Machine Learning algorithms learn from data. They find relationships, develop understanding, make decisions, and evaluate their confidence from the training data they're given, and also, the better the training data is, the higher the model performs. The standard and quantity of your machine learning training data have the maximum amount to do with the success of your data project as the algorithms themselves. Firstly, it's important to possess a standard understanding of what we mean by the term dataset. The definition of a dataset is that it's both rows and columns, with each row containing one observation.
And for in-house teams, labeling data can be the proverbial bottleneck, limiting a company's ability to quickly train and validate machine learning models. By its very definition, artificial intelligence refers to computer systems that can learn, reason, and act for themselves, but where does this intelligence come from? For decades, the collaborative intelligence of humans and machines has produced some of the world's leading technologies. And while there's nothing glamorous about the data being used to train today's AI applications, the role of data annotation in AI is nonetheless fascinating. Imagine reviewing hours of video footage – sorting through thousands of driving scenes, to label all of the vehicles that come into frame, and you've got data annotation.
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. Today, examples of machine learning are all around us. Digital assistants search the web and play music in response to our voice commands.
Artificial Intelligence technology is completely dependent on the data sets that are provided to train its underlying machine learning (ML) model. Machine learning models are built by developers based on their collected and annotated training data sets. These training data get used to training the ML model to make predictions about the world. The better the annotated data, the better the predictions. Problems arise when that data is wrong or distorted.
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning where feature engineering is the bottleneck, deep learning techniques automatically generate features, but instead require large amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.