AI Research Memo

AI translation reshapes the labor market, eroding low-skill roles while rewarding domain expertise Education must shift toward “language plus” skills—pairing translation with data, law, or health Policy should teach students to work with machines, not against them

AI and robotics remain narrow tools, excelling only in tightly defined tasks Human versatility—handling exceptions, combining roles, and adapting to context—remains the decisive advantage Education policy must prioritize training for this versatility, turning automation into complement rather than substitute

Search behaves like reinforcement learning, rewarding confirmation Narrow queries and clicks shrink exposure at scale Break the loop with IV-style ranking and teach students to triangulate queries

Student well-being is falling fast AI chatbots are spreading quickly Without safeguards, risks will escalate

AI’s IMO gold isn’t AGI Deploy it as an instrumented calculator Require refusal metrics and proof logs <

LLMs are not conscious, only probabilistic parrota They often mislead through errors, biases, and manipulations Education must use them as tools, never as advisors

I am in my early 40s and work at an office near Magok Naru Station and I live near Haengsin Station in Goyang City. I used to commute by company shuttle, but recently I've taken up cycling as a hobby and now commute by bike. The biggest reason I got into cycling was because of the positive image I had of Seoul's public bicycle program, Ddareungyi.

While founding a university (SIAI), I encountered a surprising reality—university rankings, like any evaluative system, are shaped by more than just academic performance. Factors such as institutional branding, media visibility, and methodological choices play a role in shaping how institutions are perceived and ranked. This has led to ongoing debates about how rankings should be structured and whether certain metrics introduce unintended biases.

I have spent years in AI and data science, believing that structured models and quantitative analysis were the future. That perspective changed the moment I became a target of an orchestrated misinformation campaign—one that wasn’t random but designed to destroy my credibility, my institution’s reputation, and my work.

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.

GIAI's primary research objective with the coming cycle's of MSc AI/Data Science is to build a graph-based Shapley Value for HR contribution analysis.

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 modelsMath/stat contains the modelers' logic and intuition to real world data