AI Research Memo

AI talks turned the table and become more pessimisticIt is just another correction of exorbitant optimism and realisation of AI's current capabilitiesAI can only help us to replace jobs in low noise data

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

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

ChatGPT is to replace not jobs but tedious tasksFor newspapers, 'rewrite man' will soon be goneFor other jobs, the 'boring' parts will be replaced by AI,

Data Science can find correlation but not causalityIn stat, no causal but high correlation is called 'Spurious regression'Hallucinations in LLMs are repsentative examples of spurious correlation Imagine two twin kids living in the neighborhood. One prefers to play outside day and night, while the other mostly sticks to his video games. After a year later, doctors find that the gamer boy is much healthier, thus conclude that playing outside is bad for growing children's health.

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

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.

Math in AI/Data Science is not really math, but a shortened version of English paragraph.

Korean GDP growth was 6.4%/y for 50 years until 2022, but down to 2.1%/y in 2020s.Due to low birthrate down to 0.7, population is expected to 1/2 in 30 years.Policy fails due to nationwide preference to leftwing agenda.

Transition from column to matrix, matrix to tensor as a baseline of data feeding changed the scope of data science,

When an expectation for future is shared, market reflects it immediatelyUS Fed hints to lower interest rates in March, which is already reflected in prices

Web novel to Webtoon conversion is not only based on 'profitability'If the novel author is endowed with money or bargaining power, 'Webtoonization' may be nothing more than a marketting tool for the web novel.

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

Asian companies convert degrees into years of work experienceWithout adding extra values to AI degree, it doesn't help much in salary

The relationship between a commercial district and the concentration of consumers in a specific generation mostly is not by causal effectSimultaneity oftern requires instrumental variables

One-variable analysis can lead to big errors, so you must always understand complex relationships between various variables. Data science is a model research project that finds complex relationships between various variables. Obsessing with one variable is a past way of thinking, and you need to improve your way of thinking in line with the era of big data. When providing data science speeches, when employees come in with wrong conclusions, or when I give external lectures, the point I always emphasize is not to do 'one-variable regression.'

With high variance, 0/1 hardly yields a decent model, let alone with new set of dataWhat is known as 'interpretable' AI is no more than basic statistics