Enhancing Human Learning
How AI Models Work and What We Can Learn from Them
What is AI?
AI, or Artificial Intelligence, is like a super-smart computer that can learn and make decisions. It learns and makes choices in surprisingly similar ways to how people learn.
Training Needs Brute Force and Power
Training is the first step in making an AI smart. Imagine a student learning to read. The student needs a lot of books, time to read, and a quiet place to practice. This is just like AI training. The AI needs a lot of information (called data) and powerful computers to help it learn.
Practice and Patience: Just like AI needs a lot of data and time to learn, we also need a lot of practice and patience. Whether learning to read or mastering a musical instrument, repetition and time are key.
Training takes a lot of energy and requires us to work effortfully.
Inference Takes Compute
After training, the AI moves to the inference stage. This is when the AI uses what it has learned to make decisions or predictions. It's like a student who has learned how to read and now uses that skill to understand new stories or solve problems on a test. The AI doesn’t need as much power as it did when it was learning, but it still needs some computer power to do its job.
Applying Knowledge: Just like AI uses what it learned to solve problems, we can use what we know to solve real-life problems. This shows the importance of applying what we learn in different situations.
Inference, applying knowledge, takes up energy but not as much as training.
Lessons We Can Learn from AI
1. Practice Makes Perfect:
AI needs lots of data and time to learn.
We improve with much practice and patience, just like practicing reading or playing an instrument.
This requires a lot of energy and effort. The high requirements prevent many people from learning new or difficult things.
It is helpful to see that the energy and effort spent on learning are an investment in ourselves for the future because once we learn something, it takes significantly less energy to apply that knowledge to our lives.
2. Diverse Learning Experiences:
AI learns best with different types of data.
We learn better when we have different experiences, like reading various books or trying new activities.
Varied inputs give us a unique advantage. We can make connections that others may not and see things from new perspectives.
For example, I learned a lot about music production from studying chefs. Professional chefs have systems and methods for their creative process, which I have applied to my own process for making music. Understanding mise en place when making music has given me structure when creating. Additionally, the concept of garbage in, garbage out has shown me the importance of using high-quality sounds to make high-quality music, similar to how a chef would use high-quality ingredients to make a high-quality meal.
3. Using Knowledge is Crucial:
AI uses what it has learned to solve new problems.
We need to use our knowledge in real-world situations. Our ability to solve problems directly relies on our training. Training does not have to mean what we learn in traditional school. Training can come from any experience that has lessons to offer.
It’s worth emphasizing that using knowledge (inference) takes significantly less energy than learning (training).
Learning can be tough and effortful, but using that information will be effortless.
4. Continuous Feedback:
AI gets better with feedback from its predictions.
We also improve when we receive feedback on our work, whether from teachers, parents, peers, or a marketplace.
Feedback allows us to correct our mistakes when we get things wrong. I would argue that feedback could be one of the fastest ways to learn and improve.
AI, or Artificial Intelligence, is like a super-smart computer that learns and makes decisions similarly to humans.
The first step in making AI smart is training, which requires a lot of data, time, and powerful computers, similar to how humans need practice and patience to master a skill.
Once trained, AI uses inference to apply its knowledge, similar to humans using what they've learned to solve problems.
Practice, diverse learning experiences, applying knowledge in real-world situations, and continuous feedback are all crucial for human learning, which are seen clearly when training AI models. These insights demonstrate that learning is an investment in our future, and while it requires effort, the application of knowledge becomes easier over time.