As you might explain to a friend or adult family member, machine learning is the process of training a computer model using datasets and algorithms. Really, these algorithms that form the heart of machine learning have been around for decades, but computers have only recently reached the level of processing power needed to use the techniques in practical scenarios.
Sounds good, right? But have you ever tried to talking about any of the above with a child? Not the easiest task.
One way to simplify the whole thing for kids is explaining machine learning to them as this:
Machine learning is basically getting a computer to perform a task without it being explicitly programmed to do so.
But while that explanation seems a lot more digestible, it still requires an understanding of "programming" and a bit more.
So, here are 6 tips on how you can make that complex conversation go a little bit smoother when describing machine learning to your kids.
1. Start by comparing machine learning to coding
Your kids have probably watched a battle bots competition before, right? You know, where robots are coded with an algorithm - a set of instructions that are followed to accomplish a task; a computer’s thought process - to attack and "battle" each other.
Well, if machine learning was used in this situation, the robot itself would make a decision in the moment based on the information it has been given. Meaning, the robot would choose to perform either option A or option B, rather than being told through code to always perform option A no matter what.
So, while we like to say coding is like feeding a computer a set of instructions, instead of coding software with specific steps to follow, machine learning trains an algorithm so it can learn how to make decisions for itself.
2. Introduce AI to the conversation
Let's face it—"AI" is a lot more familiar and friendly to kids than something as intimidating as "machine learning."
So, continue down that path. Let them know there is no AI without machine learning, and that AI models and tools are developed using a process called machine learning.
Explain that popular AI chatbots like ChatGPT are trained using massive datasets from sources like the internet (machine learning).
Read More: How Does Artificial Intelligence Self-Learn?
3. Share Real-World Machine Learning Examples
To kids, things are just things until they see them in action. Educate them on the fact that machine learning isn't just a complicated definition, but a valuable process used to find solutions to various challenges that arise across a variety of different scenarios and environments.
For instance:
Smart Cars: Machine learning can evaluate the driving environment and driver condition based on information obtained from different external and internal sensors.
For example, a smart car is able to make an observation and detect an object, and can then identify that object using machine learning. Since there are so many different objects in the world, it would be nearly impossible to explicitly code in what every object is or could be into the car's framework. However, if you teach the car to identify objects through machine learning, it can make those decisions itself.
Music and video recommendations: Kids familiar with music apps have probably wondered how the app can suggest other songs they might enjoy listening to. Same with YouTube—how does it know which video kids might want to view next?
All of this is made possible with machine learning. The algorithm is trained with previously watched videos, and then from that info, builds and improves an algorithm that defines the listener's or viewer's taste.
Web search: The process to find results after searching for something in a search engine is incredibly complex and uses machine learning. How does Google know that all the thousands of results listed are related to a search inquiry?
No one is manually categorizing everything on the internet—it's all a very advanced form of AI and machine learning that decides which images are "dogs" and "cats" and which articles are related to the "Loch Ness Monster" or "Bigfoot."
4. Connect Machine Learning to Kids' Interests
A complex machine learning model like ChatGPT requires massive amounts of data, training, and many hours of refining through feedback by human developers. This helps the machine categorize when a task is performed correctly or not.
Again, sounds good in theory, but kids just can't grasp such an explanation.
But what if you connect it to something like Pokémon?
For example, if you ask ChatGPT which Pokémon can evolve from a Thunder Stone, its goal is to reply with an answer close to what a human would say.
For a human to answer that question, they have to perform tasks like:
- Understanding the question (what are Pokémon evolutions and a Thunder Stone?)
- Finding a list of all existing Pokémon
- Researching each Pokémon's unique evolution requirements
- Compiling a list of which ones can use a Thunder Stone to evolve
If you have your child attempt to answer, they'll quickly see there are a lot of Pokémon, so this process would take a long time by hand.
This is what makes machine learning so helpful because you can use a computer to automate this process for you.
5. Advance with how machine learning works
While all of the above is good and great, is it enough? For those who want to know more, you can get a little more technical, while still using the previous tips as a foundation.
For instance, as mentioned, machine learning is all about training an algorithm. But, to go further, in order to train an algorithm, you need a neural network—which is a set of algorithms inspired by biological neural networks.
To connect this neural network to something they know, explain that it's actually modeled after the human brain, which consists of individual neurons connected to each other. In machine learning, a neuron is a simple, yet interconnected processing element that processes external inputs.
A neuron receives data through its inputs, processes the data using weights, biases, and an activation function, then sends the result onward as its output.
Once you've got a neuron that takes input data and outputs a value, you will have to train it by adjusting the weights and biases inside the neuron until the output is ideal.
Machine Learning uses these neurons for a variety of tasks like predicting the outcome of an event, such as the price of a stock, or even the movement of a soccer player during a match. A neuron uses input data from any past events to predict the outcome.
6. End with explaining machine learning as a career
It should be obvious by now that machine learning is one of the coolest emerging fields in tech—but why else should your child hop in and start learning about it?
In the coming years, many companies like DeepMind and OpenAI hope to solve general artificial intelligence, which is a term for an AI that can learn and perform any task put in front of it. This breakthrough is developing, but it has the potential to revolutionize how human beings interact with technology, the job market, and society in general.
In the shorter term, machine learning has practical business applications like analyzing large volumes of data, powering self-driving vehicles, and assisting medical diagnoses. As AI research advances, the number of tasks it can perform will only increase. Companies are already desperate for AI experts and aggressively hiring those with expertise in the field.
Sound like a good fit? Students can take their first steps towards revolutionizing technology and society this summer through any of the cutting-edge courses below! You can also get them to learn more with these artificial intelligence games for students.
Intro to Python coding (virtual tech camp)
Machine learning tutoring (1-on-1 online lesson)
Python summer camp (coding for machine learning)
Artificial intelligence summer camp (data sets, probability, and stats)
Machine learning summer camp (for coding deep neural networks)
View all coding courses for kids