Artificial Intelligence is the talk of the town these days, and the feeling is that the wave is just getting started. Everyone is trying to jump on the hype train, and those who are truly into it, are working on creating and training their own AI models using data sets that they have personally created or curated. But, even in those data sets, there is a need to label the data in order to make it usable for training an AI model. This is where data annotation comes in.
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What is Data Annotation?
To put it simply, data annotation is defined as the process of labelling data to make it usable for machine learning. This can be achieved by employing tasks like –
- Identifying objects in images
- Transcribing audio
- Categorizing text
- Labeling parts of speech
- Drawing bounding boxes around objects in video frames
Initially, data annotation was a task that was performed manually, and ended up taking a long time. However, thanks to the continuous evolution of technology, this process can be automated to an extent using dedicated tools.
There are different approaches being employed to achieve better, more optimised data optimisation, and we will be discussing them all in this article –
AI-Assisted Annotation
As mentioned earlier, data annotation was achieved by manual effort. Engineers had to sit down, and pore through troves of data and manually label them, which typically took a long time (for obvious reasons). Now, however, there are AI tools that perform an initial labelling operation, and the human engineers can then simply review the same, saving them hours of effort.
Active Learning
These days, active learning algorithms are used to determine the data points that are most useful, and only those are labelled. This truly makes the job of human counterparts easier, and they can focus on optimising the shortlisted data points and create better models.
Synthetic data generation
There may be situations where there is a scarcity of data due to privacy concerns. In such situations, companies use AI to generate synthetic data for annotation purposes. This leads to the formation of diverse datasets that do not fully rely on real-world data.
Crowdsourcing platforms
In this approach, companies assign the task of data annotation to a pool of volunteers. Ofcourse, since the output needs to be conform to a set standard, the companies impose quality control measures, and ensure that the annotaters are well aware of the importance of this task.
Complete Automation
Since AI has been setting itself in various domains, there are some where its influence is almost autonomous. If data annotation is required in such domains, the existing AI can be used to fully automate the process.
Data annotation technology is evolving rapidly, driven by the insatiable demand for training data in AI development. As these tools become more sophisticated, they will continue to accelerate AI advancement across industries. However, it’s crucial that we address the ethical and quality challenges to ensure that the foundation of our AI systems is both robust and responsible.
