AI has come a long way from a mere sci-fi fantasy to an important technology in our era. More organizations are adopting AI to improve their operations. Deep learning successfully positions itself in the core technology of business and even government bodies.
An important part of AI and machine learning is data. It can decide the success of an AI model. AI algorithms need data to train themselves and it is data that decides the outcome of the training. Algorithms are like a child learning to make sense of the world around them. Machine learning algorithms, when fed with a sufficient amount of data, can learn to recognize and analyze input then make decisions based on what they are taught.
When algorithms are in supervised learning, they are given sets of labelled examples. From these examples, machines learn about the labelled and how to identify them. It is similar to teaching a child to recognize everyday objects like chair, glass, tree, etc. Humans label the examples with the “ideal answer” or “ground truth” so that the AI algorithms can gradually build up their knowledge of the ground truth to improve its identification and answer.
The same process also applies to Natural Language Processing. The machines are given sets of data which have been meticulously labelled in order to learn the meaning of word, phrase, and sentence.
When we see an image, we see entities captured in the image. To a machine, however, an image is a series of pixels. With machine learning, we can teach algorithms to identify a certain collection of pixels as a semantic object. But AI algorithms can’t be able to establish understandings of objects when given random sets of data.
The data needs to be labelled by Humans in the loop, or data experts who are responsible for labelling images to train machine AI algorithms. They perform semantic segmentation on elements of images to separate images into semantically meaningful parts. When the labelled data is fed to machine learning algorithms, the machines learn to recognize objects in an image.
The ultimate object of machine learning and AI is to create machines that can work independently of human supervision and guidance. However, at this training stage, an algorithm won’t be able to learn necessary knowledge without human judgement that manifests on the labelling of input data. Machine learning solution can only be realized with the combination of human nuance and the power of machines.
People are praising the advances of machine learning and AI without knowing the participation of and reliance on humans in machine learning and AI technologies. As these powerful technologies gain its popularity and milestones, data labelling has become a specialized service, ensuring the consistency in quality and quantity for a fast-growing industry. In short, to succeed with machine learning and AI, a business needs to put data and human into perspective when building machine learning and AI strategy.