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Labelbox | Unlocking Enterprise AI with Your Proprietary Data and Foundation Models


This is May from the GLB Division Lakehouse Department.

Based on reports from members participating in the local Data + AI SUMMIT 2023 (DAIS), the exponential progress of AI and computing and the emergence of basic models were presented in the title "Labelbox | Unlocking Enterprise AI with Your Proprietary Data and Foundation Models". This session aims to share fast, cost-effective and automated methods of how AI systems are built and applied to data. The target audience is people who are interested in AI technology, people involved in data analysis and building AI systems, and corporate decision makers.

Exponential Advances in AI and Computing

In recent years, advances in AI and computing have accelerated exponentially. The same dollar can now buy billions of computing and is expected to be much greater in the next five years than the progress we have made in the last 10 or 20 years. This exponential progress is having a profound impact on how AI systems are built and applied to data. As such, businesses need to leverage AI in a fast, cost-effective, and automated way.

Emergence of basic models and their impact

The underlying model offers a fundamentally different approach to how traditional AI systems are built. It is trained on internet-scale data, unlike the past, which required human data collection, training, testing, and development. With the advent of the foundation model, features such as OCR (optical character recognition) and sentiment analysis have become widely available. As a result, companies and individuals can easily utilize these functions, accelerating the spread of AI technology.

Committed to simplifying data-centric work

Naver and LabelBox's efforts were introduced on how to simplify the development process of data-centric work. Naver simplifies the development process for data-centric work by combining traditional and vector searches on datasets. LabelBox, on the other hand, offers an end-to-end solution that picks the underlying model, applies it to the data, and maintains quality using a human-in-the-loop system.

These efforts are expected to improve the efficiency of data-centric work.


With exponential advances in AI and computing, companies must find ways to leverage their own data and underlying models to deploy AI. By leveraging the latest concepts, features and services, businesses can harness AI in a fast, cost-effective and automated way. Examples of Naver and LabelBox were introduced as efforts to simplify data-centric work. Let's use these initiatives as a reference to promote the use of AI in our company.


This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.

Translated by Johann


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