Do You Need to Develop Your Own AI Model for Your Enterprise?

First of all, let’s understand what is an AI model. An AI model is a software program or mathematical system that processes data to recognize patterns, make predictions, or automate decision-making. It is created by training an algorithm on a dataset, enabling it to learn from examples and generalize to new, unseen data.

In layman’s terms, it is the brain of your AI system – like the CPU of your PC. Do not be confused between AI models and machine learning algorithms. AI models use ML algorithms and train themselves to be AI brains. Thus, once this brain has been filled with knowledge and skills, a general software developer can include the AI model file (.pt, .pb, .keras, etc) in their code and start building an AI app that accepts user prompts.

The popular AI models in the market are GPT-3, GPT-3.5 and GPT-4 developed by OpenAI, the mother of ChatGPT.

This begs the question – do I need to build my own AI model? Well, this depends on what sort of AI brain you wish to have. If you’re looking for an AI brain to converse in English, look no further than LLM (Large Language Model). There are quite a few premium and open-source AI models available. GPT, for instance, is a paid LLM model that is meant to decipher and respond to human languages. For open-source models, you may visit HuggingFace.co – this is like the GitHub for AI.

However, if you’re looking to build a unique AI brain to perform specific tasks, you may need to develop one of your own. And it will cost you. Building an AI model generally takes a huge amount of human and computing resources. You need torrents of data for the model to learn from and lots of training time and AI thinking power – measured in GPU use. The data preprocessing aspect alone requires your data scientists to prepare and clean the data to ensure you are training your AI the right skills and knowledge. It’s like teaching a kid to do the right things. In the AI fraternity, there’s a famous saying, “Garbage in, garbage out.” Your AI model depends on the quality of the data you are teaching it. If you tell it the sky is green, it will think so and tell others.

Another interesting phenomenon in training AI is called hallucination. An AI model can lie by giving misinformation. This happens not because you train it to lie, but simply because it doesn’t know the answer to something. This can be caused by insufficient information during training, or it was taught with too specific information that it doesn’t know something in general. In AI terminology, this is called overfitting.

On the flip side, we have an underfitting phenomenon where the AI model isn’t smart enough. Basically, it is unable to perform the task we prompt it to do. For example, a generative AI model that is unable to accurately generate the face of Cristiano Ronaldo, the famous soccer player. This happens because perhaps it was not well-trained to recognize the faces of global sports celebrities.

So, you see, building an AI model is not a walk in the park. It costs time, money, and talent. But if you think, perhaps you want to build a smarter AI model that could generate pinpoint accurate images of sports and athletes and you have mountains of these photos sitting in your hard disks, then you might have a compelling AI model use case. So, go and AI it!