AI programs are well learned, developed to perform complex computation and provide dependable results. The efficiency of an AI program depends upon the data it feeds on.
When conversation on machine learning and AI (Artificial Intelligence) programs and machine learning (ML) started a decade ago, the relevancy of data utilization came to the fore. Going a level higher than software programming, artificial intelligence with the help of machine learning, artificial neural networks, and computer vision has changed the way the world is solving complex problems and finding their solutions. In all these efforts, data has worked as the sole enabler through which distinct AI programs have been able to perform and deliver quantifiable results.
Specialized Training Data Matters
To begin with, the applicability of AI programs is diverse. Every algorithm backed with reliable training data is required to deliver optimum results or learn from the results to develop another model for prediction. The entire calculation is complex and must produce accurate solutions. When an ML algorithm makes use of the specialized training data, then it should either be useful enough to evoke measurable action from the customer; or if in case, an AI program is built for detecting scenarios, it should point out problems, or merely need to compute a solution, the training data should be structured to suit the algorithm model.
Depending upon the objective the ML process and the computation, below steps are found in every process:
Step 1: Defining program, and proposing solution (as per the objective)
Step 2: Construct data (raw data, sample collection, sampling, data splitting)
Step 3: Data optimization (clean the data, re-engineer)
Step 4: Training the model
Step 5: Use model for predictions
Now if we talk about the applicability of ML algorithms on the basis of this process, then we can say that algorithms are required to do a variety of human-like tasks. Be it for analyzing visitor identities for on-premise security and medical diagnosis based on a patient’s medical history or to recommend appropriate retail products for purchase to end customers, AI programs are built to serve diverse business purposes. Some simple AI programs can perform exceptionally well with small data sets while big computations on low quality large data sets can fail to come up with accurate results. As a reason, training data holds actual importance in the quality of output produced while an AI program will marvel only when its data requirements are on point.
Building Specialized Training Data for AI
Based on the objective of the ML model, the need for training depending upon the learning model, data labelling, ML model complexity – are crucial for analyzing the reliability of the training data. Meanwhile, accessing training data quality can be a tough task. And, data accuracy is as important as checking the data quality and size. The training data should be reliable and able enough to fulfill the objective of the ML model. The reliability of the data can be checked on parameters of how the firm is ensuring training data quality.
Taking an instance, if the ML model of a healthcare service provider needs to be trained for detecting dental problems, then a specialized data labelling firm onboards dental practitioners to train in-house work forces for data labelling. To train the workforces to label the data correctly for training, the dental practitioners often suggest actual professional methods for training such as by marking the structure of the teeth and identifying the dental problems by the dental schema. Post which, the workforces can label the data comprehensively with labelling tools and annotate the dental images data.
Similarly, Radiologists read the MRI or PET Scans, CT Scans or X-Rays and train the workforces to identify and label the unlabelled training data. Pathologists are specialized to comprehend and observe molecular or microscopic images to identify and label tumors, cancer cells in the data. A Cardiologist trains the workforce to do semantic segmentation of heart and label ventriculares, atriums or myocardium around the heart. Professionals and experts from specific medical disciplines of the field train & supervise a team of data annotators. This trained team, in turn, disseminates the guidance further to workforces. Further, quality checks are carried out to transform the training data to arrive with a final clear data set that can be used for training the model. The process ensures a high level of accuracy in the training data and enables data labeling companies to maintain high quality through QC’s.
Sometimes as per the business requirement data quality is optimized using multiple computational methods such as below –
1. The question pool comes with correct and known answers at each step which alert the labeler and helps in checking their performance.
2. Consensus is used for subjective questions where common answers are based on majority.
On the basis of such methods and more such quality ensuring methodologies, training data is prepared for the agriculture sector too. Image segmented deep learning is carried out with the help of satellite imagery, drone recordings and robotic machines, which detect or identify the condition of the produce in the agricultural fields. To take an instance, robotic machines enabled with an AI algorithm zooms over the seedling of a vegetable and predicts the time by which it will be ready for harvest. Image annotation works at the back of such algorithms which help AI programs learn simply by viewing the crop. For farming and agriculture helps in crop health monitoring, crop protection, identifying crop spoilage, fructification, and livestock management.
Self reliant technology trained by high quality training data is reducing human efforts and errors, adding more value at each step of industrial operations. This is just the beginning of what AI can do. Powered by machine learning and computer vision, it can increase business profits and spearhead major transformations to bring greater efficiency and prevent issues before they take place.
While most of us believe that it is the algorithm that helps in defining the solution, however, it is the potential of specialized training data that makes the prediction model deliver concise results.
To add, as businesses start relying more on Artificial Intelligence, and Machine Learning procedures to provide their services, the market for data labelling will flourish. It is expected that the market will grow three-fold by 2023, paving the way for more solutions on the horizon to make use of. For industry 4.0 and the subsequent leap towards industry 5.0, AI-enabled self-reliant machines are needed as they are capable of doing more complex tasks, in comparison with what application programming can offer to the global enterprises.