Category: AI / Machine Learning / Deep Learning
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Multimodal AI: Combining Text, Images, and Audio in Models (e.g., GPT-4V, LLaVA)
Artificial Intelligence is evolving rapidly—from processing text in chatbots to understanding images and even interpreting audio. At the forefront of this evolution is Multimodal AI: models that can process and reason across multiple data types—text, images, audio, and video—within a unified framework. Multimodal AI is not just a technical leap; it’s reshaping how machines understand…
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The Misconceptions of LLM: Is a Large Model Really Omnipotent?
In recent years, with the rapid development of large language models (LLMs), many corporate executives have been eagerly embracing this technology, believing it to be a panacea for all problems. Since early 2025, the rise of DeepSeek has further fueled market enthusiasm, especially in Hong Kong, where many enterprises have begun massive investments in LLM-related…
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Standing Firm on Professional Integrity: The Crucial Role of Data Scientists in Data Warehousing and Governance
In the realm of data science, maintaining professional integrity is not just a matter of ethics; it’s a cornerstone of effective and meaningful work. Recently, I encountered a situation during a data warehouse project that highlighted the importance of this principle. I authored a “Data Gap Analysis Report,” where I identified several internal data errors…
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The Four Pillars of a Successful Data Scientist: Logical Thinking, Hunger to Learn, Passion to Implement, and Communication Skills
In today’s data-driven world, the role of a data scientist is both crucial and multifaceted. Success in this field requires a blend of various skills and attributes. While technical proficiency and domain knowledge are important, four key elements stand out as the foundation for a successful data scientist: logical thinking, an insatiable hunger to learn…
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Understanding the Proper and Best Practices for Using Generative AI
Generative AI, particularly large language models (LLMs) like OpenAI’s ChatGPT, has revolutionized the way we interact with technology, providing powerful tools for generating text, answering questions, and even creating art. However, many people harbor misconceptions about these tools, believing they can answer anything or solve every problem. As a data scientist, it’s crucial to understand…
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Open Source Solutions for Comprehensive Data Governance and AI Governance
Introduction: As a data scientist with limited budget constraints, implementing effective data governance and AI governance practices can be challenging. However, leveraging open source solutions can provide cost-effective options for managing data, ensuring data lineage, and maintaining metadata integrity. In this article, we will explore step-by-step strategies and open source tools to establish a robust…
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Fusing AI Canvas and BADIR: A Unified Approach to Transformative AI Projects
The BADIR (Business Question, Analysis Plan, Data Collection, Insights, and Recommendation) framework and the AI Canvas framework share some common principles as they both provide a structured approach to AI and data science projects. Let’s explore how they can work together: Alignment of BADIR and AI Canvas: Business Question: Both BADIR and AI Canvas…
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Bookshelf for a Data Scientist
As a data scientist, reading book is a daily activity and most of my skills are built from reading. However, I am always reading different types of books outside the data science tools and technology. I would like to share some of the books recommended across different areas. Methodology When doing Data Science, many…
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Different Jobs/Roles in a Data Science Team
People are always saying that Data Science is so hot and expected highly paid in the industry. There are lots of people trying to join in the industry. Everyone would like to become a data scientist. However, there are not many candidates really fit for the role as a data scientist. In this article, I…
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Advanced Data Analytics with Free Tools
In the field of Data Science, there are lots of great tools without charging a penny. Data Analytic never requires software tools with high price and the analytic result should be the same either using SAS Data Miner or your own R code. As a data science consulting company, our teams are always using free…