Artificial Intelligence and Machine Learning have seen a massive growth in the past few years, and have reached a market size of over 22 billion US dollars. …

When it comes to the world of AI, the word “learning” has a very specific meaning: it is the ability of a system to understand data. In the constantly evolving domain of Machine Learning, there are many learning approaches to cater to different use cases. …

Data-related tasks consume nearly 80% of the time of AI projects. This makes them a key factor in the machine learning pipeline. Within these data-related tasks, data labeling in particular takes, on average, up to one fourth of the project’s time. …

One of the most common idioms in the domains of Data Science, AI and Machine Learning is “Garbage in, garbage out”. While a simple sentence on the surface, it elegantly captures one of the most pressing issues of the domains mentioned above. …

When people talk about annotating data, there’s one topic that seems to always be the elephant in the room: image annotation. The domain of image annotation is as vast and old as data science itself. Indeed, one of the first works ever done in the field of AI was to…

As the expectations from artificial intelligence and machine learning algorithms increase, data annotation has started to become a fundamental necessity for AI projects. Unexpectedly, the data labeling process involves a significant percentage of the AI development process and cost. According to the recent report [1] of Cognilytica on data preparation…