With so many different versions of AI circulating around the internet, it would be strange to wonder if you can make your own. What might surprise you is that it’s actually not that difficult. Here’s a general overview of what you’ll need.
Decide on the type of AI
The first thing you will need to decide is what type of AI you want to create, as there are many different kinds. The kind you want will depend on what you want to use it for. For instance, you could create a Machine Learning (MLM) for tasks like image recognition, predictive analytics, or language processing. Chatbots are good for conversational-based applications and helping customers on your website find the answers they need.
AI can also help you with game development. Game AI can create AI opponents or NPCs. You might also need something custom if none of the ideas here fit your needs perfectly, which is all the more reason to build one.
Setup the development environment
Once you know what type of AI you want to create, it will be time to set up your work environment. Since Python is a favorite here at GeekSided and we have many articles covering ways to put it to use, we’ll stick with that to create our system. However, you can use many other languages, including Javascript and C++, if you are more comfortable with them. For Python, there are several libraries and frameworks you will want to get familiar with before you start coding.
You will also need an IDE. We cover several good ones for Python in another article, and you can use any that you are more comfortable with, but we will use VSCode for this project. You might also install important tools using pip, such as numpy, pandas, or matplotlib if you haven’t already done so.
Gather or generate data
AI requires data to train models. You can get the data by using existing datasets from Kaggle, UCI Machine Learning Repository, or other sources or generate your own through simulations or manual entry. For simple AI-like rule-based systems or basic chatbots, you may not need much data. If you have a powerful GPU, you can train larger models locally. Otherwise, stick to smaller models or rent cloud GPU services.
Top modern programming languages driving the future of development
Build your AI
- Start with a clear goal (e.g., “predict housing prices,” “play chess”).
- Write code to preprocess your data.
- Design and train your AI model:
- For machine learning: Split your data into training and testing sets.
- For deep learning: Use a neural network architecture.
- Test and validate your AI to ensure accuracy and reliability.
Learn and iterate
Experiment with different algorithms and data sets until you get the results you want. It’s also important to give the computer time to learn.
In the next part, we’ll discuss how much computer power and space you need, install the libraries, and set up VSCode so we can start adding some code.
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