Today all manual tasks are being automated, thanks to AI. AI learning algorithms help computers play chess, write poetry, perform surgeries and get smarter each time. One of the main features of this revolutionary AI era is the unsung heroes who perform AI algorithm typing and data entry. While AI algorithms get all the glory for its role in the various fields of medicine, racing, business, not enough attention is paid to the data that runs the algorithms and people who enter such hard data. AI would be useless without data and people who perform data entry.
The data scientists and other Machine learning enthusiasts who perform data analysis and entry for AI utilize three main types of data entry and machine learning techniques. The most common data entry techniques they utilize are; linear regression, logistic regression, decision trees, support vector machines, naïve Bayes, KNN (k-nearest neighbors), K-means, random forest, dimensionally reduction algorithms, gradient boosting techniques, among many others. Such data entry techniques are used along with complex data entry techniques of python and R-codes, which are coding techniques that transcribe data into an electronic medium such as a computer. The python data entry techniques are the most commonly used. It involves writing and testing codes that feed in data. Their prominent role is to feed in data that will be utilized to execute the AI algorithm.
Data scientists usually take months or even years entering and manipulating various forms of data so that they can fit into an efficient AI algorithm. The first step they usually take is collecting and cleaning data. During this step, collected data is cleansed and validated to ensure that it is in good shape, ready for entry and analysis. This step takes a lot of time but is the most critical to making a difference between good and bad results in the AI program. The cleansed data is stored in silos or voluminous databases.
The second step usually entails testing the data in the real world; this is usually necessary for the data scientists to determine whether the AI has a problem before they are too far along in the cumbersome entry process. The scientists divide data into two parts; one part is sent for testing and the other for feeding the algorithm. Another crucial step is setting up internal data auditors tasked with data management, including testing and storing data to ensure data is not lost. Only the quality data is guaranteed. Moreover, the data scientists have a role in ensuring diversity by ensuring they focus on one type of data source and looking for good enough sources to enrich the data. Machine learning thrives on vast data entry sets, making data entry the most crucial stage in AI algorithm generation. The accuracy and efficiency of AI are entirely dependent on the quality of collected data and quantity; hence data scientists and other personnel who feed in data to AI should be given more credit.