Most Data Annotation Bpos Are Missing the Point Amid the GPT LLMS Wave
Last Updated on May 22, 2023 by Editorial Team
Author(s): Puneet Jindal
Originally published on Towards AI.
The humans-in-the-loop annotation will be reduced to a minimal review effort guided by AI!
There is a lot of hype around What AI can do. Amidst the hype, there is a real incremental outcome happening which is increasing productivity.
Personally, due to ChatGPT and other LLMs, I can do the following things
- Write code quickly with GitHub co-pilot
- Take content suggestions and even rephrase my content better and faster enabling me in spending my energy on thinking instead of writing or content tactics and structuring or even trying to fix grammar.
- Prepare better responses to emails and chats according to the intended persona such as geo, language, past behavior, and even sometimes situations.
Let me share a few examples
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Automation brings economies of scale and works opposite to the economics of outsourcing to developing countries while speed makes automation even more lucrative and outsourcing can’t provide nearly the same level of quality.
However, if both humans in the loop and automation are both married such that each is packaged together in such a way to leverage each other strengths then it can become the key competitive advantage
What Humans in the loop in data annotation can do better now?
- Offer the expertise that automation still yet to learn or where it doesn't make economic sense to train AI while also considering the environmental impact
- Human workers can bring a level of nuance and context to data labeling tasks that may be difficult for AI to replicate, particularly in situations where the data is complex or ambiguous.
- Human workers can adapt more easily to changing circumstances or unexpected situations, whereas AI may struggle when faced with novel or unexpected data
What automation in data annotation can do better now?
- Annotate large types of annotations, especially classification, and content moderation as a first opinion.
- A significant percentage of data annotation manual work for NLP and computer vision and other types of AI is not going to compete with the costs offered by automation for the same level of decent quality enough to reduce the dependency on the human in the loop.
- Automation can help to standardize the annotation process, ensuring that all data is labeled consistently and reducing the potential for human error or bias. This can be particularly important in industries such as healthcare or finance, where accuracy and consistency are critical.
- Automation can be used to identify and flag potential issues or anomalies in the data, allowing human workers to focus their efforts on resolving these issues rather than spending time on routine tasks. This can help to improve the overall quality of the data and reduce the risk of errors or inconsistencies.
What tool companies should do more?
Companies focused on automation and its application should continue to invest deeper into this and increase the velocity of everyday new tech evolving and focus on innovating the User experience and enabling better consumerism of the tech and R&D possibly in the following ways:-
- Labelbox, Labellerr, CVAT, Scale AI, Appen, and others, are all leaders in the data annotation space and continue to innovate in areas such as quality control, model training, and data security.
- leveraging emerging technologies such as blockchain and federated learning to enhance data privacy and security, and exploring new applications of automation in fields such as autonomous vehicles, robotics, and smart cities.
- Partnering with other companies in the ecosystem, such as cloud providers(AWS, GCP, Azure, and on-prem) and tooling platforms, to create end-to-end solutions that can streamline the data annotation process and make it more accessible to a wider range of industries and use cases.
What Data annotation BPOs should do more?
- Investing in talent can help companies to stay ahead of the curve when it comes to emerging technologies and best practices in data annotation. By hiring skilled workers who are up-to-date on the latest developments in the field, companies can ensure that their annotation processes are optimized for efficiency and accuracy.
- Talent can also play a crucial role in identifying and addressing potential biases or errors in the annotation process. By having a diverse team of experts with different perspectives and experiences, companies can ensure that their data is labeled in a fair and consistent manner and that any issues are quickly identified and resolved.
- In addition to investing in talent, companies should also focus on creating a culture of continuous learning and improvement. By encouraging employees to stay up-to-date on the latest developments in the field, and providing opportunities for training and professional development, companies can ensure that their annotation processes are constantly evolving and improving over time.
Finally, the most important question!
What should a new-age state-of-the-art AI data preparation workflow look like?
Its already there though with our new tech LabelGPT
- Data Management — The AI team connects their NLP and computer vision data from the cloud(say S3, API, GCS, Azure blob store) and manages the datasets via data management interfaces
- Flag bad data — Because the system will already have annotated datasets and previous knowledge and models, it will suggest what data is noisy, and invalid according to the AI use case in seconds and minutes. This if provided to the large army of outsource workers would require a huge amount of effort and time and still miss the context.
- Pre-labeling — Further, it will do pre-labeling according to mentioned prompts or use cases. Till now a significant part of the offering by BPOs was this part and now it is reduced to none in the majority of the use case such as content moderation, receipt annotation, NER tags, classifying documents, people counting, surveillance, etc <TBD>.
- Guided data QA — Then a system will generate analytics and guided workflow where it really requires human assistance or scenarios where it is not confident.
- Build benchmark AI models — This smart review capability is left to such a small volume that has an in-house team with better context and data residing in-house becomes much more viable than outsourcing the work while allowing access to outside folks and still less context. In the meantime, AI teams can start building models while they explore outsourcing the pending unlabeled or difficult data.
- Outsource only if required where it matters — Now finally there possibly 5–10% of data is left where the system doesn't know where it is wrong or scenarios where it is not confident and feedback loop from customers where it is going wrong. These are the cases that require a review and could lead to the creation of a human-in-the-loop requirement.
The data annotation landscape is changing at a breakneck pace, with disruptive technologies like GPT-3 and other large language models (LLMs) making their presence felt. While the role of BPO companies in data annotation is rapidly shrinking, automation is stepping up to the plate and taking on more of the workload. According to estimates, automation can now handle up to 90% of data annotation work, leaving BPOs with a meager 10%.
Humans in the loop still play a crucial role in achieving the highest levels of accuracy and quality in data annotation. Tool companies need to focus on developing better technology that combines the strengths of both humans and automation to create a powerful hybrid approach. By leveraging the speed and efficiency of automation with the human touch, this model can deliver the best possible results.
To keep up with the rapid changes in the industry, BPOs, and data engine platforms need to collaborate and offer AI as a service. Failure to adapt to these changes can result in being left behind in the progress of the industry. Investing in talent and staying ahead of the curve by incorporating the latest technologies is the key to success.
With the right strategy and approach, companies can stay ahead in the data annotation game and achieve limitless possibilities. It’s important to address the current and evolving limitations of technology while innovating and collaborating to create a better future for data annotation.
My final thought on this!
As a leader in AI data annotation, you must be aware that a game-changer like labelgpt is not just another tool in the market.
It offers a lot more than just basic data annotation, making it an ideal choice for BPOs looking to increase their revenues by up to 10 times or retain their clients.
It makes little sense for BPOs to invest in building a tool from scratch when collaboration or strategic partnership can offer a more efficient and competitive way forward.
In my upcoming article, “Why labelGPT is not just another data annotation tool,” I will discuss the unique features and benefits that set it apart from other tools in the market.
Let’s connect over Linkedin as I write interesting and new aspects in computer vision data preparation, data ops, data pipelines, etc., and I am happy to chat on the same. Only technical deep dive!
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Published via Towards AI