Combined Expertise in AI – An alliance of Data Scientists and Subject Matter Experts
Subscribe for Updates
Artificial Intelligence (AI) is spearheading progress in various sectors of business development. In recent years, AI has shown remarkable breakthroughs in the field of medical science increasing accuracy in medical processes like diagnosing a condition, predicting its future pattern and, suggesting a treatment for the same. Similarly, even the industrial sector has been exploring the use of AI to ease hectic procedures like product manufacturing, assembly line management, and even the latest inventions like autonomous transport facilities.
While it is really exciting to put an AI strategy in place, keeping in mind a specific process that can be automated, we also have to understand that developing AI software is a tedious task. The software has to be taught how humans are taught and the perfection of the output depends on the accuracy of the raw data used to teach the ML model.
Data scientist teams are more than capable of single-handedly developing ML models for relatively generalized tasks like detecting metals from waste or detecting movement in a surveillance camera. However, when developing AI software for more complex processes such as medical procedures like the diagnosis of a medical condition by studying the various test reports of a patient or industrial processes like autonomous vehicles and advanced robotics, data scientists need more data inputs from subject matter experts(SME). SMEs are just people who have extensive experience in the specific field and have come across a large number of real-time scenarios during their practice. This combined expertise of data scientists and subject matter experts is a key element for the development of a strong and highly accurate AI software.
Here are some of the key benefits of developing AI with a combined team of data scientists and subject matter experts –
Evaluating the value of sourced data
In a complex process or a knowledge-based decision-making process, the data scientists need subject matter experts to verify the raw data source and evaluate the value of this data. Higher valued data yield more accurate results in the AI Model.
Accurate annotations for accurate model building
Subject matter experts also help data scientists in annotating (labeling) source data accurately, leading to increased accuracy of the model. For eg, a well-experienced dentist annotating radiographs of patients for the training data set. The result would be far better than annotations by a data scientist, who has scarce knowledge about the radiographs.
Understanding of the process
Subject matter experts help data scientists understand the logical reasoning behind a process making it easier for them to design the AI model with specific output in mind. Unless the data scientist understands the reason behind a specific process, the output will be inaccurate more often than not.
Various scenarios from experience
A data scientist won’t be well acquainted with the various scenarios which the AI model will face in the real world. A subject matter expert would suggest these possible scenarios based on their personal experiences and also the human steps to tackle it. This information is gold for the data scientist to develop a highly accurate AI model.
Breaking down the process into smaller blocks
SMEs can break down a complex process into many smaller and simpler modules. The data scientist can then create AI algorithms for these quicker and easier processes and then combine them to form a complete and complex process. This module-wise programming helps in avoiding errors in a complicated process. These small modules can also be placed in a different process where the same functionality is required. This helps in saving huge development costs and time.
Top-level Quality Assurance
Subject matter experts help in Quality Assurance of the AI model. They are very crucial in the testing phase of an AI model. They can determine if the output of the algorithm matches the desired result or if the data scientists need to tweak the algorithm to get more accurate results. Any quality drawbacks in the algorithm will be noted by an SME, much faster than a layman.
Data scientists know how to develop an AI software, i.e. the technical side, while subject matter experts help in perfecting the practical functionality of the model in the real world. The combination of these two elements gives the most desired output, ie. an AI algorithm with the ability to tackle maximum different scenarios.
Automaton AI hosts a dedicated team of data scientists with extensive knowledge in AI algorithm development. While working on strong AI projects such as AI for Dentistry, AI for autonomous vehicles, AI for industrial assembly lines, etc. they collaborate with subject matter experts in the respective fields and these 2 teams work hand in hand to churn out the most beneficial and efficient AI model.