Understanding artificial intelligence (AI)
AI is advancing quickly, and it’s raising a lot of questions. What can it actually do, and what are its limits?
Before getting into complex ideas, let’s start with the basics: What is AI?
At its core, AI is about getting machines to do things that usually require human intelligence, like recognizing patterns, making decisions, or learning from experience.
You’ve likely used AI already without realizing it.
Consider when a website recommends products you might like or when a music app creates a playlist. These systems feel smart because they learn your preferences.
But how does AI learn?
More importantly, how can it make useful decisions without being told precisely what to do each time?
The answer depends on the type of AI being used.
Different AI systems learn differently depending on what they’re meant to do, what kind of data they use, and how they’re built. To make this clearer, let’s walk through a simple example: building an app that can recognize handwritten numbers.
Examples of AI: How a handwriting detection app works
Let’s say we want to build an AI system that can recognize handwritten numbers in images. Here’s how that actually works, step by step.
- Collecting Data. AI learns by example. That means we need to feed it thousands – ideally, tens of thousands – of images of handwritten numbers. The more variety, the better. That includes everything from neat, textbook-style digits to messy scribbles from a five-year-old. This range helps the system learn what the numbers look like across different handwriting styles.
- Preparing the images for the model. To keep things simple, we’ll standardize the images as black-and-white and shrink them down to 28 by 28 pixels. That gives us 784 pixels per image. Each pixel becomes a data point that tells the model how dark or light that tiny square is.
- Using the right kind of neuron. To process this information, we use a digital neuron called a sigmoid neuron. Unlike a basic yes-or-no neuron, a sigmoid neuron can express degrees of confidence, like saying, “this pixel is 80% black.” This helps the system recognize subtleties in how numbers are written.
- Building the neural network. Next, we pass those pixel values through a layer of hidden neurons, say, 15. This is where the model starts finding patterns and making sense of how different shapes might represent different numbers. These hidden layers aren’t fixed. You experiment with the number of neurons and layers to find what works best.
- Generating the output. The output layer contains 10 neurons, one for each digit from 0 to 9. After processing the image, the model activates one of those output neurons with the highest confidence score. If the “3” neuron lights up the most, the system identifies the image as the number 3.
- Training the model. Now comes training. The model compares its guess to the correct answer and adjusts its internal settings, called weights, accordingly. Over time, it improves at making accurate predictions, even on handwriting it hasn’t seen before.
Examples of AI in everyday life
The handwriting detection app is a good example of established AI. These technologies have been around for years, tested at scale, and are now reliable parts of everyday tools and systems.
Other examples include:
- Object recognition, like when your security camera sends an alert if it detects a person, but ignores a squirrel or a tree branch.
- Facial recognition, used in phone security or photo apps that can tag people automatically.
- Chess-playing AI, such as IBM’s Deep Blue, which beat world champions by calculating millions of possible moves.
- Recommendation engines, like the ones on Netflix or Amazon that suggest what to watch or buy next.
- Voice assistants, like Siri or Alexa, which respond to commands and search questions.
These AI examples have a specific job and do those jobs very well. They’re purpose-built and often very efficient and precise. And when they’re not, those areas can usually be pinpointed and improved decisively. But these examples do not create anything new.
What does the future of AI look like?
Beyond established tools, newer forms of AI are advancing fast, especially generative AI. Unlike systems built for a single task, generative models can be adapted for many different things.
Some examples include:
- AI models that generate realistic video with vintage film styles or high-end special effects.
- Image generators that turn abstract or surreal prompts into detailed visuals.
- Large language models that can write poetry in the voice of a fictional character and, moments later, break down complex legal contracts into plain language.
This flexibility opens the door to powerful creative and analytical tools, but it also brings unknowns. As AI becomes more capable, it’s important to understand how it learns and solves problems.
We’re already using it every day, often without realizing it. And the more we experiment with it, the more we stand to gain. Curiosity, not hype, helps teams unlock what’s useful.