Unveiling the True Potential of Artificial Intelligence by Shifting from Model-Centric to Data-Centric AI

Artificial Intelligence is the ability of computers and machines to perform tasks that would generally require human intelligence. AI has the potential to revolutionize countless characteristics of society and transform many paths of human existence. A basic Artificial Intelligence system requires both data and models to operate perfectly. They both work concurrently to produce the desired outcome. People familiar with AI would agree that more significance is given to model-building. But the well-known Machine Learning expert Andrew NG shared his opinion in a recent conference saying that now it’s the time to focus more on data as there have already been a lot of advancements in the models and algorithms. Spending time and effort on data would help reveal AI’s real worth in various sectors like healthcare, government, technology, and manufacturing.

Model centric AI

Model-centric AI is an artificial intelligence system built around a certain machine learning model or an algorithm. It relies on the model to make predictions or generate an outcome. Most of these systems are developed to optimize the performance of the model. This AI approach is often used when the aim is to achieve a particular performance target, such as high accuracy or high precision in a classification task.

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Model-centric AI can be resourceful and effective at solving a problem that requires analysis, such as speech or image recognition. These are automated and very convenient to deploy, as there’s no need for any manual programming. However, a model-centric system may not be as flexible or adaptable as they are designed to perform a specific task and may find it difficult to adapt to new scenarios.

Data-centric AI

Data-centric AI can be defined as an artificial intelligence-based system that is set up around huge amounts of data and uses this data for learning and making decisions. These systems mostly use machine learning techniques to analyze trends in the data, extract insights, comprehend patterns, and make predictions. This type of AI is often used when the goal is to analyze and understand complex data sets or to make predictions or decisions based on data. It can learn and improve significantly over time as it is exposed to more data.

Importance of data

Data is crucial to developing and working with artificial intelligence (AI) systems. Without access to good-quality data, it is impossible to build effective AI systems, as data is a key feature in the development and deployment of AI. For an AI system to learn and make decisions, it needs to be trained on a large amount of updated data. AI uses this data to uncover patterns and insights that may not be apparent to human beings. For example, an AI system might be trained on the data of medical records and be able to find out early warning signs of a deadly disease.

Types of data

  1. Structured data – Data that is organized traditionally in a table or a spreadsheet in a structured manner in the form of rows and columns.
  2. Unstructured data – Data with a wide range of things from images and audio to emails or text messages collected together in an unorganized manner in different formats.
  3. Nominal data – It represents categories or labels. It is referred to as nominal because it is not ordered or ranked in any way. For example – non-numeric variables represent gender, type of item, etc.
  4. Ordinal data – It represents categories that have a natural order or a ranking associated with it. For example – a list of grades like A, A+, B, etc.
  5. Discrete data – It can only take on a specific set of values. Discrete data is often used to represent countable items. For example – the number of pages in a novel, the number of chairs in a room, etc.
  6. Continuous data – Continuous data is a type of data that can take on any value within a certain range. For example – height and weight of an individual, temperature, length, width, etc.

Why moving to data-centric AI is important?

Everyone is deluged by a variety of data, such as scientific data, medical data, financial data, and so on. This data is collected every day, and analyzing such information is essential. The fast-growing, tremendous amount of data collected and stored in large data repositories has exceeded human ability for comprehension without powerful tools. Data-centric AI allows the system to adapt and evolve as the data changes. It permits organizations to make better use of vast amounts of data. It improves the effectiveness of artificial intelligence systems.

  1. It improves the performance and accuracy of the model significantly.
  2. Data directly influence the approach; therefore, it takes less time for development.
  3. The method gives rise to upto date solutions as it caters to the changing data.
  4. There is more transparency as the trends and patterns are explainable by looking at the data.

Steps for shifting to a data-centric AI approach

  1. Understanding the business problem and determining how data-centric AI can help address it.
  2. Collecting, cleaning, and pre-processing high-quality data and storing it in a data warehouse.
  3. Using machine learning algorithms to analyze and understand the data and make predictions.
  4. Incorporating the insights from the data for good decision-making.
  5. Monitoring and iterating the performance of the data-centric AI system, including updation of the data, retraining the models if needed, fine-tuning the system, etc., according to the business requirements.

Conclusion

Data-centric AI can offer many benefits, such as improved accuracy, flexibility, efficiency, and transparency. These systems are even more reliable as they can learn from large amounts of data and make predictions based on patterns and trends that may not be immediately apparent to humans. They learn and improve over time as new data becomes available. Thus, shifting to a data-centric approach is the need of the hour to explore and utilize the strength of AI even better.


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References:

  • https://ai-infrastructure.org/moving-from-model-centric-to-data-centric-ai-2/
  • https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence
  • https://towardsdatascience.com/from-model-centric-to-data-centric-artificial-intelligence-77e423f3f593
  • https://neptune.ai/blog/data-centric-vs-model-centric-machine-learning
  • https://landing.ai/data-centric-ai/
  • https://www.picsellia.com/post/data-centric-ai-vs-model
  • https://medium.com/analytics-vidhya/moving-from-model-centric-to-data-centric-approach-1468fb5dbafb
  • https://www.analyticsinsight.net/from-model-centric-to-data-centric-how-the-ai-ecosystem-is-moving/
  • https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence

Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.