Photo by Gertrūda Valasevičiūtė on Unsplash

Any artificial intelligence application starts with data. As data is the basis of AI's training, it's important to have it precise and accurate. The more structured and detailed data we present to an AI tool in the input, the more error-free outputs we will get. 

For better performance of building blocks of AI, we prepare huge amounts of raw data that need to go through an elaborated transformation process. What challenges do we face by working with the raw data? What is the whole process of making the data suitable for further AI training?

Let's take a look at the detailed process an AI tool goes through before performing the pre-defined tasks.

The Nature and Challenges of Raw Data

In the context of artificial intelligence, raw data means all gathered and unprocessed information that you use as an input for an AI model. Whether you work with computer vision or natural language processing models, you will start with gathering audio, text, or visual raw data. It can exist in various formats and in different types. 

As an example, take a computer vision AI model designed for disease diagnostic assistance. Before creating the building blocks of generative AI, you'll need to process raw data from medical imaging equipment. These can be images from X-rays, computer tomography scans, and magnetic resonance imaging. Providing a detailed view of different body parts, these images give us valuable information for further AI training.

Since raw data comes from different sources, it may have the following challenges:

• Inconsistency. You may have variations in how data is initially recorded and then collected.
• Noise and errors. Due to inaccuracies, errors in sensors, you may deal with incorrect values, measurement errors, or recording mistakes.
• Missing values. During recording, some fields may lack data, which will result in incomplete records, later also impacting AI building blocks.
• Volume and scale. With large volumes of data, the training of AI building blocks can become overwhelming, causing challenges with storage and preprocessing.
• Variability. When you collect data from various sources, you may lack uniformity across sources (e.g., terminology, abbreviations). To make an AI model perform correctly, you may need to standardize the data first. 

With such a big amount of challenges, we're still in dire need of human assistance in processing all raw data. By collecting, standardizing, and labeling the raw data, we have a huge influence on the AI model's further performance.

The Process of Raw Data Transformation

Once you collect the data from various sources, and it constitutes a sufficient base for the following training, you will need to standardize and annotate it. The whole process of data transformation for the building blocks of AI will follow these steps:

1. Cleansing. At this stage, you remove data that is not relevant and doesn't contribute to the AI task. You also fill in the missing gaps, using various algorithms that predict missing values. This stage also includes removing duplicates and correcting typographical errors.
2. Standardization. The process starts with feature distribution before standardizing and unifying everything across all datasets. You may need to apply mean and standard deviation before applying the same approach to all features (e.g., words, abbreviations, terminology, etc.). 
3. Data annotation. This step is one of the most critical ones, which contributes to the AI model's performance. If you don't know how to label a dataset for a machine learning project, you can address an outsourcing vendor. You can do the annotation with the help of automatic tools or with the help of human annotators, which is more preferable and gives more accurate results. Data labeling allows machine learning algorithms to later identify categories, groups of words or people, and better understand the data. 
4. Quality assurance. After the annotation, data annotators undertake the last step to ensure the annotation is correct, and the accuracy corresponds to initial guidelines. At this last step, you ensure the accuracy, integrity, and relevancy of your data. These steps are critical for the building blocks AI model will later use for its performance. 

The Role of Human Intervention in Raw Data Transformation

As we glance at the whole process of raw data transformation, we see that it consists of numerous steps. This complex journey is not possible without human intervention. On the stage of data annotation, data curation, and data cleansing, a human is still the main gatekeeper who sets the standards and trains the model based on real-world scenarios.

Even if some parts of data transformation can be automated, there are also ethical sides of the model performance. It's solely the role of a human to decide on the ethical implication of certain data usage for AI training. We also still have the power and responsibility to protect data privacy and individual rights. 

Consequences of Poor Raw Data Usage

All the steps of data preparation mentioned above are critical in making AI models work accurately, without biases. If you don't cleanse and annotate raw datasets before starting the AI model training, you will not get the desired results and the tasks accomplished by AI will not perform as expected. Here are the main consequences of poor data management:

• Reduced model accuracy. AI model predictions and outputs will contain errors, noise, and incorrect responses. This will compromise the effectiveness of the model. 
• Biased outputs. If the raw data contains information on a limited group of people or categories, it can lead to biases and discriminatory results.
• Loss of trust. An AI model built on poor data will compromise its quality, leading to the loss of trust from clients, competitors, and stakeholders. 
• Legal implications. If no regulation is set on the stage of the data collection, the model can touch on data privacy aspects and affect individual's rights. 
• Resource wastage. Building a new AI model requires resources, both in the form of time and money. Deploying a model of poor quality will make it ineffective, leading to significant wastes.

Concluding the Transformation Journey

Photo by Bernard Hermant on Unsplash

Transforming raw datasets and preparing them for further AI model training is a meticulous and complex process. However, these steps are critical in ensuring that the final model works accurately and executes the defined tasks. 

Such important steps as data cleansing, data annotation, data preprocessing and validation are obligatory before starting the model training. By accomplishing all the stages, you will ensure that your model performs the tasks perfectly and contributes to the innovation in solving complex problems across industries.