How Can You Benefit From Automated Data Annotation?

Data annotation is an essential process in machine learning. An easy way to understand this is to think of how a young child learns about the name of things or objects they can see. Show a child a photo of a cat and then repeat the process a few times and then the child will say ‘cat’ when they see a new photo of a different cat.

The child has learned what is essential for the image to be named a ‘cat.’ We go through a similar process with machine learning where an artificial intelligence (AI) system has to be trained using text and images, but with some guidance. If we were training the AI system to recognize the distinguishing characteristics of a cat then it would take more images than a child requires, but eventually the AI would be able to see a new image of a cat it has never seen before and it will say ‘this is a cat.’

But this training takes time. It’s expensive and it usually requires people to verify what the AI model is being trained on. In the example we have already explored, a human will select cat images and will ensure that the AI is only ever seeing images of cats - not dogs - when the training process is focused on teaching it how to recognize cats.

If this data annotation process can be automated then it reduces the cost of training an AI model, but it also dramatically reduces the time needed to teach the model about what it needs to understand.

Some experts in AI believe that models trained in data annotation are now sophisticated enough for us to presume that manual data annotation is no longer required. University of Michigan robotics professor, Jason Corso, even said it more bluntly in his article ‘Annotation is dead.’

Corso states in his article that annotation has been needed for decades and this requirement has accelerated in recent years as machine learning has become so much more powerful. He acknowledges that on the frontier of knowledge, some human interaction will still be necessary, but that the vast majority of annotation should now be possible using large language models (LLMs).

The use of LLMs in annotation not only automates the process - and there accelerates the speed - but also enhances the consistency and quality of the data labeled. This change in approach is therefore not merely about efficiency, it’s a fundamental change in how data can be prepared for machine learning applications. It ensures models are trained on accurately annotated datasets that reflect complex nuances and contexts.

Existing LLMs, such as GPT-4, Gemini, and Llama-2, can all be used to help data annotation - so the system required for automation is already largely trained.

Moving beyond the basic annotation process, data augmentation techniques further support automation by generating synthetic data that resembles real data, complete with annotations. This is particularly useful in fields like computer vision or natural language processing, where creating diverse datasets is crucial for model training. Additionally, existing data can be augmented to reduce the reliance on manual labeling.

Crowdsourcing and Human-in-the-Loop (HITL) systems combine human expertise with automation. Platforms, such as yoummday, can distribute annotation tasks across many human annotators, speeding up the process. HITL systems blend this human input with automated systems, allowing models to handle straightforward cases while humans focus on more complex or ambiguous data.

Automation can dramatically scale annotation efforts, but it usually will require substantial upfront investment in model training and development. This is where a partner such as yoummday can dramatically help - our experience in data annotation and expertise in automating this process ensures that you can greatly reduce the time and effort needed for data annotation while maintaining high accuracy.