How to Educate AI
Educating AI… A strange/new concept. We’re teaching machines how to “think” and make decisions, just like we would teach a child. But instead of guiding a kid through the ins and outs of life, we’re feeding algorithms (endless streams of data) and hoping they make sense of the world in their own way. The thing is, it’s not magic—it’s methodical, formulaic, and sometimes chaotic.
Where does it start? Data. Always data. It’s everywhere, it’s a bit overwhelming. Imagine trying to teach someone to read by showing them every book ever written. That’s kind of what we’re doing with AI. We gather mountains of data—texts, images, videos—and we throw it at these systems, hoping they catch on. It’s not just about dumping information; we have to clean it first. If the data’s messy, the AI gets confused. It’s like giving someone a textbook that’s missing half the pages—they won’t learn much.
And then there’s the process of teaching it to focus. We call this "feature extraction," but really, it’s about helping the AI recognize the important stuff. Basically: You’re walking into a room full of people talking, but you only want to pay attention to one conversation. Feature extraction is the AI figuring out which conversation matters. For a chatbot, that might be ignoring all the irrelevant noise and focusing on keywords that signal frustration or satisfaction.
Then comes the model itself which is technical. You’ve got all these different ways to build an AI—some are simple, others are brain-bending. You’re choosing between a bicycle and a spaceship. If you’re just solving a simple problem, you don’t need a spaceship. But for things like image recognition or understanding natural language… yeah, you’re going to need that spaceship, aka deep learning models. These neural networks are designed to mimic the way our brains work, which is wild because they’re really just a bunch of mathematical equations interacting in complex ways.
Training AI is the action. You’re teaching the model by showing it examples over and over again until it “gets it.” It’s like training a dog—rewarding it when it gets things right, nudging it in the right direction when it doesn’t. Only here, we’re not using treats; we’re using adjustments to the model’s parameters. Eventually, the AI starts making pretty good guesses about the data it’s seeing. But you can’t stop there. You have to test it on new data, stuff it hasn’t seen before, to make sure it’s not just memorizing answers like a student cramming for a test.
Why does all of this matter for every public-facing organization that exists? Think about how AI personalizes ads and products. It’s creepy but also kind of genius? They know what you want before you do because they’ve educated their AI to spot patterns in your behavior. The same goes for advocacy—imagine being able to tailor messages so precisely that you reach the exact audience you need to sway. We’re talking about AI that can understand public sentiment, detect rising issues, and even predict how people might react to certain policies. It’s a crystal ball, powered by data.
And automation: AI can handle the boring stuff, freeing you to focus on what matters. Take customer service. AI can be trained to answer the same questions a thousand times without getting tired, leaving humans to deal with the trickier issues. In advocacy, AI can scan social media for discussions on key topics, flag important conversations, and even step in to engage with people, all in real time. That’s efficiency at scale, something humans can’t do alone.
But here’s a threat: AI can also pick up on our biases. If you feed it biased data, it’s going to spit out biased results. Things can get messy. Businesses and advocacy groups have a huge responsibility here. They have to educate AI in a way that’s ethical, fair, and transparent.
In the end, educating AI is about scaling intelligence. It’s taking human intuition, decision-making, and problem-solving, and supercharging it with data and algorithms. For advocacy, it means amplifying voices, predicting outcomes, and driving campaigns more effectively. For businesses, it means reaching new markets, making smarter decisions, and doing it all at lightning speed. But we have to be thoughtful—about the data we use, the biases we introduce, and the outcomes we expect. If we get it right, AI can help us solve some of the biggest challenges, but only if we educate it carefully, responsibly, and with a clear vision of what we want it to achieve.
Ultimately: everyone who has a message or an audience, now needs to think about educating AI. We, at litenflame, are experts at this. Contact a@litenflame.com to learn more.
