A neural network is a computing system inspired by the human brain. Just as your brain uses billions of connected neurons to recognize faces, understand speech, and make decisions, artificial neural networks use layers of mathematical "neurons" to find patterns in data.
For businesses, neural networks power many AI applications you already use. When your email automatically sorts messages into categories, when fraud detection catches suspicious transactions, or when customer service chatbots understand your questions, neural networks are doing the heavy lifting behind the scenes.
The business value comes from their ability to handle complexity that traditional software cannot. A neural network can learn to recognize a fraudulent invoice not by following rigid rules, but by studying thousands of examples and picking up subtle patterns that humans might miss.
Do I need to understand neural networks to use AI in my business?
No. Just as you do not need to understand how a car engine works to drive, you do not need to understand neural networks to benefit from AI. Modern AI tools handle the complexity for you.
How do neural networks differ from traditional software?
Traditional software follows explicit rules you program in advance. If you want it to flag suspicious invoices, you write rules like "flag invoices over $10,000" or "flag invoices from new vendors."
Neural networks learn patterns from examples instead. You show them thousands of invoices, both normal and fraudulent, and they figure out the patterns themselves. This makes them better at handling nuanced situations where rigid rules fall short.
What are the risks of relying on neural networks?
Neural networks can sometimes make confident mistakes, especially with unusual data they were not trained on. They can also be difficult to explain, making it hard to understand why they made a particular decision. For critical business processes, this opacity can be concerning.
Zamp addresses this through activity logs that record every action, allowing you to review decisions. When our agents encounter unusual situations, they flag items as "Needs Attention" rather than guessing, ensuring human oversight on edge cases.
How much data do neural networks need to work effectively?
The amount varies by use case. For common business tasks like invoice processing or email classification, a few hundred to a few thousand examples are often sufficient.
Neural networks work best when they have representative data covering normal cases and common exceptions. If you are processing invoices from 50 vendors, the network needs examples from all 50, not just your top 5.
Can neural networks replace my existing business rules?
Not always. Neural networks excel at pattern recognition but struggle with absolute requirements. For example, if your company policy states "never approve invoices without a PO number," you still need an explicit rule for that.
The best approach combines both: use rules for absolute requirements and neural networks for pattern-based decisions. This way, you get flexibility where you need it and strict control where you require it.
How long does it take to train a neural network for my business?
For pre-built AI tools, training is already done. For custom applications, training can range from hours to weeks depending on complexity and data volume. However, the ongoing "learning" from new data often happens continuously in the background.
When you correct an AI agent's mistake, it can learn from that feedback and improve over time.
Do neural networks get better over time?
They can, if designed to learn from new data. Some systems retrain periodically on fresh examples, incorporating recent patterns and edge cases. Others remain static after initial training.The key is having feedback loops where corrections and new examples update the model.
Zamp's agents improve through your Knowledge Base updates, where you refine instructions based on what you see in practice, creating a continuous improvement cycle.
What happens when a neural network encounters something completely new?
Neural networks make predictions based on patterns they have seen before. When they encounter completely unfamiliar data, their predictions become less reliable. Well-designed systems recognize their own uncertainty.
Zamp handles this by flagging uncertain cases as "Needs Attention" instead of making risky guesses, ensuring a human reviews anything outside normal patterns.