Why Accuracy and Efficiency Matter?

Machine learning (ML) models require enormous amounts of high-quality annotated data for training. Getting the data labeled quickly and accurately is not easy. And if you are thinking of doing it yourself (in-house), well, manually labeling is time-consuming and labor-intensive.

Since the data labeling is considered a foundation step for a successful AI model. Businesses typically choose to outsource the data labeling process. The reason is twofold. They are:

• Quality: It means having high-quality training data that will save you time, as low-quality datasets prolong the model development process and make production costly.
• Quantity: It means gathering and labeling as much data as possible to train the model. Wading through a vast amount of unstructured data to get accurately labeled data requires the utmost patience.

Because data scientists need to focus on the quality of data alongside quantity, they often miss one factor over another. Additionally, the data labeling process requires specialization. So, a data annotation company, like Cogito Tech, does this task for businesses, model developers, data scientists, or any other AI project requirements for training data.

Understanding Labels: How does data labeling work and why is it important?

In the pre-processing stage, when training data is annotated, the tagged or labeled data is referred to as ground truth. This is considered a foundational step for AI models to learn effectively.

Accurately labeled data gives precise model responses or predictions, but poorly labeled data gives inaccurate or biased outputs, adversely impacting business operations and decision-making.

Poorly labeled data contains inaccuracies, inconsistencies, or errors in the labeling process. There are several ways data can be poorly labeled:

• Incorrect Labels such as human annotation error, misclassification, or data corruption. Labelers sometimes make mistakes due to fatigue, lack of domain expertise, or oversight, leading to incorrect labeling.
• Incomplete Labeling is a challenging case that typically results in poor prediction of the AI model. It appears when some aspects of the document are described while others are ignored.
• Inconsistent Labeling across various data points is also an example of poor-quality training data. As in, if two identical images of a hen are labeled differently (e.g., one labeled as “hen” and another as “chicken”).

Such a case of inconsistent labeling is subjective. It happens when different annotators apply different standards. In sentiment analysis, one annotator might label a review as “neutral” and another as “positive” for the same content.

• Ambiguous Labels mean not denoting a text, image, etc., which might confuse the model. For instance, a red and round fruit might be labeled only as “red,” considering only seasonal attributes, not its shape.
• Non-standardized labeling also leads to poor model performance. For instance, when different terms are used in similar connotations, such as ‘car’ and ‘automobile’, in the same category.
• Another type is non-representative labels, where outdated information misleads the model because it does not reflect current trends. This may happen in the electronics product category, where new smartphone models are announced every year. If an outdated phone model is not labeled as an old product category, it will lead to non-representation.

Without labels, the model would have no reference point for the correct outputs. Data labeling turns raw data into structured input that models can process, which is why it is a foundation in supervised machine learning workflows.

In machine learning, especially supervised learning, models learn from examples. It means assigning meaningful tags or labels to the raw data, which allows models to “understand” the relationship between inputs (features) and outputs (labels).

Keep reading along to learn about what is supervised learning in the next section.

Supervised Learning vs Unsupervised Learning

Throughout the data labeling process, machine learning practitioners strive for both quality and quantity. A larger quantity of training data creates more useful deep-learning models. In this regard, the training dataset is dependent on the kind of machine-learning algorithms.

The machine learning algorithms can be broadly classified into two:

• Supervised learning: The most popular machine learning algorithm is supervised learning, which requires data and associated annotated labels in model training. It consists of common tasks such as picture segmentation and classification.
  Usually, the algorithm’s testing phase uses annotated data with hidden labels to assess the accuracy of machine learning models.
• Unsupervised learning: Unannotated input data is used in unsupervised learning, and the model trains without being aware of any labels the input data may have. Autoencoders with identical outputs to inputs are standard unsupervised training techniques. Clustering algorithms that divide the data into clusters are another type of unsupervised learning technique.

The table below indicates the fundamental differences between supervised and unsupervised learning.

What are the different types of data labeling tasks?

Different types of AI systems work with specific data types and require unique labeling techniques to fit their purpose. Here’s a breakdown of data labeling tasks that you must look at in your annotation partner:

Data Labeling for Computer Vision (Image & Video)

In computer vision, the goal is to help models recognize objects, people, actions, or scenes in images or videos. It includes:

• Bounding Boxes: Drawing rectangular boxes around objects to identify their locations.
• Segmentation: Dividing an image into parts to classify each pixel, which can be semantic (entire regions) or instance-based (specific objects).
• Landmark Annotation: Marking key points in images, like facial features, for face recognition.
• Object Tracking: Continuously labeling objects throughout video frames.

Data Labeling for Natural Language Processing (NLP)

NLP focuses on understanding and generating human language in text or speech with:

• Entity Annotation: Identifying named entities in text, like people, locations, and organizations.
• Sentiment Annotation: Tagging the emotional tone in text, whether it’s positive, neutral, or negative.
• Text Categorization: Labeling text by topic or intent, such as customer feedback or support requests.
• Part-of-Speech Tagging: Grammatical parts in sentences, like nouns, verbs, or adjectives, and other parts of speech.

Data Labeling for Audio Processing (Speech Recognition)

In audio data, labeling helps models recognize spoken language and other sound patterns. It includes:

• Speech Transcription: Converting spoken language into written text.
• Sound Event Labeling: Identifying and labeling specific sounds, like sirens, laughter, or animal sounds.
• Phoneme Labeling: Tagging individual sounds within words for finer linguistic analysis.

Automating Data Labeling Tasks Using Generative AI

The data labeling process is human-intensive work because raw data are tagged or labeled in bounding boxes and segmentation masks. However, this process of manually curating datasets is time-consuming. So, in some cases, computer-assisted help or AI tools are used where labels are predetermined under domain experts (typically a machine learning engineer). They are chosen to give machine learning model-specific information about what is there to label in the data. The labels can range from identifying someone’s face in a picture to identifying the eyes, nose, lips, and other features of a human face across human life stages (child, adult, old age).

For enterprise-grade training data needs, Gen AI models meet large synthetic (yet realistic) datasets to address the lack of data problem. By exposing the ML models to various annotated data, say for social media platforms, the company can pre-defined classification schemas to filter out negative content and create relevant and semantically appropriate responses.

Pre-labeled Data to Assist Human Annotators

In this, pre-labeled data from Generative AI is used to keep pace with the massive annotation demands of the future. This approach supports human annotators in speeding up the data labeling process. The combination of HITL with help from AI-enabled tools results in reduced effort and faster turnaround times.

Importance of HITL

The phrase “Human-In-The-Loop” (HITL) describes human supervision and verification of the AI model’s output.

Two primary methods exist for people to join the machine learning loop:

• Training data labeling: Human annotators must label training data input into supervised or semi-supervised machine learning models.
• Model training: In fine-tuning, model training is done under human supervision, verifying the model’s performance and predictions. Data scientists too train the model by monitoring things like the loss function and predictions.

An annotation partner smoothens the data labeling process via AI-enabled tools and an experts-in-the-loop approach so that ML engineers can focus on other critical aspects of model performance, such as its overall accuracy and algorithm.

How does Cogito Tech support data labeling?

Data labeling projects begin by identifying and instructing human annotators to perform labeling tasks. Our team of annotators gets trained on each annotation project guidelines, as every use case, team, and organization will have different requirements.

In the specific case of images and videos, our annotators are provided guidelines on how to label the data. They start by labeling images, text, or videos using tools (V7, Encord, among others).

Our annotators familiarize themselves with annotation tools to label data in smaller batches instead of working on one large dataset to train the model. Our domain experts, project managers, and specialists guide them through technical details. This means utilizing the HITL approach to have more supervision and feedback on the project.

Cogito Tech leverages two-way collaboration between human labelers and AI-enabled tools to ensure that the data labeling process is efficient and accurate.

In addition to enabling the iterative approach to the data labeling process, Cogito Tech includes additional measures that specifically help optimize your data labeling projects.

1. Speeding Up Labeling Processes

With pre-labeled data, we automate repetitive and labor-intensive labeling tasks. This is especially relevant for businesses requiring large training data in less time. We have moved past the traditional method of training model where one large training dataset is no longer effective. Our approach is to be more agile all while carefully curating datasets to accelerate the data labeling process and training the model using AI tools.

2. Cost-effectiveness

Cogito can significantly reduce the costs associated with training data requirements. We tailor to emerging and existing industries with annotation services to improve efficiency, be it for updating old training datasets (e.g., self-driving cars, social media monitoring) or labeling the latest incoming data.

3. Improving Labeling Consistency

We provide consistent labels without the subjectivity that human annotators may fail to do. For example, in sentiment analysis, we employ domain experts and also AI tools for both qualitative and quantitative consistency.

In tasks like medical imaging, where the data is complex and requires board-certified professionals, AI-enabled tools assist in the initial labeling stages by identifying key features or patterns, reducing the load on human experts. For example, AI tools can highlight regions of interest in an MRI scan for doctors (our domain experts) to review.

4. Security and Regulatory Compliances

You need not worry about quality control measures in training data because Cogito takes care of it. We have numerous certifications and follow compliances to meet ethical, privacy, security, etc., considerations of data. Our services include keeping data privacy in check and achieving consensus between what is being labeled and the gold-standard benchmarks.

5. Quality Control and Error Detection

Quality control and error detection are automated processes that operate continuously throughout our training data development and improvement processes. Our team reviews labeled datasets and flags potential labeling errors or inconsistencies by comparing new labels to existing patterns.

Final thoughts

Data labeling is a key data preprocessing stage for machine learning and artificial intelligence. It is the need of the hour because ML models have increased in scale with millions of parameters put in algorithms. And since it’s becoming complex, data labeling and annotation companies like Cogito Tech exist. We put more emphasis on the role of rigorous quality control in data annotation processes.

Compromising on training data with poorly labeled data impacts model learning capabilities. So, when looking for the right annotation provider for your AI project, it’s important to ensure that the training data has enough labels and is supported by annotation tools without sacrificing loading times. Well, Cogito Tech domain experts get such nuances.

Schedule a call to know Cogito’s data labeling process and your AI model capabilities for both simple and complex use cases with the right training data.