Data-Driven AI
Data-Centric AI
AI system = Code(Model / Algorithm) + Data
AI system consists of code, often referred to as a model or algorithm, and data.
Traditionally, the Model-centric approach was widely used, emphasizing the structure of algorithm, training techniques, and hyperparameter tuning.
In recent days, the Data-centric approach has taken the center stage, prioritizing the acquisition of high-quality data to enhance the performance of AI systems.
Due to its crucial role in both training and evaluating AI, data is regarded as a valuable asset. Thus, most AI developers and enterprises are dedicating efforts to secure extensive datasets, recognizing their significance in the advancement of artificial intelligence.
Why Data-Centric AI?
The core of efficient and high-performance model development and the AI systems enhancement lies in 'improvement through data.'
This is a common theme mentioned by AI companies such as Tesla and notable engineers like Andrew Ng.
Numerous case studies have shown significant improvements in performance when data is enhanced.
Large · Clean · Diverse
Data is becoming more necessary.
Consistency on data is important.
Garbage in, garbage out.
Tesla Andrej Karpathy emphasized that data need to be large / clean / diverse when he presented his research at CVPR 2021 Workshop on Autonomous Driving.
The most important thing for training model is collecting good datasets. It requires millions of videos, high quality of labeling, and a lot of edge cases, not just nominal, boring data.
Andrew Ng presented the importance of data, and presented Data-Centric in machine learning. Also, the term ‘garbage in garbage out(GIGO)’ is still used in the filed of machine learning world.
Currently, with the advancement of Hyperscale models, Large Language Models, and generative AI, the significance of data has become even more pronounced.
Complex, creative, and extensive datasets are essential for AI to achieve approaching or surpassing human capabilities.
In line with these trends, Data-centric AI has become a pivotal means to construct superior artificial intelligence systems.
How To Secure Good Data
Then, how can we collect and acquire good data?
AI bias increases when diverse annotators assign different labels to the data. Thus, it is crucial to ensure consistent labeling to mitigate subjective biases among annotators.
Leveraging big data is important for optimizing models, even when dealing with noisy data. However, with smaller datasets, data consistency and quality becomes more influential on overall performance.
Therefore, it is essential to reduce prediction errors through extensive data training.
Achieving these criteria requires the skilled data expertise.
Codevision possesses diverse experiential knowledge and specialized AI skills through collaborative efforts between research and field-based practices.
Our data experts systematically carry out operations, from collection to processing, to ensure consistent and high-quality data and tailored to customer demands.
Experience a data solution with Codevision to obtain optimal data that aligns seamlessly with your needs.
