Companies across industries are adopting Artificial Intelligence to scale up and improve their business operations. Advances in Deep Learning are helping drive the business success from e-commerce to national security.
Data is the most important ingredient to a successful recipe of an AI model. Unlike traditional coding models, the outcome of an AI algorithm is very dependent on the data used to train it as it infers results based on what it has been trained on.
It’s quite similar to teaching a young child. When a toddler sees an Alaskan husky, its parents help to identify it as a “dog”. Now the toddler has a word for the four-legged furry thing, which she can use to identify its movement and its behaviour. But what happens when the toddler comes across a cat? She may very well assume it to be a dog too. Here, the parents will help her to understand that a cat, while four-legged and furry, behaves quite differently from the concept of “dog”. The feedback mechanism helps the toddler build up a recognition framework. There may still be edge cases, for example where a very furry small dog can be mistaken for a cat – until it makes a sound. This is an additional feature extracted from the data to increase the differentiation.
In supervised learning, machines learn from labelled examples. In Computer Vision, the machine is taught to identify everyday objects like chairs, table, and pillars in a room, or cars, pedestrians, and pavement on the road. The training data set needs the “ideal answer”, also known as “ground truth”, to be associated with each training sample, for the machine to build a feedback loop and improve its answers. Associating the ground truth with the data is called labelling, and relies on human specialists. This is called human judgement. This concept also applies to other types of data. For Natural Language Processing, machines need to be taught the difference between “That chicken burger was so bad” and “I want a chicken burger so bad”. Though both sentences share several words, they mean totally different things. Hence machines need to be trained on a large volume of meticulously labelled data. This is where humans step in to parent the machine-learning model.[…]