Artificial Intelligence (AI) is one of the most fascinating and rapidly developing fields in technology. Thanks to the advances in computer power and algorithms, we can now train large and complex AI models that can perform tasks such as speech recognition, image classification, and language translation with great accuracy. Some popular AIs include ChatGPT, Jasper, Midjourney, or the latest by google, Bard. After seeing so many AIs popping up, I became curious and did a bit of research into how they work, and I'm excited to share my findings with you in this article!
AI is, in simple terms, a computer program that has decision-making abilities based on the data it was fed. To accomplish this, AI systems use special algorithms with a set of rules and procedures, also known as AI models. There are multiple methods that can be used to train these AI models:
Supervised Learning
Supervised Learning AI models are the models that are trained on labeled data. The labeled data is a set of examples with inputs and outputs that are provided to the model during training. The goal of the model is to learn about the relationship between the inputs and outputs so that it can predict the outputs for new inputs that it has not seen before. For example, a supervised model could be trained on a dataset of images of cats and dogs and their respective labels. The model then can be used to classify new images as cats or dogs.
Unsupervised Learning
Unsupervised Learning AI models are the models that are trained on unlabeled data.
This means the program has to find connections and patterns in the given data on its own without any instructions. Unsupervised learning is used in many real-world applications, such as market segmentation, recommendation systems, and image compression.
Reinforcement Learning
Reinforcement Learning AI models are models that learn from the consequences of their actions. In reinforcement learning, an AI agent interacts with an environment and based on the steps it takes and the feedback it receives, it learns to maximize a reward signal. It is used in various real-life applications such as gaming, self-driving cars, robotics, and finance. For example, in gaming, AI systems can learn to play games at an expert level, and self-driving cars use reinforcement learning to make decisions on actions such as when to accelerate, brake, or turn.
Conclusion
In conclusion, those are some of the AI model training methods that I have found. Each of them is suited for different types of scenarios and provides different benefits. There's much more to learn about AI, and I am excited to start diving into it. Thank you for reading, and feel free to leave me your thoughts and opinions on the article.