The artificial intelligence paradigm shifts from model-centric to data-centric

Data-oriented artificial intelligence can help reduce illusions and biases in generative AI systems, thereby improving the quality of their output.

Translated from The Paradigm Shift from Model-Centric to Data-Centric AI, author Rahul Pradhan has more than 16 years of experience and currently serves as the Vice President of Product and Strategy at Couchbase.

With the advancement of artificial intelligence (AI) such as transformer neural networks and generative adversarial networks (GAN), the technology field is undergoing a major transformation. Not only do these technologies hold enormous potential, they can also unlock innovation and creativity at scale. They can provide more accurate and efficient solutions and bring new business and development opportunities to various industries. The combination of transformer neural networks and GANs enables AI systems to better understand and generate human language, images and sounds, thereby promoting the development of natural language processing, computer vision and speech recognition and other fields. As these technologies become increasingly mature, we can expect more innovative applications and breakthroughs to emerge, bringing more to human society. As AI develops, data becomes critical. Data is the lifeblood that drives machine learning projects, turning concepts into practical insights. However, effectively leveraging data in AI projects is fraught with challenges, which hinders its adoption and realization of transformational value.

To enhance the development of AI, we are currently undergoing a paradigm shift from model-centric to data-centric AI transformation. The purpose of this shift is to reduce the hallucinations and biases that arise in generative adversarial network systems. By focusing on data-centric AI and bringing models closer to the data, we can improve the output of AI models and help enterprises realize their full potential. This shift will bring an important impetus to the development of AI.

Model-centered AI method

The traditional model-centered AI method is the main way of machine learning development. Its core idea is to improve the performance of the model through continuous iteration to generate the best model to handle the given data set. Researchers and engineers spend a lot of time fine-tuning the parameters, number of layers, and other architectural elements of the model. However, because building and fine-tuning models has been a complex and resource-intensive process in the past, requiring deep expertise to produce meaningful results, data has often been treated as a secondary factor. However, in recent years, with the advancement of machine learning technology and the enhancement of computing power, the importance of data has gradually been emphasized. Modern AI methods pay more attention to the quality and diversity of data, training models through larger data sets and more powerful computing power, thereby improving the performance and generalization capabilities of the model. This data-centric approach has become the current mainstream trend in the field of machine learning.

Transformation to data-centric AI

A data-centric approach improves the quality of data for model training, including cleaning, enhancing and ensuring that the data represents real-world scenarios.

As artificial intelligence (AI) models mature and expand in complexity, organizations need to focus on improving data quality and building closer alliances between models and data. In this evolving field, it is important to make a necessary and clear shift: bringing the model closer to the data, rather than transferring data to the model. This improves the quality of model output and reduces the illusions that often plague AI systems. A data-centric approach to AI is a cornerstone for organizations that want to deliver generative and predictive experiences based on the latest data.

Although data-centered AI is the direction of future development, model-centered AI still plays a key role in some scenarios. Model-centric AI is particularly important when data are limited or the goal is to explore model complexity and performance limits. It drives the frontiers of AI research and provides the possibility to solve problems where high-quality data is difficult to obtain. Therefore, model-centric AI is not just a supplement to data-driven AI, but an indispensable approach in the field of AI.

Reimagining AI with Data-Centric Thinking

By shifting to a data-centric approach to AI that ensures data quality and relevance, organizations can gain the following benefits:

Bridging reality by improving data quality

One of the typical benefits of a data-centric approach is the ability to deliver experiences that are tightly integrated with real-world scenarios. Unlike model-centric approaches where models often struggle with the fallacies of low-quality data, data-centric artificial intelligence (AI) seeks to bridge the gap between AI models and the dynamic reality they are trying to navigate.

Mitigating the Shadow of Illusions

AI hallucinations are primarily caused by defective data, characterized by the generation of incorrect or fictitious information. Moving to a data-centric approach can enhance the likelihood of reducing these errors. Training a model on a cleaner, more representative dataset produces more accurate and reliable output.

Unleashing the full potential of predictive and generative AI

With a solid foundation of high-quality data, organizations can unleash the full spectrum of AI’s predictive and generative capabilities. This shift makes AI more capable of interpreting existing data patterns while also generating new insights and experiences, fostering a culture of innovation and informed decision-making.

Leading the future of AI evolution with data

The transformation from model-centered to data-centered artificial intelligence (AI) methods represents a basic change in the way of thinking. This is putting data at the heart of the AI ​​transformation journey. This shift is not just a technical tweak, but a conceptual recalibration that puts data at the heart of AI. As organizations embark on this path, they must cultivate a robust data infrastructure, develop data literacy, and create a culture that values ​​data as the cornerstone of AI's promise.

Blending the best of both

Building powerful AI solutions requires a nuanced understanding of when to emphasize data and focus on model innovation. Balancing the benefits of model-centric and data-centric AI is critical to solving today's AI challenges so that organizations can get the most value from their AI projects. To help ensure that AI models are developed on the latest data and are accurate and reliable, organizations must embrace the transformation to data-centric AI.

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