Two simple questions to determine whether your problems should and could be solved by AI
I am an AI Consultant at AI Singapore. I help teams to undertake the development and implementation of AI models within their organisations.
One of the questions frequently asked by clients is ‘can my problem be solved by AI?’. To answer that, I suggest asking two questions to assess the possibility of using AI.
Question 1: Can the problem reasonably be solved by IF and ElSE statements?
The first question serves as a filter to avoid using a chainsaw to cut butter.
It is unnecessary to use AI if the problem is solvable by IF and ELSE statements (i.e. rules-based system). The rules-based system is more explainable and cheaper and faster to implement.
AI excels when the problem is too complex to be handled by rules-based systems. For instance, it is impossible to write rules to identify dogs in pictures. AI could look through thousands of dogs images to learn how to identify dogs without any explicit rules written.
Note the ‘reasonably’ in the question. AI could be considered too if the solution requires thousands of IF and ELSE statements, as such complex rules-based system will eventually become hard to maintain and update.
Question 2: If you pass the same dataset to an industry expert, could the expert make reasonably accurate predictions?
The second question serves as a sanity check on whether the dataset contains relevant information needed to train an AI model.
If the use case is to predict hospitalisation of kidney patients, the AI model won’t be able to learn if the dataset contains only patients’ favourite colour. Sounds simple and commonsense? Not really.
“It is a capital mistake to theorize before one has data. — Sherlock Holmes
Datasets could have hundreds of columns and sometimes it is hard to tell if the dataset truly contains relevant information for the intended use case. When asked, companies would like to believe their datasets are comprehensive, but it often turns out otherwise.
The best way to determine whether a dataset contains relevant information?
- Pass the same dataset to industry experts and check if they could make reasonably accurate predictions.
- Ask the industry experts their thought process in making their predictions.
There could be relevant information in the dataset if the industry experts perform well. However, industry experts have the prior background and tacit knowledge which might not be represented in the dataset. The AI model won’t be able to learn if this knowledge is not represented in the dataset.
For instance, if the use case is to predict which companies are likely to default on payments, the industry experts might already know which are the infamous customers that frequently default on payment. They will probably perform well even if they are just given a list of companies’ names, but the AI model won’t have access to the same tacit and background knowledge.
Determine the right approach, then ensure relevant data is available
Can your problems be solved by AI? Think about using AI only when simple IF and ELSE statements cannot help, then ensure all relevant information is represented in the dataset.