You can understand marketing trends and related martechs, in which personalized recommendations have become particularly important in recent years. We will understand the concept and rise and fall of personalization-related technologies such as 3rd/1st Party Data, retail media, and retargeting advertising, and will inform you of marketing technologies that will be revived in the future.

Who creates good data, and how can we make it well?

IT IS IMPORTANT TO BE FAMILIAR WITH THE DIFFERENT ROLES AND IMPACTS OF DIFFERENT PEOPLE ON DATA, AND HOW TO STRUCTURE THESE PARTS INTO THE SYSTEMS THAT CREATE AI. IT INTRODUCES THE DATA BUILDING PIPELINE AND INTRODUCES THE IMPORTANT PARTS OF THE HUMAN ROLE IN EACH PROCESS.

What are the recommendations that users are enthusiastic about? For those of you who are thinking about a customized recommendation system, we will tell you about the experiences of the authors of the RecSys 2022 papers and those who have visited them.

Let's discuss the importance of data-centric AI in solving real business problems and look at the key technologies and methods that make up it. Additionally, we will introduce AWS services that support such data-centric AI.

I will explain the limitations I encountered while working on the document search task for using the book database, the pain points I encountered during the modeling process, and the methodology applied.

WE WILL SHARE SOLUTIONS FOR INTRODUCING AI DOCUMENT PROCESSING SYSTEMS, FROM A BRIEF EXPLANATION OF AI INTRODUCTION TO WHAT LIMITATIONS THE EXISTING RULE-BASED TECHNOLOGY HAS.

HOW DIFFERENT IS THE WAY SILICON VALLEY AI STARTUPS WORK?

A NEW ALTERNATIVE TO OVERCOME THE LIMITATIONS OF MODEL AND DATA-DRIVEN AI PARADIGM - INTERACTION-ORIENTED AI

SHARING AI, KAGGLE COMPETITION SOLUTIONS TO HELP YOUTH IMPROVE WRITING SKILLS

WHO ARE THE SOFTWARE ENGINEERS AT AI COMPANIES? DO I NEED AN AI BACKGROUND TO BECOME A SOFTWARE ENGINEER FOR AN AI COMPANY? SOLVE ALL YOUR QUESTIONS AND CONCERNS ABOUT SWE OF AI COMPANIES THAT YOU HAVE NOT KNOWN BEFORE.

The NFL Health & Safety - Helmet Assignment Kaggle Competition reveals the 6th place out of 833 teams, the gold medal winner.

We will share the solution of the upstage team that took 3rd place in 'Ventilator Pressure Prediction' conducted by Google Brain.

The NFL Health & Safety - Helmet Assignment Kaggle Competition reveals the 6th place out of 833 teams, the gold medal winner.

Let's solve A to Z about the know-how of a 4th-year undergraduate student from the Department of Liberal Arts, getting a gold medal as soon as they enter the AI competition Kaggle where 6 million ML developers around the world participate!

We will introduce the solution that won the first place in the legal document summary technology section of the 2021 Artificial Intelligence Online Contest and share the review of the completion of Naver Boost Camp AI Tech.

INTRODUCES RESEARCH AND EFFORTS TO ADDRESS THE 'IMPACT OF THE COST-DISEASE THEORY OF TREASURE' IN SERVICE-INTENSIVE INDUSTRIES. DISCOVER RESEARCH AND ACHIEVEMENTS IN THE PROCESS OF PRODUCTIVITY PROBLEMS AND DATA PROCESSING THROUGH AI APPLICATIONS IN HEALTHCARE.

IN AN ONLINE-BASED LEARNING ENVIRONMENT, WE WILL INTRODUCE A CASE STUDY ON HOW TO ANALYZE THE LEARNER'S STATUS USING THE KNOWLEDGE TRACKING (KT) TECHNIQUE AND PROVIDE CUSTOMIZED RECOMMENDATIONS, AND CASES APPLIED TO ACTUAL SERVICES.

HOSTED BY UPSTAGE, WE WILL SHARE THE PROCESS AND RESULTS OF THE DEVELOPMENT OF A KOREAN LANGUAGE MODEL AND BENCHMARK DATASET TO LAY THE FOUNDATION FOR THE DEVELOPMENT OF KOREAN NLP, AND INFORM YOU IN DETAIL ABOUT THE KOREAN NATURAL LANGUAGE UNDERSTANDING EVALUATION DATASET, KLUE.

We will tell you where the difference between the practical application of machine learning and the theoretical approach in academic research arises, and how various machine learning models are introduced into real industrial settings.

Yunsu Kim, who won a gold medal in Kaggle's RANZCR CLiP, a medical image classification competition, shares stories about AI model development solutions, the process of building an image model learning pipeline, and experiences gained by participating in the competition.

Yejin Yoon from the Amazon Web Services team introduces the basics of graph modeling, graph-related engines and languages, and use cases, and shares essential tips for beginners in graphic databases.

INTRODUCING THE METHODOLOGY OF BUILDING A MACHINE LEARNING PIPELINE ON THE TOPIC OF MACHINE LEARNING CI/CD PERFORMED WITH GOOGLE PRODUCTS. CHECK OUT VARIOUS KNOWLEDGE AND INFORMATION ABOUT MACHINE LEARNING THAT IS EASY TO UNDERSTAND EVEN FOR BEGINNERS AND INTERMEDIATES.

INTRODUCING THE METHODOLOGY OF BUILDING A MACHINE LEARNING PIPELINE ON THE TOPIC OF MACHINE LEARNING CI/CD PERFORMED WITH GOOGLE PRODUCTS. CHECK OUT VARIOUS KNOWLEDGE AND INFORMATION ABOUT MACHINE LEARNING THAT IS EASY TO UNDERSTAND EVEN FOR BEGINNERS AND INTERMEDIATES.