Five Effective Steps to Train AI for Your Business

Artificial intelligence (AI) has earned a spot in many industries. A study conducted by IBM found that roughly 42% of companies with more than 1,000 employees have reportedly deployed AI in their business. This is a large percentage, and many more companies are expected to explore this technology’s benefits in their businesses.

However, implementing AI can require planning to ensure it runs as expected to guarantee the business will benefit from its use. This will often require a type of machine learning, such as Reinforcement Learning with Human Feedback, or RLHF, which results in the machines learning from both trial-and-error and human guidance. The RLHF approach will refine AI, such as chatbots and voice-to-text systems, to ensure it understands and responds better.

Knowing how to effectively train AI through RLHF will assist your business in the long run, especially if this type of technology is going to be used more often and in more sectors.

1 – Begin with a Pre-Trained Model

To start, it is best to use a pre-trained model. This means the model will already have a wide understanding of the basics, including languages, but there will be no specialization. It is the specialization that you are looking to build here. Beginning with a pre-trained model is a big advantage.

As pre-trained models will already understand large amounts of data, time and resources required during the initial training state will have been saved. This will ensure the following steps are increasingly focused and the training will be specific.

2 – Supervise the Fine-Tuning

After selecting a pre-trained model, the next step is where this model will undergo extra training regarding a specific domain or task. Labeled data will be used at this stage to help the AI model create accurate and relevant outputs. This is where human guidance begins.

Human judgement will steer the model towards the preferred behaviors and responses. The person, or people, in charge of training will need to be careful when selecting data so the model will adapt to the task’s specific requirements.

3 – Train a Separate Model for the Desirable Outputs

For the third step in training AI, you will train a different model to identify and reward the desired outputs that are generated. Some approaches will integrate reward modeling into the overall training loop, though.

If completed separately, this AI model will assess the outcomes and score them based on certain criteria, such as accuracy, alignment with the preferred outputs, and relevance. The scores given will act as guidance to produce better responses.

4 – Use Reinforcement Learning via PPO

The AI model will next go through Reinforcement Learning via PPO, also known as Proximal Policy Optimization. This widely used complex algorithmic approach is crucial in interactive machine learning to let the model learn when interacting with the environment in real-time. It has been proven to make better use of training data when compared to previous techniques.

The decision-making process will be honed to rewards and forfeits, allowing it to learn and adapt. PPO has been used for AI assistants, chatbots, robotics, and more.

5 – Test in the Real World

Once all the other steps have been completed, it is now time to test the AI system in the real world, or as close to the real world as you wish to get. This is often a selected group of individuals to evaluate and challenge the system in a variety of situations. It will be tested in its responses, ensuring it can cope with practical functions and unexpected scenarios.

These individuals will test the AI system to guarantee it will work correctly, and that it is completely ethical in its sequences. This will include potential harm, regulatory compliance, and biases in training data.

Throughout this post, and with a better understanding of RLHF, it should become very obvious how important human involvement is in the development of artificial intelligence. Without human input, AI would not be as attuned to the values of society or become intelligent. So, it is essential to utilize human guidance when developing AI systems.