Category: Content Annotation

When OpenAI introduced ChatGPT in 2022, it created a near-historical milestone in conversational AI. ChatGPT is one of the most advanced AI chatbots powered by a highly sophisticated language model. What makes ChatGPT a cut above the rest? Experts point out that the chatbot is powered by an extensive data annotation process that goes into its model training. ChatGPT could accurately interpret human language thanks to vast amounts of human-labeled text data. Annotations are crucial to a chatbot’s (like ChatGPT) ability to converse intricately and provide insightful responses. The technologies driving the ability to process, understand, and speak any human language are Natural Language Processing (NLP) and Machine Learning (ML). What is the reason for this sudden explosion in high-end technologies? Let’s explore. What is Driving the AI Revolution? Natural Language Processing is a pulsing buzzword in the tech world. The global NLP market is projected to reach an expected value of USD 91 billion by the year 2030. The market is already growing at a steady CAGR of 27% and is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, with a CAGR of 38.8%. The existing Large Language Models or LLMs are all powered by NLP and ML that are, in turn, trained with very high-quality training data. This is what determines the success of these AI applications. What’s training data? Training data are sets (input and output pairs) of examples on which Machine Learning models are trained to make accurate predictions. The ML models use the input-output pairs to learn how to map inputs to the corresponding outputs. This mapping is the project’s foundation and is the learning basis for all ML models. This concept is better explained with an example. Take the sentiment analysis task, for instance. The training data for this task comprises a set of reviews and corresponding sentiment labels like: Fabulous > positive Unacceptable > negative Functional >neutral The model is trained on this kind of data to learn how to predict the sentiment of new reviews. The concept is simple: The higher the sample quality, the more accurate the output. ChatGPT 3 is an ideal example of this concept. The chatbot was trained on 176 billion parameters, 570 GB of books, articles, websites, and other textual data taken from the Internet. Where is the ChatGPT’s Data Sourced From? Basically, ChatGPT is fed WebText datasets comprising nearly 8 million web pages taken from the internet, along with additional datasets to enhance its performance. The WebText datasets which refers to data taken from the Internet, provide easy access to information. The diverse collection covers various sources like online forums, websites, and new articles. The additional datasets comprising text sources like written works, articles, and books make the training data diverse for developing LLMs like ChatGPT. So, how was ChatGPT trained? Let’s unravel that puzzle. How ChatGPT Was Trained: A Step-by-Step Guide Data annotation is the key element used to construct LLM as advanced as ChatGPT. The main process here was adding meaningful tags to text data to enable the AI model to understand the context and meanings behind phrases and words. Using data annotation as the core element, ChatGPT was developed using these steps: Step 1: Data collection To build such an advanced Chatbot, OpenAI used a massive corpus of text data from numerous online sources. All irrelevant and duplicate information was then removed from this enormous data collection to clean it up. Step 2: Data labeling All the collected data were annotated by a skilled team of annotators who were trained to apply the labels with complete precision. The labels included: Pat-of-speech tagging Text classification Sentiment labels Named entity recognition Step 3: Training the model By using transformer architecture, the language model was trained using the annotated data. The model was trained to predict the most suitable labels for words or phrases based on the context and annotated data. Step 4: Evaluation & Fine-tuning A separate dataset evaluated ChatGPT’s ability to accurately predict labels in new, unseen texts. The evaluation results were then used to fine-tune the AI model until it achieved the desired performance level. Step 5: Deployment The trained and fine-tuned ChatGPT was deployed and made available for real-time usage allowing users to generate natural language responses to their inputs. How Data Annotation Fuelled ChatGPT’s Conversational Capabilities As a starting point, ChatGPT was trained using transformer-based language modeling. Basically, ChatGPT’s architecture follows the concept of transformer architecture. It comprises a multi-layer encoder-decoder system and self-attention capabilities.  The self-attention capabilities allow it to focus on various input aspects as it generates output. During the training phase, ChatGPT’s parameters were modified by exposing them to vast volumes of text data. The aim was to minimize the disparity between the model-generated text and the target text. Identifying patterns in the text data was necessary to create contextually appropriate and semantically sound text. The fully trained model was then deployed for several Natural Language Processing tasks like: Finding answers to questions Language Translation Text creation ChatGPT is powered by the GPT-3 model, which was trained using annotated data, which provided it with a wealth of information, including named entities, coreference chains, and  syntax trees.  This data annotation enabled ChatGPT’s model to completely understand text generation and comprehension in multiple genres and styles. ML and AI applications depend heavily on data labeling to ensure the accuracy and quality of the data used to train effective ML models. Furthermore, the text data was basically annotated manually by a team of annotators trained to label accurately and consistently. To ensure data accuracy and quality, labelers annotated the data using automated methods in some cases. How ChatGPT Eases The Work For Data Annotators ChatGPT is a boon for data annotators. This amazing AI tool helps annotators with the following tasks: Classifying sentences into various categories like intent, sentiments, and other topics. Identifying named entities in texts, such as locations, dates, organizations, and people. Extract structured information from unstructured data like product prices and names.

Bounding Boxes & Content Annotation: What’s the Connection? Technology is riding high on a wave of success. Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV) are scaling one peak after another and changing how machines work. However, technology is yet to create something potent enough to match the precision of human perception. The model prediction is only as effective as the accuracy of the data annotation, which in turn is only as effective as the algorithm training. So it all boils down to one thing: How to annotate data effectively? There are three ways to data annotation: Manual annotation, where experts manually label all data. Semi-automated annotation where machine learning models help experts. Automated annotation, where machines use bounding box object detection to identify and label the objects in the data. Choosing the right annotation depends on the use case because different annotation techniques are better suited for different use cases. When it comes to data annotation, image and video annotation form the core of CV-based AI models transforming our world. Annotation adds information to images and videos and, by doing so, provides context to training datasets for CV models. In this context, Bounding Boxes are considered one of the most popular image and video annotation tools. Why Are Bounding Boxes Important in Data Annotation? Bounding boxes are imaginary rectangles-very much like a box. Bounding boxes outline objects in an image and serve as a point of reference for the objects. These rectangles are drawn around ML images and define the X and Y coordinates of the objects of interest within each image. Using Bounding Boxes in the image and video annotation is beneficial because it: Streamlines ML algorithms search to find what they are looking for. Determining collision paths Protects indispensable computing resources Axis-aligned bounding boxes work best when vertical shapes directly face the camera. Rotating bounding boxes can be placed over multiple objects, which reduces the amount of pixels covered by the objects not targeted by the bounding boxes. Basically, without annotation, machines cannot detect the objects of desire. Hence, bounding boxes are fundamental for image annotation as they create accurate training and testing data for CV models. Multiple industries are relying on this annotation technique to make more precise datasets. The importance of bounding boxes is better explained with its use cases in these industries. Let’s explore the popular ones. Major Industry-Based Uses Of Bounding Boxes 1. Surveillance & security Bounding boxes train AI-driven security models to scrutinize and identify suspicious-looking objects caught in the camera footage. For example, they can be trained to pinpoint guns, bombs, and vehicles entering restricted areas. Suspicious objects are often hidden from the camera’s direct view. However, with advanced AI algorithms, it is possible to detect objects lying in the dark or even out of the camera frame. 2. E-commerce & retail Image annotation with bounding boxes provides more clarity and product visualization in online retail stores. By training perception models on multiple datasets with labeled images, they can learn to recognize image patterns. The perception model can apply this knowledge to new datasets to correctly identify and classify images. 3. Autonomous cars In the automotive industry, bounding box training data helps machines detect objects like: Traffic lights Pedestrians Other vehicles Number of lanes Street signs Barricades Advanced data training allows the machines to respond to instructions based on perceived data. 4. Animal husbandry Surprisingly, image and video annotation with bounding boxes make itself a valuable asset in animal husbandry in the following ways: Livestock management- To detect behavioral changes in animals in the presence of humans. Disease management- Early detection of diseases and their symptoms help take prompt action and curb the spread of disease. Livestock protection- Monitoring potential attacks from wild animals, especially at night. 5. Insurance industry Insurance regulators can use bounding box-trained CV models to identify accidents and repeated mishaps. By using bounding boxes, models can pinpoint where the mishap happened on the vehicle, such as: Broken window glasses Broken front and tail lights Dents on the body Damages to the roof Scratches on the paint With bounding box annotations, machines can accurately estimate the damage to the vehicle, and insurers can use this information to make their claims. 6. Robotics & drone imagery Thanks to various elements annotated by bounding boxes, robots, and drones can detect physical objects from a distance. For example, robot-operated assembly lines can operate more efficiently with trained AI models. Annotators can fit rotating bounding boxes to things within crowded assembly lines, enabling the robot to operate without human intervention and supervision. Likewise, in drone imagery, AI models help in the accurate detection of AC units, damaged roofs, and even animal migration. 7. Waste management Since waste management involves a wide range of objects, AI models use bounding boxes to identify different materials, especially in landfills. In the coming years, AI systems will perform much better as they will be trained with rotating bounding boxes. 8. Shipping industry Under the broad spectrum of the shipping industry, rotating bounding boxes play an inherent role in training AI models to help with: Automated fishing management. Naval warfare Vessel traffic service Cargo management Ship detection and counting In this context, AI models are trained to capture the rotational and translational properties of objects within the boxes. This technology enables precision under complex shipping conditions. Also Read: AI-Based Gaming Behavior Moderation or Human Moderation: What’s Better? 9. Agriculture Image annotation with bounding boxes has reached the realm of agriculture. With the development of “smart farming,” bounding boxes teach AI models with collected data to detect plant growth rates and seasonal diseases. Even AI-driven drones can help survey vast agricultural areas and see farmers in problematic situations. 10. Real-life situations Bounding boxes enable ML models to detect real-life situations like: Sense of space Location of objects within the space Dimensions of the objects For instance, it is possible to detect indoor objects like cabinets, benches, tables, beds, and electrical appliances arranged inside a room.