5 Ways to Apply AI to Small Data Sets

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Artificial intelligence and data science work together for better data collection, categorization, analysis, and interpretation. However, we only ever hear of using AI to understand big data sets. This is because small data sets are usually easily understood by people, and applying AI to analyze and interpret them isn't necessary.

These days, many businesses and manufacturers integrate AI into the production line, slowly creating data scarcity. And unlike big companies, many setups cannot collect massive training sets due to risk, time, and budget limitations. This results in the neglect or incorrect application of AI solutions to small data sets.

As most companies don't know how to benefit from AI application on small data sets correctly, they blindly apply it to make future predictions based on previous files. Unfortunately, this results in wrong and risky decisions.

So, it is essential to learn the correct ways to apply AI to small data sets and avoid any misinterpretation.

The 5 Correct Ways of AI Application on Small Data Sets

 
It is better to use AI algorithms on small data sets for results free of human errors and false results when applied correctly. You also save time and resources usually spent on manually interpreting small data. 

Here are some methods to apply AI to small data sets:

1. Few-Shot Learning

 
The few-shot learning model introduces a small amount of training data to AI as a reference for new dataset interpretation. It is a commonly used approach in computer vision because it doesn't require many examples for identification.

For example, financial analysis systems do not require an extensive inventory to be effective. So, instead of overloading the AI system with a lot of information, you input a profit and loss statement template according to the system's capacity. 

Unlike other AI systems, if you enter more information into this template, it will result in false results.

When you upload sample data in the AI system, it learns the pattern from the training data set for future small data set interpretation. The appealing point of the few-shot learning model is that you don't need an extensive training data set to train the AI, making it operational at low cost and effort.

2. Knowledge Graphs

 
The knowledge graphs model creates secondary data sets by filtering through a big original data set. It is used for storing interlinked descriptions and characteristics of events, objects, real situations, and theoretical or abstract concepts. 

In addition to working as data storage, this model simultaneously encodes semantics underlying the specific data set.

The primary function of the knowledge graphs model is the organization and structuring of important points from the data set to integrate information collected from various sources. A knowledge graph is labeled to associate specific meanings. There are two main components in a graph - nodes and edges. Nodes are two or more items, and edges represent the connection and relation between them.

You can use knowledge graphs to store information, integrate data, and manipulate data through multiple algorithms to highlight new information. Moreover, they are handy for organizing small data sets to make them highly explainable and reusable.

3. Transfer Learning

 
Companies avoid applying AI on small data sets because they are uncertain about the results. The same methods that produce effective results for big data sets backfire and create false results. However, the transfer learning method makes similar and reliable results despite the size of the data set. 

Transfer learning uses one AI model as a starting point but obtains the results with a new AI model. In short, it is the process of transferring knowledge from one model to another.

This model is primarily used in the computer vision field and naturally processed languages. The reason is that these tasks require a lot of data and computing power. So, using transfer learning cuts down the extra time and effort. 

The new data set must be similar to the original training data set to apply the transfer learning model on small data. During application, remove the end of the neural network and add a fully connected layer similar to new data set classes. After this, randomize the weights of fully connected layers while freezing the previous network's weights. Now, update and train the AI network according to the new fully connected and operational layer.

4. Self-Supervised Learning

 
Self-supervised learning or SSL model gathers supervisory signals from the available or training data set. Then, it uses already available data to predict the unobserved or hidden data.

SSL model is mainly used for performing regression analysis and classification tasks. However, it is also helpful for labeling unlabeled data in computer vision, video processing, and robot control fields. This model has rapidly solved the data labeling challenge as it independently builds and supervises the complete process. This way, companies save the additional cost and time spent creating and applying different AI models.

Using the SSL model is highly adaptable as it creates reliable results despite the data set size, proving the model's scalability. SSL is also great for improving AI capabilities long term as it supports up-gradation. Moreover, it eliminates the need for sample cases as the AI system evolves independently.

5. Synthetic Data

 
It is artificially generated data created by an AI algorithm that has been trained on a real data set. As the name suggests, it is artificially created and is not based on actual events. Synthetic data's result prediction prowess matches original data predictions. It can replace the initial data predictions because it does not use disguising and modification.

Synthetic data is ideal to use when there are gaps in the available data set, and it is impossible to fill them with the accumulated data. Moreover, it is inexpensive compared to other AI learning and testing models and does not compromise customer privacy. As a result, synthetic data is quickly taking over multiple fields, and by the end of 2024, 60% of AI analytic projects will be synthetically generated. 

Synthetic data is gaining more footing because companies can create it to meet specific conditions that are not available in the existing data. So, if companies cannot access a data set due to privacy limitations or a product is not available for testing, they can still obtain reliable results using AI algorithms to create synthetic data. 

Wrapping Up

 
AI is quickly evolving and taking over to simplify every complex task. However, most people do not know that they can apply AI algorithms. For example, it is good for organizing and analyzing big data while also being quite effective on smaller data sets. But to achieve the correct result, you must utilize accurate AI methods and models. Use the AI models listed in this article as they are suitable for creating correct results on small data sets.

 
 
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.