Every single piece of information on the internet is labeled in some way or the other. This is how we identify them in the first place. So, data labeling is the process of tagging data samples to repurpose training machine learning. In other words, Data Labeling in AI provides models with context, which is how they learn anything that they generate.
What are the practical uses of AI data labeling?
Now we know that the use of tagging is crucial for identifying information on the internet and training machine learning. This is how information is fed to artificial intelligence in the form of scraping. While there are several ethical issues that come along with this, these things can only be resolved as and when we get more transparent laws and regulations about the usage of data for training models. These are a few examples of how this technique can come in handy:
1. Autonomous vehicles
Data labeling is the backbone of training self-driving and autonomous vehicles. Such cars and vehicles have the capacity to detect and react to pedestrians, objects on the road, signs, and other things that populate the road while driving.
2. Healthcare
Medical image labeling is essential for diagnosing and creating treatment plans by identifying abnormalities in X-rays and MRIs. Electronic health records also need the support of annotated patient data, which helps medical professionals make informed decisions.
3. eCommerce
All product recommendation systems rely upon data labeling. It involves analyzing data on consumer behavior, product descriptions, etc. Proper labeling reduces eros and improves engagement, thereby increasing sales.
4. Social media
Data labeling is the basis of social media algorithms and moderation. This is how different types of content are grouped and flagged. This involves tracking offensive or unsavory content floating around on the internet.
5. Financial services
Labeling transaction data is necessary for risk assessment and fraud prevention in financial services. It protects customers, banks, and non-bank financial institutions by spotting odd patterns of what might be potentially fraudulent activity.
6. Language Translation
Labeling text data is a crucial part of the language translation software. Machine translation models are only able to improve their accuracy when they are trained thoroughly with labeled data.
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Wrapping Up
That brings us closer to the prospect of data labeling for the purpose of mitigating AI bias. We’ve so far seen how machine learning has been replicating real-life biases in its technology. This is because artificial intelligence is based on data scraping and cannot create any new novel information of its own, which is why it is so prone to hallucinations and now vases. But as it seems, with more accurate labeling and stricter laws on training data, things can actually look up in ethical usage.
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