80% of Korean students failed at SIAI not due to lack of intelligence but due to deep-rooted cultural conditioning that discourages independent thought and risk-taking
The Confucian, exam-based education system promotes rote memorization over problem-solving, making students struggle in an environment that requires deep, abstract thinking
Korea’s broader economic and corporate structure reinforces a ‘safe thinking’ mindset, making it unlikely that Western-style innovation will thrive here without significant systemic change

Before going into details, pl

Not due to math knowledge, but due to difficulty applying knowledge in real-world scenarios
accustomed to structured learning, struggle more with open-ended, problem-first approaches compared to those trained in Western-style
superficial engagement, reliance on structured guidance, avoidance of ambiguity, and resistance to open-ended problem-solving
Failed in abstraction (encoding) and application (decoding)

Since 2021, the Swiss Institute of Artificial Intelligence (SIAI) has refined its approach to teaching AI and data science (DS), learning valuable

AI Bootcamps provide emotional satisfaction but no real AI knowledge.
SIAI’s AI MBA (Business & Tech Tracks) offers real AI project exposure and strategic thinking.
Basic software engineers will be obsolete by 2035, replaced by AI and offshore talent

After launching AI MBA's business track, we sometimes have questions about the value of the track. Most people, particularly, engineers think that's just a waste of time. Some of them even claim that AI Bootcamp is the better option, as it costs less money.

Over the past few years, there has been a growing trend of STEM MBAs—business programs that integrate basic AI, analytics, and coding to appeal to professionals interested in tech-driven industries. While these programs may sound promising, in reality, most STEM MBAs provide little more than bootcamp-level technical training, leaving graduates with surface-level AI knowledge and little ability to differentiate real AI innovation from hype.

A recent discussion on GIAI Square brought up concerns about networking opportunities in the SIAI 2.0 AI MBA program. While technical students focus on engineering and quantitative finance, business track students need a different kind of networking—one that connects them to venture capitalists, private equity firms, and AI-driven business leaders.

In a recent discussion on GIAI Square, a student raised concerns about networking opportunities in the SIAI 2.0 AI MBA program, particularly about the strength of the alumni network and its impact on career opportunities post-graduation. As a professor and industry professional, I provided my perspective based on both academic experience and real-world industry exposure.

Unlike typical AI bootcamps, SIAI offers in-depth AI education with a strong foundation in mathematics, statistics, and real-world business applications.
The MSc AI/Data Science program at SIAI emphasizes rigorous scientific studies, ensuring students master the theoretical and practical aspects of AI.
SIAI’s MBA AI programs incorporate extensive business case studies, with a new MBA AI/Finance track focusing on corporate finance and financial investments.

Mathematical ability differs across cultures, with Western academia emphasizing abstraction over procedural speed
AI is automating routine calculations, making conceptual thinking more valuable than ever
Future professionals must focus on logical reasoning and model formulation to stay relevant

After years of teaching here at SIAI, we have witnessed a varying cultural differences in perception of experts in AI/Data Science in the western hemisphere and in Asia.

Many amateur data scientists have little respect to math/stat behind all computational models
Math/stat contains the modelers' logic and intuition to real world data

Boot camp is for software programming without mathematical training
MSc is a track for PhD, with in-depth scientific research written in the language of math and stat

STEM majors are known for high dropouts
Students need to have more information before jumping into STEM

Not the quality of teaching, but the way it operates
Easier admission and graduation bar applied to online degrees