By Kock Chee Kiong, ARM, ARiMI-CERM.
Routine work activities are being handled by machines with increased speed, precision and consistency. Problems and anomalies would show up along the way while work is being done. Resolving such problems promptly will depend on the skill set and experience of the personnel handling the machine to tackle these issues to the problem. It relies very much on experience and also collective agreement among co-worker/subject matter expert/supervisor for the decision to be made and the measure taken thereafter. There is still an “intuitive” gap between human and machine such as Artificial Intelligence (AI) on problem solving and application of solutions within short time span in engineering work.
The approach to solve problems lies in the action and decision of human, which is dependent on knowledge, past experience, gut feeling and intuition. The development on big data analysis would help machine to make better informed decision. Machines learning should include solving issues that are not in databases and previously not analyzed. Algorithm involving predictive analytic and neuro-science would help machine and AI to develop “intuitive” solution.
Intuitive Problem Solving
Human should work together with machine and AI to solve problems. Problem, in my own explanation comprises of a series of interlinked issues that cause disruption and lead to a unfavorable event. The quantification of issues and problems will be dealt by human while the computing works to be performed by machines. Machines would help to generate the list of possibilities on the issues without any form of bias and human subject matter expert would help to eliminate unlikely issues from the list that are identified by machine by assessment. The key in problem solving is telling the right story by getting the right issues leading to the problem and formulating the correct approach to address these issues while solving the problem. The “spark” to generate out of the box idea could be derived from the approach towards problem solving.
Refined information in Database
Machine learning to be brought to the next level such that continuous learning can take place. All result of the selection and analysis will be kept in the “sub-conscious” database that are short term and this enable machines to make a more intuitive approach towards problem solving, while deriving possible scenarios and addressing newer issues. AI and machine should not be seen as external consultant or tools, but an internal facilitator that drives implementations and getting out of box solutions.
This write up is about implementation of a collaborative efforts with technology to solve problems or improvement especially in the field of Engineering. Trouble shooting and engineering work such as maintenance still relies on conventional ways of problem solving such as experienced personnel and known problem-solving tools.
Artificial Intelligence (AI) and Machine can help human performing tasks and doing actual human works in the world. As a previous employee in the manufacturing industry, I would group the work experience of a worker in three difference stages, Past, Present and Future. This could help programming Machine and AI to think and work like technician.
To give an example, A newly hired maintenance technician working in a manufacturing plant. He/she is paired with a more experienced co-worker doing routine work. The newly hired learnt through the job and gained work experience from co-worker. Few years later, the newly hired becomes an experienced senior technician and to develop his skills, in performing tasks that are non-routine. The now senior technician will be handling work with increased technical difficulty, managing works that are complex and is required to lead the task. The senior technician could build his career in the company and progress up becoming an engineer in the company.
Past: Data and information into Machine.
Present: Data analytics plus human machine interface.
Future: Algorithm for Intuitive solution