AI programming involves creating tools, software or programs that possess the ability to learn from extensive datasets, enabling them to predict outcomes and discover easily overlooked patterns and behaviors. It’s an approach that can be applied to a wide variety of business issues, from boosting efficiency by automating repetitive tasks to improving customer experience and accelerating innovation.
The most common way to evaluate a machine learning algorithm is accuracy, which measures how often it produces a result that agrees with the real world — in other words, how much did it get right? But that’s just one of many considerations to take into account when evaluating AI. Evaluators need to consider the other facets of an AI’s performance, including explainability, scalability, and adaptability. Informed evaluation also looks to the future, anticipating how AI/ML capabilities may change over time and ensuring that an organization is prepared to handle those changes without technological constraints.
A good place to start is by looking at the model’s performance on a given problem. But it’s important to remember that a given model may perform very differently on different data sets. The best way to compare the results of different models is to use a “representative” test set that’s as similar as possible to the target problem. This way, engineers can assess the accuracy of different algorithms on a consistent basis. For example, in medicine, the most representative test case is probably a mammography screening population.
Another important factor on evaluation of the program is the quality of the data. It must be clean and well-organized so that the algorithm can train effectively on it. It must be large enough to provide a good estimate of the error rate. And it should be sufficiently unbiased to ensure that the model is not biased in some way, for instance by training on the same data for too long.
Lastly, the ability of an AI to adapt to changing circumstances is a crucial aspect of its value and usefulness. It must be able to cope with new data and situations that the original developers did not anticipate. This means being able to modify the model or create an entirely new one when necessary. It also needs to be able to track in real-time its progress, allowing the user to adjust its parameters as needed and improve its performance.
Currently, the science of repeatable and useful evaluations for AI is very young. There is a lot of work that needs to be done, such as increasing funding for departments of computer science and research organizations focused on this area. There is also a need for better standards and more rigorous testing to improve the quality of evaluations. Finally, it is critical to promote an environment in which developers and users can share their experiences with a given tool to help the community make informed decisions and provide feedback on improvements.