This blog launches our series called “AI Essentials,” which aims to bridge the knowledge gap surrounding AI-related topics. It discusses how neural networks function and why data accessibility is so important for startups.
Before policymakers regulate AI, they need to understand a fundamental technology: neural networks. Although a neural network is often likened to the human brain, it actually works a little differently.
A neural network is a computer model that learns to identify complex relationships between input and output data. Structurally, it consists of interconnected layers: an input layer, several hidden layers, and an output layer. Information flows from the input to the output layer through these hidden layers.
Layers are composed of nodes, basic information processors. Each node takes a piece of information (input), applies a mathematical function to it, and then passes the result (output) to the other nodes in the next layer. Connections between nodes across layers carry weights, numerical values that determine the importance of the transmitted information. The network “learns” by adjusting these weights based on the errors it makes.
You can think of a neural network as a water filtration system. Each layer represents a filtration stage: the input layer is where raw, murky water enters, and the output layer is where clean water exits. Each node is an individual filter unit, where the amount of filtering it performs can be calibrated based on water quality, similar to how weights can be adjusted to amplify the most significant pieces of information. The system fine-tunes each filter to produce the cleanest water possible, much like how a neural network adjusts weights to produce the most accurate outputs.
Training most neural networks involves two main steps. First, the network is given a dataset called training data, which consists of examples with known outcomes (labels). Starting with random weights, the network takes examples, makes predictions, compares them to the correct outcomes, and then adjusts its weights to reduce errors. As training progresses, the network’s predictions get better and better. This process is repeated for many cycles (epochs) until these adjustments no longer result in significant improvements to the network’s performance, at which point the model has converged.
After training, the network is tested on a separate pool of data known as testing data. This testing phase evaluates the network’s prediction accuracy, showing its ability to generalize and handle real-world data outside of training examples.
Data is the lifeblood of neural networks because it fuels their learning process. These networks identify patterns by adjusting weights to highlight relevant information and minimize attention to less important details. Through iterations, the network learns to recognize which features in the data are most important for making accurate predictions. The more comprehensive and diverse the dataset a network trains on, the better it becomes at identifying pertinent information and understanding patterns, which enhances its performance in real-world applications.
For startups in the AI space, access to abundant, high-quality data is crucial. Larger tech companies typically have vast data resources from existing services or partnerships that startups often lack. This lack of data can significantly impede their ability to build models that are accurate and reliable. Therefore, open data initiatives, public-private partnerships, balanced intellectual property frameworks, and uniform regulatory environments play a critical role in helping startups obtain the data necessary to build robust neural networks, foster competition, and drive innovation in AI.
