The importance of continuous learning in AI systems
AI systems should be designed and deployed with the ability to continuously learn and adapt, in order to promote ongoing improvement and prevent negative consequences. The goal is to retrain models using updated data.
Many machine learning models assume that data will always be similar to the one it was trained with. Notwithstanding, this is not always the case. Many of the models operate within environments where data changes rapidly and ‘concept drifts’ may likely happen, thus negatively impacting the dependability and accuracy of the model’s predictions. To deter ‘concept drifts’ from taking place, it is important to monitor models and retrain them when the data is inaccurate.
AI developers should engage in continuous learning to stay up-to-date with the latest developments in AI and to improve their skills. Continuous learning is a crucial step in developing AI. From a technical perspective, it is an important improvement over narrow AI, which is a goal-oriented AI designed to perform one task.
A model should have the capacity to learn from large datasets, in the same way humans do. It also ensures that developers are more skilled in adaptability and scalability.
Similar to the human mind, models need to filter and process data in a short period to minimize the quantity of data that should be stored and maintained. Every job including AI developers needs certain skills which are not static.
Skill requirements evolve rapidly since technological development happens frequently today. This means that AI developers should continually upgrade their skills. They should be willing to embrace lifelong learning to be updated and remain competitive.
Developers should further be open to feedback and engage stakeholders, including civil society organizations, regulators, and users to ensure that their systems meet societal needs and align with human rights values. This can involve attending workshops, training programs, or pursuing further education.
Recommendations for ensuring continuous learning in AI systems
Periodic Retraining: Developers should be regularly retrained to be updated on the latest data and trends.
High-quality Data: The acquisition of high-quality data free from inconsistencies, duplicates, and errors is critical to creating a strong continuous learning AI system that will make predictions based on it and learn from this data. Poor quality data means that the system cannot model the issue, resulting in suboptimal performance. In addition, diversity in the training data can ensure the system learns from various examples.
Right Algorithms: The selection of appropriate algorithms is important for achieving effective results. Various algorithms suit continuous learning systems, including online learning and reinforcement learning algorithms. Notwithstanding, the selection needs to be problem-specific and relies on the type of data used for training.
Performance Monitoring: Regularly monitoring performance and using metrics relevant to objectives is important for evaluating an adaptive AI system’s effectiveness.