A Lapse From Model-Centric To Data-Centric AI
Recently, AI has taken off the ground and has been bringing revolutionary changes in the industry. Its influence has been seen in many aspects of businesses. Many methodologies and algorithms with varying degrees of sophistication have been developed to address a variety of problems and designed to concentrate on the technical aspects of problem-solving. So, the emphasis lies on the coding part of the problem. However, any AI solution built to solve a problem consists of two parts — algorithm and data. The recent Data-centric AI campaign launched by Andrew Ng tries to emphasize that the models have achieved quite a good amount of sophistication and it’s high time we put more focus on the quality of data.
What is Data-Centric AI? And How it Helps Data-Driven Businesses?
Many AI algorithms with varying degrees of sophistication have been developed to address a variety of problems (eg. ResNet50, Inception, VGG16, etc. for image classification). Along with that, many methodologies have been developed to further finetune the model, such as regularization, cross-validation, etc. However, these techniques are built to focus on the technical side of problem-solving. So, the emphasis lies on the coding part of the problem.
The core idea of Data-centric AI is that no amount of fine-tuning can fix bad data. Many of the models presently in use have high levels of complexity and can solve complex challenges. But in case the data is incorrect or not clear enough, the model will learn as presented. Therefore, Andrew Ng proposes to focus more on data, a new methodology where the model is kept the same, and the data is modified iteratively. Precisely, the model can be effectively notified using high-quality data. For this to work well, a proper and deep understanding of data is crucial. This is quite important because what helps to solve a business problem is a solid understanding of the problem itself. This will help us to systematically engineer data, and this can come only when there is clarity on data.
Characterizing the Aspects of High-Quality Data
For steeper insights, we want refined and high-quality data, but how do we define it, and what are the aspects of quality maintenance?
The data should be well defined. There should be clear guidelines and definitions for annotation and labeling. This could require inputs from multiple labelers and subject matter experts. For example, consider the following object detection problem. In the below figure, two lions are labeled very differently. Both ways are correct. However, the lack of a clear definition (how to label when there is another object in the foreground) led to different annotations. In more complex problems, this can be counterproductive. Therefore, it is essential to have clear guidelines.
Information such as time of creation, source, etc. are also important to determine the kind of data that is to be used. This helps us determine the principles on which the AI solution should be built. The abilityto select data precisely can be beneficial while dealing with data drift and updating the model.
High quality of data is essential to develop a clearer understanding of the problem. It orients the decision-making process to be more data-driven rather than technique-driven. Proceeding with this solution requires closer collaboration with the subject matter experts. As a result, the solutions model can be developed in a way that allows Data Scientists to comprehend and manage how the model learns. It will almost certainly lead to the development of better solutions and an improvement in their performance.
In Data-centric AI, the philosophy is aimed at the best utilization of data which requires clear standards set up from the beginning i.e., the data collection. It can motivate businesses to standardize data collection and different processes across their value chains. This will streamline the data management, which in turn will make accessing, monitoring, and analyzing data to build solutions a lot easier.
Data-centric AI brings in a bag full of benefits. Since this paradigm requires a deeper understanding of data, it can easily be integrated with the preprocessing of data, which usually takes up a massive amount of time in building a solution. As a result, the resource allocation for training in the Data-centric paradigm could be far less as it doesn’t require a lot of fine-tuning of hyperparameters. These are the benefits of Data-centric AI, to name a few.