LabelGPT, as a prompt-based data labelling tool, harnesses the power of multiple foundation models and proprietary pre-trained models to automate and streamline the labour-intensive data annotation process. Traditionally, data labelling has been a costly and time-consuming manual effort, requiring numerous annotators to label images and tag objects. LabelGPT, however, transforms this landscape by automatically detecting objects in data, reducing labelling time by 60-70% and potentially saving up to $100,000 for medium-scale projects.
The interview further explores the inspiration behind venturing into prompt-based labelling, model-assisted labelling, and active learning for automation. Puneet highlights the significant bottleneck of exceeding costs for organizations embarking on AI initiatives, underscoring LabelGPT’s mission to empower AI teams of all sizes, enabling multiple experiments without the burden of cost and operational complexities.
As the conversation unfolds, we learn about the industries and use cases where LabelGPT makes the most significant impact, spanning Automotive, Health Care, Agriculture, Retail, and Chatbots. The zero-shot learning-based output of LabelGPT ensures high accuracy in use cases like autonomous driving, object detection, pest detection, safety compliance, and data quality improvements.
Security and privacy concerns in data labelling are addressed, emphasizing LabelGPT’s commitment to implementing stringent security and compliance measures. The interview delves into challenges faced during LabelGPT’s development, strategies to overcome them, and success stories demonstrating remarkable labelling speed and quality improvements.
Puneet Jindal envisions a continued evolution with more versatile foundation models and open-source pre-trained models in the future of labelling automation. LabelGPT aims to navigate this landscape seamlessly, providing organizations with high-quality labelled data without compromising cost and timeline.
Finally, readers are teased with upcoming features and developments in LabelGPT, including expanding data format support beyond images. Puneet Jindal also encourages AI enthusiasts to explore the platform through Labellerr for a firsthand experience of its capabilities.
Aspiring entrepreneurs keen on making a mark in AI and automation will find valuable advice from Puneet Jindal, emphasizing the transformative potential of AI in solving significant problems, particularly in managing unstructured data—an essential element for realizing the full potential of AI systems.
Can you give us a brief overview of LabelGPT and how it differs from traditional labelling methods?
LabelGPT is a prompt-based data labelling tool. Its power comes from multiple foundation models and the combination of our in-house, per-trained models. Traditionally, data labelling is a manual process that demands many annotators to label images and manually tag the objects humans can see in an image/video.
Naturally, it makes this process very expensive and time-consuming. LabelGPT reduces that time by automatically detecting the object inside the data, saving 60-70% of time preparing labelled data and saving tons of money. This saving can go up to 100,000 USD for any medium-scale project.
What inspired you to venture into prompt-based labelling, model-assisted labelling, and active learning for automation?
Exceeding cost is a massive bottleneck for many organizations, even larger ones, to start with their AI initiatives. Even for small experiments, they need to allocate a big budget and time, and we want to remove that. We want to empower AI teams of any size to manage multiple AI experiments without worrying about cost and operational hassle. Model-assisted auto-labeling can help them in a big way.
As data scientists, we have faced similar pain while working in our previous role, which prompted us to solve this problem for others, too.
In what industries or use cases do you see LabelGPT making the most significant impact?
Automotive, Health Care, Agriculture, Retail and Chatbots are some industries that can hugely benefit from this tool as they have use cases like autonomous driving, object detection, pest detection, safety compliance and data quality improvements. LabelGPT’s zero-shot learning-based output gives very high accuracy for these use cases.
How does LabelGPT ensure the accuracy and efficiency of the labelling process, especially in complex or niche domains?
LabelGPT leverages multiple open-source foundation models and pre-trained model marketplaces to give the output. Teams can test various models to see which ones suit their use case best, as our technology handles that at the backend. However, niche and complex use cases still need fine-tuning for highly accurate results. Our enterprise solution comes with seamless integration with our data annotation tool Labellerr, where the team can leverage expert-in-the-loop service to manage niche domains with the help of active learning-based labelling to solve complex labelling tasks too,
Can you share any success stories or case studies where LabelGPT has demonstrated remarkable results in improving labelling speed and quality?
We have seen remarkable time and cost savings in street semantic segmentation projects as it saves 1000s hours of manual effort. We’re also working with a few of our customers to run some POC for other projects, on which results are expected. https://www.
Security and privacy are critical concerns in data labelling. How does LabelGPT address these concerns to ensure the confidentiality of sensitive information?
Data security is always a big concern for organizations. We also give them paramount importance as we have implemented all security and compliance measures for cloud connectivity. We partner with all major cloud providers like GCP, AWS and Azure to create secure data connections. Our labelling partners come with all certifications, and we’re also in the SOC II implementation process.
What challenges have you encountered in developing LabelGPT, and how have you overcome them?
Building the proper infrastructure and user experience is always challenging for any data platform. Processing a large volume of data is still very complex as it needs to maintain the logs and versions. We put extra effort into the UI and latency of the tool to give the users a clean and seamless labelling experience.
How do you envision the future of labelling automation, and what role do you see LabelGPT playing in that landscape?
Data labelling automation has a long way to go, as every organization has unique use cases, objects and quality requirements. More versatile foundation models will come in the future, and open-source pre-trained models will come. LabelGPT will keep improving the experience and provide convenience to the organization by navigating all those different models without any hassle, as they will get high-quality labelled data for their use case without worrying about cost and timeline.
Are there any upcoming features or developments in LabelGPT that our readers should be excited about?
Currently, LabelGPT is supporting images only. However, we’re also working on keeping the data format. Our data labelling tool, Labellerr, supports most unstructured data types, from photos and videos to speech and text. Moreover, if a design needs support, the speed to bring new unexplored data types is just a matter of days. AI team members can try it for their POCs by logging in at https://try.labellerr.com/
As a co-founder, what advice would you give aspiring entrepreneurs looking to make a mark in AI and automation?
Aspiring entrepreneurs should see AI as an enabler to build great products for any segment, enterprise or consumer space. They should watch out for big problems that are easily solvable with AI and automation.
The core insight is that Over 80% of the global data will be unstructured. To realize the AI vision, any country or enterprise needs high-quality data for their AI systems, and we are solving the most significant bottleneck. Otherwise, we all know the “Garbage In, Garbage Out” phenomenon in the current state of AI.
