If you are an AI developer, be aware that this is not a job instruction. This is an attempt to outline 7-steps to build Artificial Intelligence for people that don’t normally build AI or are planning on building AI.
Like with every good old project we plan, we start with:
Problem Identification and Goal Definition: The first and foremost step is to identify a problem that AI can help solve. Whether it's optimizing supply chain management, predicting disease outbreaks, or enhancing customer service, defining clear objectives is essential.
And because the fundamental thing we need to build AI is data, our second step is:
Data Collection and Preprocessing: AI thrives on data. Collecting relevant data is crucial, and it often involves cleaning and preprocessing to ensure it's in a usable format. This step lays the foundation for the learning process.
Like with a lot of things, with AI, developers also need to choose the most suitable method.
Selecting an AI Technique and Developing an Algorithm: Depending on the nature of the problem, you'll choose an appropriate AI technique. This could range from traditional rule-based systems to advanced neural networks. Developing an algorithm involves designing a mathematical model that can make predictions or decisions based on the data.
Okay, once all that work is done, we are going into (probably iterative) process of training, evaluation and optimizing:
- Model Training: This is where the magic happens. During the training phase, the algorithm learns from the dataset, and this process depends on the chosen learning method (supervised, unsupervised, or reinforcement).
- Model Evaluation: After training, it's essential to assess how well the AI model is performing. Evaluation metrics depend on the task at hand and may include accuracy, precision, or recall.
- Model Optimization: Based on the evaluation results, the model is fine-tuned. This could involve adjusting hyperparameters, collecting more data, or using more advanced techniques to improve its accuracy and efficiency.
And finally, one day, the AI system is ready for deployment:
Deployment: Once the model's performance is as expected, it's time to deploy it in a real-world environment. This could mean integrating it into a mobile app, a website, or an industrial automation system, depending on the application.
Don’t worry, you’re a not expected to remember all the details but here are the key take aways:
- With AI we can solve various problems
- For AI development, we require data, lots of data
- Algorithms are a fundamental part for AI development
- AI needs to be trained and optimized before deployment
And most importantly, humans are involved in most of these steps. This can be good and bad. But let’s talk about this another time.