The Engineering of AI
- engineering4impact
- Aug 18, 2025
- 3 min read
Artificial Intelligence (AI) has swept the globe, making searching the web even easier. With many AI models like ChatGPT or Claude AI, AI's expansion is only getting larger. New businesses are utilizing AI to automate tasks, create their website--or even their whole business. Students are using AI to correct their grammar, easily find credible sources for research, and study ever more efficiently. And with this monumental expansion, it's even more important to understand just how AI works. In this post, we will dive into how AI was engineered.
Brief History
The idea of AI originated in 1956 at the Dartmouth Conference (or maybe some sci-fi novel) in Dartmouth, New Hampshire, USA. Here, many mathematicians, physicists, and computer scientists gathered to discuss the idea of intelligent machines that could process information just as well as, if not better than, a human could. Before the conference, the idea was called Synthetic Intelligence, but it was at this meeting that the term AI was coined. This conference laid the foundation for research in AI.
Neural Networks
One of the first steps in developing AI involves creating the neural network. Usually, it's the AI engineers who take on the task of building these networks. At the core of this structure is the node, or neuron, which operates similarly to a biological neuron, equipped with a bias that helps it decide how to process information. An AI engineer will figure out how many nodes are needed for the network. These nodes are connected through layers, typically categorized into three types: the input layer, the hidden layer, and the output layer. The input layer is responsible for receiving raw data, like a pixel or a character. The hidden layers serve as intermediaries, executing various functions and computations on the nodes and the incoming data. Finally, the output layer delivers the final decision and generates something meaningful.
Data and Training
Engineers sift through data to train and enhance the AI model. Typically, when dealing with large data sets, automation processes like pipelines are implemented to speed things up, allowing them to concentrate on what truly matters. Essentially, after the neural network is built, it needs to be trained to identify patterns and produce accurate results, similar to how a child's brain is trained to learn certain facts and ideas. For instance, if we aim to train our AI model to create a realistic image, we need to provide it with existing images of the desired outcome so it can learn to recognize patterns and discrepancies. Therefore, we must know exactly what is expected for the input and output to assess and compare it against existing data. This explanation is certainly a simplification, and there's more complexity involved, but that's the gist of what you need to know to get started.
Why understanding AI is ever more important
As previously mentioned, AI is rapidly growing and is expected to dominate the workplace. The US Bureau of Labor Statistics predicts that by 2027, around 75% of businesses will adopt AI technologies (US Bureau of Labor Statistics, 2025). If you're familiar with Y Combinator or active on LinkedIn, you may have noticed that many new startups are focused on AI. According to CNBC, in March 2025, more than 80% of the startups showcased were AI-driven (CNBC, 2025), and they appear to be thriving. In fact, it's now possible to build a million-dollar business in the AI sector with just 3 or 4 team members—something that was much less common five years ago. With major players like Meta, Google, and AI startups such as ChatGPT and Claude, it's increasingly crucial to understand the fundamentals of AI, whether you find yourself working with it or considering launching a business in that space. Even if you're not looking to enter the tech industry, AI has numerous applications across finance, healthcare, manufacturing, and law. From generating quick financial reports to tracking symptoms to creating organizational robots to using models that can instantly review case studies, even knowing how to simply use AI effectively is incredibly valuable.
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