at the time of programming is impossible. As such, expert systems are inflexible and prone to error in real-world deployment, as they cannot handle the variations and uncertainty that can happen in practice. Rather than hand-craing rules as in expert systems, machine learning teaches a system how to do a task by "training" it on a large data set. e extensive rules are devised implic- itly around the data. In some ways, it mim- ics human learning. When my daughter was a toddler, she learned what a dog was by seeing them in parks and books. When she went out with us, she could quickly point out dogs quite accurately, despite not being able to explain her rationale in expert detail. Generative AI is a form of artificial intelligence where systems create new content, such as written articles, dialogues, and imagery. Typically, generative AI is trained on past content and can generate new content based on prompts (e.g., "Gener- ate a picture of a dog running in the field on a warm, sunny day"). Within the electronics manufacturing space specifically, how has AI been integrated into our industry? Here are a few examples: • Automated Inspection: Traditional AOIs based on expert systems have been effec- tively used for many years. ey require significant programming up front and re- programming whenever there are changes to products and components used, changes to the manufacturing environ- ment and processes, etc. Machine learning not only leads to a big leap in the perfor- mance of finding defects in PCB assembly but significantly reduces and even elimi- nates programming time and frequency. • Design process: To help address complex designs and a scarcity of skilled engineers, soware companies are adding machine learning and generative AI capabilities to automate aspects of PCBA design to speed up design and improve the efficiency of the resulting PCBA design. • Predictive maintenance: Analytics to predict when maintenance is needed for equipment, such as a reflow oven or a pick-and-place machine, to prevent it from breaking down. Arif Virani