Digital Themes

Data Efficient Learning

Data efficient learning is a type of machine learning that can understand complex domains without the need for large amounts of data. Traditional machine learning algorithms generally rely on big data in order to reach logical conclusions. By requiring large amounts of data, they are able to spot trends and commonalities that exist. In comparison, data efficient learning utilizes smaller data sets, with the aim of learning more quickly, while still maintaining the same performance level, when compared to other machine learning methods. This becomes especially important for domains with limited amounts of data, such as personalized healthcare and sentiment analysis.

Data efficient machine learning often utilizes the reinforcement learning method. This means that these algorithms use a reward and punishment system as part of their programming. Rather than having to be programmed to complete a task, reinforcement algorithms will work through the problem on its own. The program knows it wants to reach the “reward” state, such as correctly identifying an emotion, rather than the “punishment” state, failing to correctly identify one. As it works through the existing problems, it will learn how to improve the analysis as it goes along. Reinforcement learning is similar to the ways the children learn how to identify objects, such as recognizing that a firetruck is a firetruck because it is a large, red vehicle.

Reinforcement learning can be paired with a neural network Gaussian process to improve outcomes. Gaussian processes are used to assign probabilities to events and outcomes. Neural network Gaussian processes combine deep learning and artificial neural networks to not only make predictions, but also to provide insight into how accurate their conclusions likely are. The combination of machine learning and Gaussian processes allows the two to effectively analyze small amounts of information in order to reach accurate results.

By programming machine learning algorithms to successfully analyze small data sets, businesses and organizations can ensure that they have accurate results, even with small amounts of information. Whereas traditional machine learning would be run on big data, organizations can now process information with just data from a few select clients or participants. This would allow for customized results, such as in healthcare, without the need to process data for a large number of patients.

Data efficient learning can help businesses and organizations in many ways, including:

  • Quickly processing data: Rather than having to spend a lot of time analyzing large amounts of data, data efficient processes can quickly assess small amounts of data and return relevant results and predictions.

  • No need for big data: In traditional machine learning methods, a large amount of data is required before accurate predictions can be made. This could mean that data just sits in storage until enough has been gathered to use effectively. In comparison, efficient learning can start analyzing data and returning predictions much more quickly.

  • Protect data privacy: Because only a small amount of data is required for these processes, there is no need to store large amounts of data and unnecessarily risk a data breach. Furthermore, there is less need for anonymized data, as less data is necessary. This further reduces the risk of private information being compromised as part of the analysis process.  
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