Artificial Intelligence (AI) is everywhere. From your phone's autocorrect to self-driving cars, the term “AI” is thrown around so often that it seems to mean anything and everything. But what actually counts as AI? Is a simple calculator AI? What about your spam filter? Or the chatbot you are arguing with everyday? AI is actually a very broad and sometimes misleading term that addresses a range of technologies. Some systems follow basic rules, while others seem almost human like in their ability to understand and generate responses. Understanding these differences is a must in making sense of AI’s role in our lives.
Before we start, let’s agree on a simple fact: AI is not magic. It’s just a tool. A tool that ranges from simple automation to highly sophisticated systems. The more we understand these layers, the more we can appreciate AI’s strengths and limitations.
To illustrate just how broad the term AI really is, take a look at the diagram below. “Artificial Intelligence” is the general name we call machines doing tasks that normally require human intelligence. Which means they are "smarter" than an average algorithm. It's the difference between a traffic light that works with a fixed timer, and a traffic light that works based on how busy is the cross section.This "smart-ness" has different levels that have been divided based on algorithms level of sophistication, or in other words "being more similar to us".Within AI, we have “Machine Learning” (ML), algorithms that learn patterns from data rather than following explicit instructions. Nested within Machine Learning, is “Deep Learning” (DL), which not only learns like human, but uses neural networks (basically a digital version of neurons we have in our brains) with many layers to extract increasingly abstract representations from raw inputs. (we will get to that later)And inside this deep learning world, we find famous “Large Language Models” (LLMs) which we are getting familiar with since November 30, 2022 when the ChatGPT borned. The big paradigm shift in human history.
First Things First, What is an Algorithm?
Every computer program or digital product you encounter with, has 3 main elements: Input, Process, and the Output. Inputs can be any type of data coming from the user or the environment, words, numbers, images, sensor readings, or even when you press a button on the device. Outputs also are one of these types we have mentioned, or even an action from the device, like a robotic arm. The processing part which determines the output based on the inputs is called Algorithm. If you think about it, everything is an algorithm, even for human beings.
Let's begin with a simple example: making a cup of tea. First, you have to turn on the stove, wait for the water to boil, and then pour the hot water on the tea and wait for 2 minutes. Same story goes machines with more or less steps.In computer science we have different type of algorithms: Sorting, Searching, Path Finding, Optimization, Compression and many more, but that is for another blog post, and since this one is about Artificial Intelligence, I'm going to give you an example about Decision Making Algorithms, which captures the concept of Intelligence in machines much better. When we think about intelligence in humans, one of first reasons we call ourselves the intelligence beings, is that we can choose and make decisions not only based on instinct, but also based on a "logical" process in our mind we call "thinking". Humans gather inputs (our senses), process that information (our thoughts), and produce outputs (our actions). Similarly, machines use sensors, data, and algorithms to make decisions and act. What makes decision making algorithms particularly fascinating is how they mimic human reasoning in a structured, logical way, often following steps like “if this happens, then do that.”
Let's talk about another everyday example: a traffic light. In the beginning, the “algorithm” was simply a human decision maker, a police officer standing at the intersection, instructing one side to stop and the other side to go. The Input was the officer’s observation of traffic: how many cars are in each lane, whether there are pedestrians waiting to cross, and so on. The Processing was the officer’s decision-making process, simply based on common sense and experience. The Output was just a signal to the drivers. A hand gesture, a whistle, or a traffic sign movement.To reduce the officer’s workload, a simple digital system was introduced: a traffic light controlled by the officer. The officer would observe the traffic (Input) and press buttons to switch the light from red to green or yellow (Processing). The Output was the automated light changing, providing a more visible and consistent signal for drivers.Then thanks to transistors, we removed the need for constant human input by introducing a timer. The Input here was time: a predefined interval for each light (green for 30 seconds, yellow for 5 seconds, and red for 30 seconds). The Processing was a simple algorithm in early versions, just a loop that switched lights after a programed duration.
But as we know these days everything is controlled with image processing and more complex systems. the traffic light uses Inputs from sensors or cameras, like the number of cars waiting, the presence of pedestrians, or even the time of day. The Processing involves more sophisticated algorithms which deep down can be a series of rules and conditions that dictate what the light should do based on the current conditions. This is a simple example of our first machine learning algorithm, a Decision Tree.
This simple Decision Tree Flowchart tells the traffic light: IF there are cars in the main road AND there is no car on the other one, turn the main road light green, and the other one red. BUT if there were cars on the other side too, just use a timer in a way that balances out the traffic flow. Also be careful if there are pedestrians waiting, give the priority to their signal first. But how do these machines "understand" the conditions of the traffic to balance the flow between them? Keep in mind that every decision made by a machine represents a glimpse of how “intelligence” is built into the digital world, inspired by nature and humans.
