Unless you are zooming back from life on another planet, machine learning (ML) and artificial intelligence (AI) is all around us. Both AI and ML have transformed the way we live and work, and life has been made easier and comfortable. From self-driving cars, smart and nudge replies to emails, and smart communications in social media networks using emojis – all these breathtaking advancements are AI-powered. Needless to say, smart equipment and smart life have become a fundamental part of our daily routines. What is more pleasant is that AI and ML are so established in simple things that we do not notice them on a whim and only take note of their behavior in the grander scheme of things.
Rise of Data Annotation and Data Labelling
The simplest method to explain the use cases of data annotation and data labeling is to discuss supervised and unsupervised machine learning firstly.
Usually speaking, in supervised machine learning, humans are providing “labeled data” which provides the machine learning algorithm with a head start, something to go on. Humans have labeled data units using various tools or platforms such as ShaipCloud so the machine learning algorithm can implement whatever work needs to be done, now knowing something about the data it’s encountering.
By difference, unsupervised data learning includes programs in which machines have to classify data points more or less on their own.
Using a simplistic way to understand this is using a ‘fruit basket’ example. Assume you have a goal to sort apples, bananas, and grapes into logical conclusions by using an artificial intelligence algorithm. Besides labeled data, results that are already classified as apples, bananas, and grapes, all the program has to do is make a difference between these labeled test items to correctly classify the results.
Advantages of Data Annotation and Data Labelling
- With supervised learning, ML models receive correct training to make correct predictions and evaluations.
- ML automated systems can give several stellar experiences for end-users. For example, digital assistant tools and chatbots respond to users’ questions according to the speed of their demands.
- Web search engines are applying ML technology like Google in developing the efficiency of their results based on the history of research behavior of end-users.
- Likewise, ML in speech recognition has come in handy, allowing virtual assistance in human speech with the help of NLP.
- Accurately labeled data ensures success in all ML projects because the smallest error in making the data for training ML models can be disturbing and disastrous.
- Data annotation permits AI to reach its full potential. Various advantages are coming from AI, and with correct data labeling, we can get the best and most value from it.
Conclusion
Now you know why data annotation is highly significant in ML or AI. There is no insight for an AI-powered project without high-quality training data sets on the table. Training data available in different forms such as texts, images or videos are the “fuel” for ML algorithms proficient in generating any reasonable autonomous models.
