Artificial Neural Network
Neural Network
What is an artificial neural network?
- The core AI structure modeled on brain neurons
- Learns patterns layer by layer
- The foundation of deep learning and generative AI
What neural networks have really changed is the nature of a developer’s work. In the past, people hand-coded rules one by one, such as “if this pixel is this dark, it’s a defect.” With a neural network, you gather labeled data, feed it in, and let the machine build the decision rules itself. So the make-or-break part of a project shifts from logic design to data collection and label quality. That is also why the in-house idea “Can’t we just hire one developer who’s good at AI?” so often falls short. What really eats up the schedule is collecting data and labeling it.
The structure divides into an input layer, hidden layers, and an output layer, and a network with many stacked hidden layers is called deep learning. Different families suit different data: CNNs for images, RNNs and LSTMs for ordered values such as audio or demand data, and Transformer models for language. Scale differs enormously too. A handwriting classifier needs only tens of thousands of weights, while large language models have hundreds of billions. It also needs pointing out that machine learning and neural networks are not the same thing. Decision trees and regression analysis are legitimate machine learning too, and when you have only a few hundred records, boosting methods are often more accurate than neural networks.
The most common misconception is that a neural network “understands” the way a person does. It only follows the distribution in its training data, so a model trained only on photos taken under night lighting will perform much worse on a daytime line. It is also common to train on data that is 95% normal images and then report “95% accuracy,” a number you would get even by labeling everything normal. If you don’t track the defect detection rate and the false-alarm rate separately, you put an unvalidated model on the line, defects still escape, and accountability only gets blurrier. Because it is hard to explain why the model made a given call, it is safer to keep a human re-check step in place for at least a few months.
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Seolyeong ParkGenerative AI and AX training specialist with 17 years of hands-on experience and 200+ lectures, helping non-technical staff put AI to work right awayIt began with McCulloch and Pitts’ artificial neuron model in 1943, passed through the perceptron in 1958 and the backpropagation algorithm in the 1980s, and flourished as deep learning in the 2010s.
Consider a hypothetical case. Company A, an auto parts maker, has 420 employees and annual revenue of KRW 90 billion (≈ US$67,335,000). Three inspectors visually check 12,000 painted parts a day. Over the past three months, 27 defective parts slipped through and were returned by customers. The quality assurance team launched a 6-month neural network inspection pilot.
- When QA team lead Kim went through the 27 return records, 19 turned out to come from the night shift. At the executive meeting, Kim concluded: 'It’s not that our inspectors have bad eyesight. Nobody can see well at 3 a.m.'
- The production engineering team collected 60,000 line-camera images, but only 800 showed defects. Data lead Manager Lee put on the brakes: if most samples are normal, labeling everything normal still scores 98.
- Over three weeks, the team re-photographed defective parts and added rotation and brightness variations, growing the defect set to 4,200 images. They then applied transfer learning to a publicly available pretrained CNN and had a first model in two days on a single GPU.
- It scored 96% on validation data, but once connected to the actual line it fell to 82% during the afternoon hours when the lighting changes, so the team fixed the lighting in the inspection booth and retrained under those conditions.
- Six months later, escaped defects fell from 9 a month to 2, re-inspection time dropped from 4 hours a day to 50 minutes, and 2 of the 3 inspectors moved to the assembly line.