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.

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.

Students are dreamers. In the applcation doc, or at least in first enquiry, almost all of them ask us if they are qualified for MSc AI/Data Science, the flagship program of SIAI.

Back in late 2020, during the heavy downturn of all operations due to COVID-19, basically all businesses were sent to online, including education. Universities around me all became 'digital', the form of education mostly known for an inferior and less prestigious delivery of a university degree.

As a founder of an AI business school, I come to see a load of unsophisticated 'dreamers' asking how much coding skills they need to be an 'AI Expert', or a job title that is known as 'Data Scientist'.

This book discusses all open SIAI exams that you can find from following:

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A short memoir of SIAI

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

Top brains in AI/Data Science are driven to challenging jobs like modelingSeldom a 2nd-tier company, with countless malpractices, can meet the expectations

People following AI hype are mostly completely misinformedAI/Data Science is still limited to statistical methodsHype can only attract ignorance

As a professor of AI/Data Science, I from time to time receive emails from a bunch of hyped followers claiming what they call 'recent AI' can solve things that I have been pessimistic. They usually think 'recent AI' is close to 'Artificial General Intelligence', which means the program learns by itself and it is beyond human intelligence level.