How do you know that the AI model you are working with tells you what you are looking for? How can you be certain of its answers? Are you making things actually worse?

Choosing the wrong AI model for your task can lead to various issues, including inaccurate predictions or results, overfitting,  underfitting, and inefficient use of resources.

Imagine you're trying to sort out your emails, separating the spam  from the important communication. You decide to use a method that doesn't know anything about what 'spam' or 'important' means. It just  groups similar things together.

So, instead of sorting out the spam, it might group together all the long emails, or all the emails sent at night. That's not really helpful, right? You might end up missing an important email because it was long or sent late at night. Or, you might still get lots of spam because they were short or sent during the day.

The key takeaway is: choosing the right tool for the job is crucial.

What tools (AI models) are there? and for What?

Let's try simplifying things.

• Supervised Learning:

Imagine you're learning to cook from a recipe book. The book tells you what ingredients to use and how to use them to get the dish you  want. This is like supervised learning - the computer is given data with the 'right answers', and it learns from this to make future predictions. It's used when we know what the outcome should look like.

• Unsupervised Learning:

Now, imagine you're in a kitchen with lots of ingredients but no  recipe book. You experiment with different combinations to see what  works. This is like unsupervised learning - the computer isn't given any 'right answers', it has to find patterns and relationships in the data  itself. It's used when we don't know what we're looking for.

• Semi-Supervised Learning:

This is a mix of the two above. It's like having a few recipes and a lot of ingredients. You can follow the recipes you have and also try out new things. It's used when we have a little bit of data with 'right  answers', and a lot of data without.

• Reinforcement Learning:

Think of playing a video game. You learn by playing the game, making mistakes, and trying not to make the same mistakes again. The computer does the same thing - it learns by trial and error to achieve the best result. It's used when we want the computer to learn from its  mistakes.

• Transfer Learning:

Imagine you've learned to ride a bicycle and now you want to learn  to ride a motorcycle. Some skills you learned on the bicycle, like balancing, will help you with the motorcycle. Similarly, the computer uses knowledge from one task to help it with a related task.

• Active Learning:

This is like learning by asking questions. If you're studying and  you come across something you don't understand, you ask someone or look it up. In active learning, the computer can ask for help when it needs  it. It's used when we don't have a lot of 'right answers' for the  computer to learn from.

The choice of method depends on what you want to do, what data you have, and what resources are available. Keep that in mind next time you are expecting an AI model to provide you with what you think will be accurate.