Maritime 4.0: Innovation Driven by AI, Data, and Cyber Security

— Practical Tools and Strategic Applications You Can Use Right Now As of 2025, the maritime industry is riding the second wave of digital transformation. While the first wave focused on sensor-based automation—think AIS, ECDIS, PMS—the current trend is shifting toward true intelligence . We’re no longer just viewing data. We’re now understanding , predicting , and making decisions alongside AI. So here’s the most important question at this moment: “What kind of AI are we using, for what purpose, and how effectively?” In this article, I’ll walk you through the latest AI tools—particularly conversational and development-oriented solutions—and offer practical strategies tailored for maritime operations. Category Purpose Primary Users 💼 Business Planning Strategy development, report writing, market analysis Planners, Executives, Strategy Teams 🔬 Technical Research Tech trend analysis, research summarization, policy review R&D Teams, Class Society Liaisons, Secu...

Shifting Perceptions and Expectations of AI from Companies The way companies perceive and expect from AI is evolving. Until now, aside from cutting-edge research and product development , the role of AI engineers and data scientists in enterprises can generally be categorized as follows: Image Processing : Companies have widely utilized CNN-based models for object recognition and comparison (performance improvement). To overcome data scarcity, techniques like few-shot learning have been applied in areas such as product search, similar image/product recommendation, and design generation . Text Processing : Enterprises have leveraged various Transformer-based models trained on proprietary datasets for customer intent recognition and sentiment analysis . Data-Driven Decision Support : Companies often assume they possess sufficient data, but in many cases, they lack the necessary datasets. AI teams analyze available data, form hypotheses , and generate insightful reports to support an...

Let's explore the Face Recognition Project ! 😊 In this project, we will maximize the use of open-source resources with the following objectives: 1️⃣ Understand key libraries and source code 2️⃣ Set up the required development environment 3️⃣ Optimize performance for better accuracy and efficiency 4️⃣ Share results and discuss potential improvements The goal is to go beyond simple implementation , actively improving the system and exploring ways to enhance its capabilities. 🚀

Subject: Natural Language Processing (NLP) Through my work in implementing AI chatbots and taking the Natural Language Processing (NLP) course, I realized that since the introduction of the "Attention Is All You Need" paper and the breakthrough models like "Transformer" , "BERT" , and "GPT-2" , there has been a significant increase in both corporate and academic interest in dialog systems such as AI chatbots and customer support services , with NLP technologies at their core. This shift can be attributed to the advancements in NLP technologies , which have reached a point where natural language services are widely accepted without significant resistance . In this context, I want to explore what Natural Language Processing (NLP) is, why the interest in it has grown rapidly , and what technologies have made this possible . Additionally, I will present the requirements and methods for developing AI services based on NLP technology . Approach...

  reference - http://hunkim.github.io/ml/ 모두를 위한 머신러닝/딥러닝 강의 -   홍콩과기대 김성훈- 시즌 1 - 딥러닝의 기본 (TF 1.X lab 완료!)  비디오 리스트 수업에 사용하는 코드  https://github.com/hunkim/DeepLearningZeroToAll 수업의 개요  비디오    슬라이드  머신러닝의 개념과 용어  비디오   (TensorFlow의 기본  Lab 비디오  )  강의 슬라이드    Lab 슬라이드  Linear Regression 의 개념  비디오   (TensorFlow 로 구현  Lab 비디오  )  강의 슬라이드    Lab 슬라이드  Linear Regression cost함수 최소화  비디오   (TensorFlow 로 구현  Lab 비디오  )  강의 슬라이드    Lab 슬라이드  여러개의 입력(feature)의 Linear Regression  비디오   (TensorFlow 로 구현  Lab1 비디오  ) (파일 데이타 로딩  Lab2 비디오  )  강의 슬라이드    Lab 슬라이드  Logistic (Regression) Classification  강의 슬라이드    실습 슬라이드  Hypothesis 함수 소개  비디오  Cost 함수 소개  비디오  TensorFlow 에서의 구현  비디오  Softmax Regression (Multinomial Logist...