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Crowdsourcing in general is the collective gathering of various types of information from the public which can be used to complete a task. So, in this tutorial, we will look at the ways in which crowdsourcing benefits can be utilized for Machine learning tasks. We will also talk about some of the common crowdsourcing platforms for technical solutions as well as the benefits of the concept itself. So, let’s begin!
Crowdsourcing
Crowdsourcing is the idea of getting work done by outsourcing it to a crowd of workers, usually online. The best example of it that exists today is Wikipedia. Instead of creating their own content by hiring writers and editors, Wikipedia gave crowds the ability to create the information on their own and the result of this is that Wikipedia is the most comprehensive encyclopedia that the world has ever seen.
Machine Learning is a technique that allows computers to acquire skills by looking at and following several examples instead of sets of rules. Machine learning makes it easier for you to do everyday things like searching for photos that you love, speaking to someone in any language, and getting to wherever you want to go in the world. The first question is, how do machines learn? In machine learning, computers find, identify and learn about common patterns through several sets of data, known as training data e.g. showing a computer many pictures of cats, teaches it to recognize one in any picture. The more the variety of cat images that we show it, the better it gets at recognition.
The second question is, how does crowdsourcing fit into all of this? The thing is, people understand many domains especially the real world, more deeply than the machine learning systems nowadays. Basically, what crowdsourcing does it that helps to create and verify accurate examples for computers to learn which can in turn enable features that can benefit everyone. It is the use of human knowledge coupled with a machine’s computing power to learn interesting patterns. For example, when you verify image labels, you help various photo apps to get better at classifying photos and identifying the objects within them. When you label the sentiments of sentences, you allow apps to classify reviews as positive or negative in your language. Likewise, when you verify different translations, you help translation apps to make more accurate translations in your language. Your responses are combined with thousands of other responses from people like you to determine the best response which is known as ground truth. The ground truth is then given to the machine learning models that find patterns to learn specific skills such as how to identify cats in a photo or to translate something from one language to another. What a machine learns is limited by the data that it is given. Therefore, the more the data provided and that too from different parts of the world, the better the machine will get at recognition.
Crowdsourcing can be used by both large and small companies to their advantage because of the benefits that this concept provides. It has proven to be especially handy in the Machine learning domain. Some common applications of Machine learning that make use of crowdsourcing are:
Data generation
This is probably the most common application of crowdsourcing within the machine learning community. In this crowd-workers are provided with unlabeled data instances e.g. websites and are asked to supply labels e.g. a binary label indicating whether the website contains inappropriate content or not. Crowdsourcing is also used to generate more complex and free-form labels, such as transcriptions, translations of language, image annotations, etc.
Evaluation and debugging of models
Crowdsourcing is also used in the evaluation and debugging models e.g. unsupervised learning models in which the ground truth is not so obvious or clear. One such example is that of topic models in which you decide upon the topic of an article on the basis of the words used in the article and their frequency e.g. if an article mostly contains repeated words like cheese, bread, milk, etc. then it is most likely an article related to food.
Hybrid intelligent systems
As the name suggests, these systems are a hybrid of machine learning and human intelligence. These systems are able to achieve more than state-of-the-art machine learning or AI systems alone because they can make use of people’s common-sense knowledge, life experience, beliefs, reasoning skills. One such example would be a system that forecasts an event.
Crowdsourcing Platforms
While crowdsourcing can be done in a number of different ways, most companies and businesses turn to crowdsourcing platforms to get workers. Finding the best crowdsourcing platform depends on the type of tasks or work that the company wants to be completed. One of the most renowned crowdsourcing platforms for Machine learning-related tasks can obviously be Amazon Mechanical Turk (MTurk). MTurk is best for simple, small tasks with minimal management efforts. However, typically, MTurk platform provides limited or almost zero control over the data collection environments and leads to poor data qualities.
Crowdsourcing Benefits
Crowdsourcing has a number of benefits that companies and businesses can use to their advantage. When it comes to machine learning, crowdsourcing has led to an:
- Improvement in sentiment analysis
By using a classifier, a huge number of unlabeled items can be classified to provide robust statistics about sentiment trends, and statistics can be generated after the annotation process ends. The extent to which this can be done relies on the amount of concept drift that occurs over a period of time in the specific domain of interest.
- Improvement in Natural Language Processing
Customer reviews hold great importance in assessing market feedback. However, accurately analyzing these reviews is challenging because of the hurdles in natural language processing. Crowdsourcing can improve the accuracy of natural language processing techniques. Firstly, multiple machine learning algorithms collectively pre-process review classification. Then, the reviews are selected on which all machine learning algorithms cannot agree and assigned to humans to process. Finally, results from machine learning and crowdsourcing are aggregated to generate the final analysis result.
- Improvement in the quality of data
Due to crowdsourcing, labeled data is now available in abundance. Previously, due to traditional barriers to data collection, researchers tended to reuse existing data rather than collect and annotate their own. Crowdsourcing has changed the landscape for the quantity, quality, and type of labeled data available for training data-driven machine learning systems.
Crowdsourcing is not only hard in terms of gathering the crowd-workers but also difficult to control the quality of the collected data. This is especially the case if your company is a small- or medium-sized company; having enough human resources is always a great challenge for companies in such sizes. Therefore, it is often more efficient to find another service that does laborious works for you. We could be your perfect solution!
Here at DATUMO, we provide an intelligent and quality-assured crowdsourcing platform to diverse users located globally. Moreover, our in-house managers double-check the quality of the collected or processed data. So if you need data, check us out!
To sum it all up, we started off by getting an introduction to what crowdsourcing is and how it can be used to accomplish several tasks related to machine learning particularly. We also talked about some of the common platforms for it and the advantages that it has in the world of machine learning. Overall, crowdsourcing is an extremely useful concept and reaps many benefits for different companies and businesses.
