Math in AI/Data Science is not really math, but a shortened version of English paragraph.
In science, researchers often ask 'plz speak in plain English', a presentation that math is just to explain science in more scientific way.

I liked math until high school, but it became an abomination during my college days. I had no choice but to make records of math courses on my transcript as it was one of the key factors for PhD admission, but even after years of graduate study and research, I still don't think I like math. I liked it when it was solving a riddle.

The questions in high school textbooks and exams are mostly about finding out who did what. But the very first math course in college forces you to prove a theorem, like 0+0=0. Wait, 0+0=0? Isn't it obvious? Why do you need a proof for this? I just didn't eat any apple, so did my sister. So, nobody ate any apple. Why do you need lines of mathematical proof for this simple concept?

Then, while teaching AI/Data Science, I often claim that math equations in the textbook are just short version of long but plain English. I tell them "Don't be afraid of math. It is just a language." Students are usually puzzled, and given a bunch of 0+0=0 like proof in the basic math textbooks for first year college courses, I get to grasp why my students showed no consent to the statement (initially). So, let me illustrate my detailed back-up.

Math is just a language, but only in a certain context

Before I begin arguing math is a language, I would like to make a clear statement that math is not really a language as in academic defintion of language. The structure of math theorem and corollary, for example, is not a replacement of paragraph with a leading statement and supporting examples. There might be some similarity, given that both are used to build logical thinking, but again, I am not comparing math and language in 1-to-1 sense.

I still claim that math is a language, but in a certain context. My topic of study, along with many other closely related disciplines, usually create notes and papers with math jargons. Mathematicians maybe baffled by me claiming that data science relies on math jargons, but almost all STEM majors have stacks of textbooks mostly covered with math equations. The difference between math and non-math STEM majors is that the math equations in non-math textbooks have different meaning. For data science, if you find y=f(a,b,c), it means a, b, and c are the explanatory variables to y by a non-linear regressional form of f. In math, I guess you just read it "y is a function of a, b, and c."

My data science lecture notes usually are 10-15 pages for a 3-hour-long class. It might look too short for many of you, but in fact I need more time to cover the 15-pager notes. Why? For each page, I condense many key concepts in a few math equations. Just like above statement "a, b, and c are the explanatory variables to y by a non-linear regressional form of f", I read the equations in 'plain English'. In addition to that, I give lots of real life examples of the equation so that students can fully understand what it really means. Small variations of the equations also need hours to explain.

Let me bring up one example. Adam, Bailey, and Charlie have worked together to do a group assignment, but it is unsure if they split the job equally. Say, you know exactly how the work was divided. How can you shorten the long paragraph?

y=f(a,b,c) has all that is needed. Depending on how they divided the work, the function f is determined. If y is not a 0~100 scale grade but a 0/1 grade, then the function f has to reflect the transformation. In machine learning (or any similar computational statistics), we require logistic/probit regressions.

In their assignment, I usually skip math equation and give a long story about Adam, Bailey, and Charlie. As an example, Charlie said he's going to put together Adam's and Bailey's research at night, because he's got a date with his girlfriend in the afternoon. At 11pm, while Charlie was combining Adam's and Bailey's works, he found that Bailey almost did nothing. He had to do it by himself until 3am, and re-structured everything until 6am. We all know that Charlie did a lot more work than Bailey. Then, let's build it in a formal fashion, like we scientists do. How much weight would you give it to b and c, compared to a? How would you change the functional form, if Dana, Charlie's girlfriend, helped his assignment at night? What if she takes the same class by another teacher and she has already done the same assignment with her classmates?

If one knows all possibilities, y=f(a,b,c) is a simple and short replacement of above 4 paragraphes, or even more variations to come. This is why I call math is just a language. I am just a lazy guy looking for the most efficient way of delivering my message, so I strictly prefer to type y=f(a,b,c) instead of 4 paragraphes.

Math is a univeral language, again only in a certain context

Teaching data science is fun, because it is like my high school math. Instead of constructing boring proof for seemingly an obvious theorem, I try to see hidden structures of data set and re-design model according to the given problem. The diversion from real math is due to the fact that I use math as a tool, not as a mean. For mathematicians, my way of using math might be an insult, but I often say to my students that we do not major math but data science.

Let's think about medieval European countries when French, German, and Italian were first formed by the process of pidgin and creole. In case you are not familiar with two words, pidgin language is to refer a language spoken by a children by parents without common tongue. Creole language is to refer a common language shared by those children. When parents do not share common tongue, children often learn only part of the two languages and the family creates some sort of a new language for internal communication. This is called pidgin process. If it is shared by a town or a group of towns, and become another language with its own grammar, then it is called creole process.

For data scientists, mathematics is not Latin, but French, German, or Italian, at best. The form is math (like Latin alphabet), but the way we use it is quite different from mathematicians. For major European languages, for some parts, they are almost identical. For data science, computer science, natural science, and even economics, some math forms mean exactly the same. But the way scientists use the math equations in their context is often different from others, just like French is a significant diversion from German (or vice versa).

Well-educated intellectuals in medieval Europe should be able to understand Latin, which must have helped him/her to travel across western Europe without much trouble in communication. At least basic communication would have been possible. STEM students with heavy graduate course training should be able to understand math jargons, which help them to understand other majors' research, at least partially.

Latin was a universal language in medieval Europe, so as math to many science disciplines.

Math in AI/Data Science is just another language spoken only by data scientists

Having said all that, I hope you can now understand that my math is different from mathematician's math. Their math is like Latin spoken by ancient Rome. My math is simply Latin alphabet to write French, German, Italian, and/or English. I just borrowed the alphabet system for my own study.

When we have trouble understanding presentations with heavy math, we often ask the presentor, "Hey, can you please lay it out in plain English?"

The concepts in AI/Data Science can be, and should be able to be, written in plain English. But then 4 paragraphes may not be enough to replace y=f(a,b,c). If you need way more than 4 paragraphes, then what's the more efficient way to deliver your message? This is where you need to create your own language, like creole process. The same process occurs to many other STEM majors. For one, even economics had decades of battle between sociology-based and math-based research methods. In 1980s, sociology line lost the battle, because it was not sharp enough to build the scientific logic. In other words, math jargons were a superior means of communication to 4 paragraphes of plain English in scientific studies of economics. Now one can find sociology style economics only in a few British universities. In other schools, those researchers can find teaching positions in history or sociology major. And, mainstream economists do not see them economists.

The field of AI/Data Science evolves in a similar fashion. For once, people thought software engineers are data scientists in that both jobs require computer programming. I guess now in these days nobody would argue like that. Software engineers are just engineers with programming skills for websites, databases, and hardware monitoring systems. Data Scientists do create computer programs, but it is not about websites or databases. It is about finding hidden patterns in data, building a mathematically robust model with explanatory variables, and predicting user behaviors by model-based pattern analysis.

What's still funny is that when I speak to another data scientists, I expect them to understand y=f(a,b,c), like "Hey, y is a function of a, b, and c". I don't want to lay it out with 4 paragraphes. It's not me alone that many data scientists are just as lazy as I am, and we want our counterparties to understand the shorter version. It may sound snobbish that we build a wall against non-math speakers (depsite the fact that we also are not math majors), but I think this is an evident example that data scientists use math as a form of (creole) language. We just want the same language to be spoken among us, just like Japanese speaking tourists looking for Japanese speaking guide. English speaking guides have little to no value to them.

Math in AI/Data Science can be, should be, and must be translated to 'plain English'

A few years ago, I have created an MBA program for AI/Data Science that shares the same math-based courses with senior year BSc AI/Data Science, but does not require hard math/stat knoweldge. I only ask them to borrow the concept from math heavy lecture notes and apply it to real life examples. It is because I wholeheartedly believe that the simple equation still can be translated to 4 paragraphes. Given that we still have to speak to each other in our own tongue, it should be and must be translated to plain language, if to be used in real life.

As an example, in the course, I teach cases of endogeneity, including measurement error, omitted variable bias, and simultaneity. For BSc students, I make them to derive mathematical forms of bias, but for MBA students, I only ask them to follow the logic that what bias is expected for each endogenous case, and what are closely related life examples in business.

An MBA student tries to explain his company's manufacture line's random error that slows down automated process by measurement error. The error results in attenuation bias that under-estimates mismeasured variable's impact in scale. Had the product line manager knew the link between measurement error and attenuation bias, the loss of automation due to that error must have attracted a lot more attention.

Like an above example, some MBA students in fact show way better performance than students in MSc in AI/Data Science, more heavily mathematical track. They think math track is superior, although many of them cannot match math forms to actual AI/Data Science concepts. They fail not because they do not have pre-training in math, but because they just cannot read f(a,b,c) as work allocation model by Adam, Bailey, and Charlie. They are simply too distracted to math forms.

During admission, there are a bunch of stubborn students with a die-hard claim that MSc or death, and absolutely no MBA. They see MBA a sort of blasphamy. But within a few weeks of study, they begin to understand that hard math is not needed unless they want to write cutting edge scientific dissertations. Most students are looking for industry jobs, and the MBA with lots of data scientific intuition was way more than enough.

The teaching medium, again, is 'plain English'.

With the help of AI translator algorithms, I now can say that the teaching medium is 'plain language'.

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.
Few globally competent companies leave the country.

Financial Times, on Apr 22, 2024, reported that South Korean economic miracle is about to be over. The claim is simple. The 50 years of economic growth from 1970 to 2022 with 6.4%/year is replaced by mere 2.1%/year growth in 2020s, and it will be down to 0.6% in the 2030s and 0.1% in 2040s.

For a Korean descendent, it is no secret that the country's economy has never been active since 1997 when Asian financial crisis had a hard hit to all east Asian countries. Based on IMF's economic analysis, South Korea's national GDP had grown over 7% per year until 1997, and with linear projection, it was expected that South Korea will catch up western European major economies in early 2010s by per capita GNI. Even after the painful recovery until 2002, per year growth rate was over 5% for another a decade, way above pessimistic economists' expectation, whose projection was somewhere near Japan's 1990s, the country nearly stopped growing after the burst of property bubble at the end of 1980s.

The 1970s model worked until 1990s

South Korean economic growth is mostly based on 1970s model that the national government provided subsidy highly export driven heavy industires. The country provide upto 20% of national GDP as a form debt insurnace to Korean large manufacturers for their debt-financing from rich countries. The model worked well up until late 1980s. Economic prosperity by extremely favorable global economic conditions coined as '3-Lows (Low Korean won, Low Petro price, Low interest rate)'. The success helped the economy to rely on short-term borrowing from US, Japan, and other major economies until 1997.

Forced to fire-sale multiple businesses at a bargain price, the passion for growth in the country was gone. Large business owners become extremely conservative on new investments. They also turned heads to domestic market, where competitors are small and weak. SMEs have been wiped out by large conglomerates, most of which were incapable of competiting internationally, thus turning to safe domestic battle.

Had time and money, but collective policy failures killed all

Compared to North Korean economic struggle, South Korea has been the symbol of success in capitalism. Over the 50 years, South Korean per capital GNI has grown from US$1,000 to US33,000, while Northern brothers are still struggling with US$1,000 to US$2,000, depending on agricultural production affected by weather conditions. In other words, while North Korea is still in pre-industrialization economy, the South has grown to a major industrial power with lots of cutting edge technological products, including semiconductors by Samsung Electrics and SK Hynix.

The country was able to keep higher than expected growth up until 2020, largely because of China's massive import. China, since the opening of its economy in 1998 by joing WTO(World Trade Organization). China has been the key buyer of South Korean electric appliances, smartphones, semiconductors, and many other tech products, most of which were crucial for its own economic developement.

But experts have raised attention that China's technological catch-up was a growingly imminent threat to Korean's tech superiority. The gap is now mostly gone. Even the US is now raising a bar high against China for 'security purpose'. The fact that the US has been keen on China's national challenge to semiconductor industry now even to chem and bio is an outstanding proof that China is no longer a tech follower to western key economic leaders, not to mention South Korea.

US-China trade war expedited Korea's fall

By a simple Cobb-Douglas model, with capital and labor, it is easy to guess that capital withdrawal from China resulted in massive surplus to the US market, where the economy is suffering from higher than usual inflation. It's the cost that the US market pays. On the other hand, without capital base, the Chinese economy is going to suffer from capital shock like Asian Financial Crisis of 1997. Facilities are there, but money is gone. Until there is any capital influx to fill the gap, be it by IMF and World Bank like 1997 or long-term internal capital building like Great Britain from 1970s to 2000s, we won't be seeing China's economic rise.

The sluggish Chinese economy deadly affected its neighbors. One of the trading partners that were hit hard is South Korea in Asia, Germany in Europe, and Apple in BigTechs. Germany used to be the symbol of economic growth in Europe, at least during European sovereign debt crisis of 2008-2012. Unlike other big tech companies, Apple kept its dependency to China until very recently. The company lost stock values by 40% since the peak in 2022. South Korean story is not that different. The major trading partner zipped its wallet. 15% to 40% of trade surplus, depending on industries, were disappeared. Korean companies were not ready to replace the loss by other sources.

The evident example that South Korea was not ready to China's withdrawal is its dependency to Aqueous urea solution(AUS) for diesel powered trucks. Over 90%, sometimes upto 100%, of AUS consumption in the nation was from Chinese sources, which was stopped twice recently. In Dec 2021 and Sep 2023, lack of AUS pushed Korea's large cargo freight trucks being inoperable. The country's logistic system was nearly shut down. The government tried to replace it for two years from 2021 to 2023, but the country still failed to avoid another AUS crisis in Sep 2023.

For South Korea, China was a mixed blessing. Dependency to China from 1998 upto 2020 helped the economy to keep high enough growth rate to run the country. But heavy dependency now creates detrimental effects to every corner of the country's industrial base. Simply put, Korea has been too dependent to China.

Education, policy, companies, all failed jointly and simultaneously

Fellow professors in major Korean universities do not expect Korea to rebound anytime soon. The economic growth model that worked in 1970s have not been working as early as late 1990s. The government officials have, however, been ignorant of failing system. Back then, under military regime, only successful business men were given government subsidy. The selection process was tough. Failing businesses were forced to close down, before creating any harm to wider economy.

But the introduction of democratic system that brough freedom to business, press, and civil rights groups deprived the government of total control on resource allocation. The country no longer is operated by a single powerful and efficient planner. While recovering from devastating financial crisis in 1997, every agents in the economy learned that the government is no longer a powerful fatherhood and tasted some economic freedom.

Had the freedom been regulated properly, the economy would have been armed by national support in subsidy, human resources, as well as 50 million domestic customer base. Instead, except a few internationally qualified products, most of them turned their heads inward. For lack of English speaking manpower, companies were not able to compete internationally, unless they have hard and unique products. Building a brand from 'copying machine' to 'tech leader' costs years of endeavor that we can only see successes in RAM chips and K-pop singers.

Korea had time to renew its economic policy. But Chinese honeyspot provided too much illusion that Korean companies thought its superiority will stay forever. Koreans have kept its 'copying machine' policy. The government officials were not as keen as 1970s, thus any sugarcoated success in overseas countries helped Korean companies to be in a position of demand to subsidy. The country stopped grow technologically. Industries, academia, and press become entangled to one goal. Massive exaggeration to earn government subsidy. For one, Korean government wasted US$10 billion just for basic programming courses in K-12 that are no longer needed in the era of Generatvie AI. In the meantime, China was not willing to stay culturally, technologically, and intellectually behind its tiny neighbor that they have looked down for the last two millenia.

While Korean education puts less and less emphasis on math/stat/science, Chinese took opposite steps. Now Korea's the most demanded college major is medical doctoral track, while Chinese put Mathematics as the top major. Despite higher competition to medical track with large expected income, some students no longer pursue medical track just because they are afraid of high school mathematics and science.

Aging society with lowest birthrate in the world

Will there be any hope in Korea? Many of us see otherwise. The country is dying, literally. The median age of the country is 49.5 as of year 2024. Generations born in 1980s had nearly 1 million babies in a year, while in 2020s they only have 200,000. The population, particularly in working age, will be shrunken to 1/5 in a few decades later. Due to touch economic conditions, young couples push marriage as late as 30s and 40s. Babies by women after 35 have shown some level of genetic defect that even 1/5 of working population won't be as effective as today.

Together with mis-guided educational policy, the country is expected to have less capable brain as the time goes. International competition will become more severe due to desperate Chinese catch up in technology. Companies have already lost passion for growth.

Economic reforms have been tried, but the unpopular minority seldom wins in elections. Even if it wins, the opposition is too strong to overcome. Officials expect that the country is on a ticking bomb without any immediate means of defusion.

Though I admit that other major economies are suffering from similar growth fatigue, it is at least evident that South Korea is now on the list of 'no-hope'. If you are looking for growth stocks, go and look for other countries.

Transition from column to matrix, matrix to tensor as a baseline of data feeding changed the scope of data science, 
but faster results in 'better', only when we apply the tool to the right place, with right approach.

Back in early 2000s, when I first learned Matlab to solve basic regression problems, I was told Matlab is the better programming tool because it runs data by 'Matrix'. Instead of other software packages that feed data to computer system by 'column', Matlab loads data with larger chunk at once, which accelerates processing speed by O(nxk) to O(n). More precisely, given how the RAM is fed by the softwares, it was essentially O(k) to O(1).

Together with a couple of other features, such as quick conversion of Matlab code to C code, Matlab earned huge popularity. A single copy was well over US$10,000, but companies with deep R&D and universities with significant STEM research facilities all jumped to Matlab. While it seemed there were no other competitors, there was a rising free alternative, called R, that had packages handling data just like Matlab. R also created its own data handler, which worked faster than Matlab for loop calculation. What I often call R-style (like Gangnam style), replaced loop calculations from feeding column to matrix type single process.

R, now called Posit, became my main software tool for research, until I found it's failure to handling imaginary numbers. I had trouble reconciliating R's outcome with my hand-driven solution and Matlab's. Later, I ended up with Mathematica, but given the price tag attached to Mathematica, I still relied on R for communicating with research colleagues. Even after prevailing Python data packages, upto Tensorflow and PyTorch, I did not really bother to code in Python. Tensorflow was (and is) also available on R, and there was not that much speed improvement in Python. If I wanted faster calculation for multi-dimensional tasks that require Tensorflow, I coded the work in Matlab, and transformed to C. There initially was a little bug, but the Matlab's price tag did worth the money.

A few years back, I found Julia, which has similar grammar with R and Python, but with C-like speed in calculations with support for numerous Python packages. Though I am not an expert, but I feel more conversant with Julia than I do to Python.

When I pull this story, I get questions like wy I traveled around multiple software tools? Have my math models become far more evolved that I required other tools? In fact, my math models are usually simple. At least to me. Then, why from Matlab to R, Mathematica, Python, and Julia?

Since I only had programming experience from Q-Basic, before Matlab, I really did not appreciate the speed enhancement by 'Matrix'-based calculations. But when I switched to R, for loops, I almost cried. It almost felt like Santa's Christmas package had a console gamer that can play games that I have dreamed of for years. I was able to solve numerous problems that I had not been able to, and the way I code solution also got affected.

The same transition affected me when I first came across 'Tensorflow'. I am not a computer scientist, so I do not touch image, text, or any other low-noise data, so the introduction of tensorflow by computer guys failed to earn my initial attention. However, on my way back, I came to think of the transition from Matlab to R, and similar challenges that I had had trouble with. There were a number of 3D data sets that I had to re-array them with matrix. There were infinitely many data sets in shape of panel data and multi-sourced time series.

When in search for right stat library that can help solving my math problems in simple functions, R usually was not my first choice. It was mathematica, and it still is, but since the introduction of tensorflow, I always think of how to leverage 3D data structure to minimize my coding work.

Once successful, it not only helps me to save time in coding, but it tremendously changes my 'waiting' time. During my PhD, for one night, the night before supposed meeting with my advisor, I found a small but super mega important error in my calculation. I was able to re-derive closed solutions, but I was absolutely sure that my laptop won't give me a full-set simulation by the next morning. I cheated with the simulation and created a fake graph. My advisor was a very nice guy to pinpoint something was wrong with my simluation within a few seconds. I confessed. I was too in a hurry, but I should've skipped that week's meeting. I remember it took me years to earn his confidence. With faster machine tools that are available these days, I don't think I should fake my simulation. I just need my brain to process faster, more accurately, and more honestly.

After the introduction of H100, many researchers in LLM feel less burden on handling massive size data. As AI chips getting faster, the size of data that we can handle at the given amount of time will be increasing with exponential capacity. It will certainly eliminate cases like my untruthful communication with the advisor, but I always ask myself, "Where do I need hundreds of H100?"

Though I do appreicate the benefits of faster computer processing and I do admit that the benefits of cheaper computational cost that opens opportunities that have not been explored, it still needs to answer 'where' and 'why' I need that.

When an expectation for future is shared, market reflects it immediately
US Fed hints to lower interest rates in March, which is already reflected in prices
Bitcoin prices also rely on people's belief on speculative demands

The US Fed determines the base interest rate approximately once every 1.5 months, eight times a year. There is no reason for the market to immediately follow the next day when the Federal Reserve sets an interest rate, and in fact, changing the base rate or target interest rate does not mean that it can change the market the next day, but it is a method of controlling the amount of money supplied to general banks, It is common for interest rates to be substantially adjusted within one to two weeks by appropriately utilizing methods such as controlling bond sales volume.

The system in which most central banks around the world set base interest rates in a similar way and the market moves accordingly has been maintained steadily since the early 1980s. The only difference from before was that the money supply was the target at that time, and now the interest rate is the target. As experience with appropriate market intervention accumulates, the central bank also learns how to deal with the market, and the market also changes according to the central bank's control. The experience of becoming familiar with interpreting expressions goes back at least 40 years, going back as far as the Great Depression in the United States in 1929.

However, the Federal Reserve declared that it is not time to lower interest rates yet and that it will wait until next year, but interest rates at commercial banks are lowering day after day. I briefly looked at the changes in US interest rates in the Financial Times, and saw that long-term bond interest rates were falling day by day.

Why is the market interest rate lowering while the Federal Reserve remains silent?

Realization of expectations

Let’s say that in one month, the interest rate falls 1% from now. Unless you need to get a loan tomorrow, you will have to wait a month before going to the bank. No, these days, you can send documents through apps and non-face-to-face loans are also active, so you won't have to open your banking app and open the loan menu for a month.

From the perspective of a bank that needs to make a lot of loans to secure profitability, if the number of such customers increases, it will have to suck its fingers for a month. What happens if there is a rumor that interest rates will fall further in two months? You may have to suck only your fingers for two months.

Let’s put ourselves in the position of a bank branch manager. In any case, it is expected that the central bank will lower interest rates in a month, and everyone in the market knows that, so it is not a post-reflection where interest rate adjustments are hastily made in the market after the central bank announcement, but everyone is not interested in the announcement date. If it is certain that it will be reflected in advance, there will be predictions that the market interest rate will be adjusted sooner than one month. Since you have worked your way up to the branch manager level, you clearly know how the industry is going, so you can probably expect to receive a call from the head office in two weeks to lower the interest rate and ask for loans and deposits. However, the only time a loan is issued on the same day after receiving the loan documents is when the president's closest aide comes and makes a loud noise. Usually, more than a week is spent on review. There are many cases where it takes 2 weeks or 1 month.

Now, as a branch manager with 20+ years of banking experience who knows all of this, what choice would you make if it was very certain that the central bank would lower interest rates in one month? You have to build up a track record by giving out a lot of loans to be able to look beyond branch manager, right? We have to win the competition with other branches, right?

Probably a month ago, he issued an (unofficial) work order to his branch staff to inform customers that loan screening would be done with lower interest rates, and while having lunch with wealthy people nearby, he said that his branch would provide loans with lower interest rates, and talked to good people around him about it. We will introduce you to commercial buildings. They say that you can make money if you buy something before someone else does.

When everyone has the same expectation, it is reflected right now

When I was studying for my doctorate in Boston, there was so much snow in early January that all classes were cancelled. Then, in February, when school started late, a professor emailed us in advance to tell us to clear our schedules, saying that classes would be held on from Monday to Friday.

I walked into class on the first day (Monday), and as the classmates were joking that we would see each other every day that week, and the professor came to the classroom. And then to us

I'm planning to take a 'Surprise quiz' this week.

We were thinking that the eccentric professor was teasing us with strange things again. The professor asked again when they would take the surprise quiz. For a moment, my mind raced: When will be the exam? (The answer is in the last line of the explanation below.)

---

If there is no Surprise Quiz by Thursday, Friday becomes the day to take the Quiz. It's no longer a surprise. So Friday cannot be the day to take the Surprise quiz.

What happens if there is no surprise quiz by Wednesday? Since Friday is not necessarily the day to take the Surprise quiz, the remaining day is Thursday. But if Friday is excluded and only Thursday remains, isn't Thursday also a Surprise? So it's not Thursday?

So what happens if there is no Surprise quiz by Tuesday? As you can probably guess by now, Friday, Thursday, Wednesday, and Tuesday do not all meet the conditions for Surprise by this logic. What about the remaining days?

It was Monday, right now, when the professor spoke.

---

As explained above, we are told to take out a piece of paper, write our names, write an answer that logically explains when the Surprise quiz will be, and submit it. I had no idea, but then I suddenly realized that the answer I had to submit now was the answer to the Surprise quiz, so I wrote the answer above and submitted it.

The above example is a good explanation of why the stock price of a company jumps right now if you predict that the stock price of that company will rise fivefold in one month. In reality, the stock market determines stock prices based on the company's profitability over two or three quarters, not on its profitability today. If the company is expected to grow explosively during the second or third quarter, this will be reflected in advance today or tomorrow. The reason it is delayed until tomorrow is due to regulations such as daily price limits and the time it takes to spread information. Just as there is a gap between students who can submit answers to test questions right away and students who need to hear explanations from their friends after the test, the more advanced information is, the slower its spread may be.

Everyone knows this, so why does the Fed say no?

Until last October and November, at least some people disagreed with the claim that an interest rate cut would be visible in March of next year. As there is growing confidence that the US will enter a recession in December, there is now talk of lowering interest rates at a meeting on January 31st rather than in March. Wall Street financial experts voted for a possibility that was close to 10%, which was only 0% just a month ago. Meanwhile, Federal Reserve Chairman Powell continues to evade his comments, saying that he cannot yet definitively say that he will lower interest rates. We all know that even if we don't know about January, we are sure about March, but he has much more information than us, and there are countless economics doctors under him who will research and submit reports, so why does he react with such ignorance? Should I do it?

Let's look at another example similar to the Surprise quiz above.

When the professor entered the first class of the semester, he announced that the grade for this class would be determined by one final exam, and that he planned to make it extremely difficult. Many students who were trying to earn credits day by day will probably escape during the course adjustment period. The remaining students have a lot of complaints, but they still persevere and listen carefully to the class, and later on, because the content is too difficult, they may form a study group. Let's imagine that it's right before the final exam and your professor knows that you studied so hard.

The professor's original goal was for students to study hard, not to harass them by giving difficult test questions. Writing tests is a hassle, and grading them is even more bothersome. If you have faith that the remaining students will do well since you kicked out the students who tried to eat raw, it may be okay to just give all the remaining students an A. Because everyone must have studied hard.

When I entered the exam room,

No exam. You all have As. Merry Christmas and Happy New Year!

Isn’t it written like this?

From the students' perspective, they may feel like they are being made fun of and that they feel helpless. However, from the professor’s perspective, this decision was the best choice for him.

• Students who tried to eat it raw were kicked out.
• The remaining students studied hard.
• Reduced the hassle of writing test questions
• You don't have to grade
• When entering your grade, you only need to enter the A value.
• No more students complaining about grading.

The above example is called 'Time Inconsistency' in game theory, and is often used as a general example of a case where the optimal choice varies depending on time. Of course, if we continue to use the same strategy, 'students who want to eat raw' will flock to register for the next semester. So, in the next semester, you must take the exam and become an 'F bomber' who gives a large number of F grades. At a minimum, students must use the Time Inconsistency strategy at unpredictable intervals for the strategy to be effective.

The same logic can be applied to Federal Reserve Chairman Powell. Although interest rates are scheduled to be lowered in March or January next year, if they remain silent until the end, it could reflect their will to prevent overheating of the economy by raising interest rates. Then, if interest rates are suddenly lowered, an economic recession can be avoided.

Those who do macroeconomics summarize this with the expressions ‘discretion’ and ‘rules.’ 'Discretion' refers to government policy that responds in accordance with market conditions, and 'rules' refers to a decision-making structure that ignores market conditions and moves in accordance with standard values. Generally, a structure that promotes 'rules' on the outside and uses 'discretion' behind the scenes. has worked like a market rule for the past 40 years.

Because of this accumulated experience, sometimes the central banker sticks to the 'rules' until the end and devises a defensive strategy so that the market does not expect 'discretion', and sometimes he comes up with a strategy to respond faster than the market expects. These are all choices made to show that market expectations are not unconditionally followed by using Time Inconsistency or vice versa.

Examples

Such cases of surprise quizzes and no exams can often be found around us.

Although products like Bitcoin are nothing more than 'digital pieces' with no actual value, there are some people who have a firm belief that it will become a new currency replacing the central government's currency, and some who are not sure about currency and just buy it because the price goes up. Prices fluctuate repeatedly due to the buying and selling actions of the (overwhelming) majority of like-minded investors. The logic of a surprise quiz is hidden in the behavior of buying because it seems like it will go up, and in the attitude of never admitting it and insisting on the value until the end, even though you know in your heart that it is not actually worth it, there is a central bank-style strategy using no exam hidden. .

The same goes for the behavior of 'Mabari', a so-called securities broker who raises the stock price of theme stocks by creating wind, and the sales pitch of academies that say you can become an AI expert with a salary in the hundreds of millions of dollars by simply obtaining a code is also the same. They all cleverly exploit the asymmetry of information, package tomorrow's uncertain value as if it is great, and sell today's products by inflating their value.

Although it is not necessarily a case of fraud, cases where value is reflected in advance are common around us. If the price of an apartment in Gangnam looks like it will rise, it rises overnight, and if it looks like it will fall, it moves several hundred million won in a single morning. This is because the market does not wait and immediately reflects changed information.

Of course, this pre-reflected information may not always be correct. You will often hear the expression ‘over-shooting’, which refers to a situation where the market overreacts and stock prices rise excessively, or real estate prices fall excessively. There may be many reasons, but it happens because people who follow what others say and brainwash their brains do not accurately reflect the value of information. Generally, in the stock market, if there is a large rise for one or two days, the stock price tends to fall slightly the next day, which is a clear example of 'overshooting'.

Can you guess when the interest rate will drop?

Whenever I bring up this topic, the person who was dozing off wakes up at the end and asks, 'Please tell me when the interest rate will go down.' He says he can't follow complicated logic, he just needs to know when the interest rate goes down.

If you have been following the story above, you will be predicting that interest rate adjustments will continue to occur in the market between the Christmas and New Year holidays before the central bank lowers interest rates. It is unclear whether the decision to lower interest rates will be made on January 31 or March 20 next year. Because it’s their heart. Economic indicators are just numbers, and ultimately, they are values ​​that only move when people make decisions that risk their future reputations, but I can't get into their minds.

However, since they also have the rest of their lives, they will try to make rational decisions, and those who are smart enough to solve the Surprise quiz on the spot will adjust their expectations the fastest and become market readers, and those who solve the problem will become the market readers. People who have heard of it and know about it will miss the opportunity due to the information time lag, and people who ask 'just tell me when it will arrive' will only respond belatedly after the whole incident has occurred. While you're sending emails asking who's right, you'll find out later that the market correction is over. To paraphrase, it is already coming down. The 30-year maturity bond interest rate, which was close to 5.0% a month ago, fell to 4.0%?

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.
Data science modeling based on market variables unable to grab such cases

A student in SIAI's MBA AI/BigData progam, struggling with her thesis, chose her topic as the condition for turning a web novel into a webtoon. In general, people would simply think that if the number of views is high and the sales volume of the web novel is large, a follow-on contract with a webtoon studio will be much easier. She brought in a few reference data science papers, but they only looked into publicly available information. What if the conversion was the choice of the web novel author? What if the author just wanted to spend more marketing budget by adding webtoon in his line-up?

Literature mostly runs hierarchical structures during 'deep learning' and use 'SVM', a task that simply relies on computer calculations, and calculate the number of all cases provided by the Python library. Sorry to put it this way, but such calculations are nothing more than a waste of computer resources. It has also been pointed out that the crude reports of such researchers are still registered as academic papers.

Put all crawled data into 'AI', then it will swing a majic wand?

Converting a web novel into a webtoon can be seen as changing a written story book into an illustrated story book. Professor Daeyoung Lee, Dean of the Graduate School of Arts at Chung-Ang University, explained that the change to OTT is a change to video story books.

The reason this transition is not easy is because the transition costs are high. Domestic webtoon studios have a team of designers ranging from as few as 5 to as many as dozens of designers, and the market has been differentiated considerably into a market where even a small character image or pattern that seems simple to our eyes must be purchased and used. After paying all the labor costs and purchasing costs for characters, patterns, etc., it still takes $$$ to turn a web novel into a webtoon.

This is probably the mindset of typical 'business experts' to think that manpower and funds will be concentrated on web novels that seem to have a high possibility of success as webtoons, as investment money is invested and new commercialization challenges are required.

However, the market does not operate solely on the logic of capital, and 'plans' based on the logic of capital are often wrong due to failing to read the market properly. In other words, even if you create a model by collecting data such as the number of views, comments, and purchases provided by platforms and consider the possibility of webtoonization and the success of the webtoon, it is unlikely that it will actually be correct.

One thing to point out here is that although there are many errors due to market uncertainty, there are also a significant number of errors due to model inaccuracy.

Wrong data, wrong model

For those who simply think that 'deep learning' or 'artificial intelligence' will take care of it, creating a model incorrectly means using a less suitable algorithm when one of the 'deep learning' algorithms is said to be a better fit, or worse. It will result in the understanding that good artificial intelligence should be used, but less good artificial intelligence is used.

However, which 'deep learning' or 'artificial intelligence' is a good fit and which one is not a good fit is a matter of lower priority. What is really important is how accurately you can capture the market structure hidden in the data, so you must be able to verify whether it fits well not only by chance in the data selected today, but also consistently fits well in the data selected in the future. Unfortunately, we have already seen for a long time that most 'artificial intelligence'-related papers published in Korea intentionally select and compare data from well-matched time points, and professors' research capabilities are judged simply by the number of K-SCI papers, and the papers are compared. We cannot help but point out that proper verification is not carried out due to the Ministry of Education's crude regulations regarding which academic journals that appear frequently are good journals.

The calculation known as 'deep learning' is simply one of the graph models that finds nonlinear patterns in a more computationally dependent manner. In natural language that must be used according to grammar, computer games that must be operated according to rules, etc., there may be no major problems in use because the probability of errors in the data itself is close to 0%, but the above webtoonization process is not expected to respond in the market. There may be problems that are not resolved, and the decision-making process for webtoons is likely to be quite different from what an outsider would see.

Simply put, it can be pointed out that the barriers given to writers who already have a successful 'track record' are completely different from the barriers given to new writers. Kang Full, a writer who recently achieved great success with 'Moving', explained in an interview that he started with the intellectual property rights of webtoons from the beginning, and that he made major decisions during the transition to OTT. This is a situation that ordinary web novel and webtoon writers cannot even imagine. This is because most web novel and webtoon platforms can sell their content on the platform through contracts that retain intellectual property rights for secondary works.

How much of it is possible for an author to decide whether to make a webtoon or an OTT, reflecting his or her own will? If this proportion increases, what conclusion will the ‘deep learning’ model above produce?

The general public's way of thinking does not include cases where webtoons and OTT adaptations are carried out at the author's will. The 'artificial intelligence' models mentioned above will only explain what percentage of the 'logic of capital' that operates inside the web novel and webtoon platform is correct. However, as soon as the proportion of 'author's will' instead of 'logic of capital' is reflected increases, that model will judge the effects of variables we expected to be much lower, and conversely, it will appear as if the effects of unexpected variables are higher. In reality, it was simply because we failed to include an important variable called 'author's will' that should have been reflected in the model, but since we did not even consider that part, we only ended up with an absurd story with an absurd title of 'Webtoonization process informed by artificial intelligence'.

Before data collection, understand the market first

It has now been two months since the student brought that model. For the past two months, I have been asking her to properly understand the market situation to find the missing pieces in the webtoonization process.

From my experience with business, I have seen that even though the company thought that it could take on an interesting challenge with enough data, it could not proceed due to the lack of the ‘Chairman’s will’. On the other hand, companies that were completely unprepared or did not even have the necessary manpower said, ‘This is the story you heard from the Chairman.’ I've seen countless times where they come up with absurd project ideas saying they're going to proceed 'as usual', and then only IT developers are hired without data science experts, and the work of copying open libraries from overseas markets is repeated.

Considering the amount of capital and market conditions that are also required for the webtoonization process, it is highly likely that a significant number of webtoons will be included in web novel writers' new work contracts in the form of a 'bundle', which is naturally included to attract already successful web novel writers, and generate profits. In the case of writers who want to dominate the webtoon studio, they are likely to sign a contract with the webtoon platform by signing a contract with the webtoon studio themselves and starting to serialize the webtoon after the first 100 or 300 episodes of the web novel are released. From the perspective of a web novel writer who has already experienced that profits increase due to the additional promotion of the web novel as the webtoon is developed, there are cases where the webtoon product is viewed as one of the promotional strategies to sell their intellectual property (IP) at a higher price. It happens.

To the general public, this 'author's will' may seem like an exception, but even if the above proportion of web novels converted to webtoons exceeds 30%, it becomes impossible to explain webtoons using data collected through general thinking. In a situation where there are already various market factors that make it difficult to increase accuracy, and in a situation where more than 30% is driven by other variables such as 'the author's will' rather than 'market logic', how can data collected through general thinking lead to a meaningful explanation? Can I?

Data science is not about learning ‘deep learning’ but about building an appropriate model

In the end, it comes back to the point I always give to students. It is pointed out that 'we must understand reality and find a model that fits that reality.' In plain English, the expression changes to the need to find a model that fits the 'Data Generating Process (DGP)', but the explanatory model related to webtoonization above is a model that does not currently take 'DGP into consideration' at all. If scholars are in a situation where they are listening to the same presentation, complaints such as 'Who on earth selected the presenters' may arise, and there will be many cases where they will just leave even if they are criticized for being rude. This is because such an announcement itself is already disrespectful to the attendees.

In the above situation, in order to create a model that can be considered for DGP, you must have a lot of background knowledge about the web novel and webtoon markets. It does not reflect factors such as how web novel writers on major platforms communicate with platform managers, what the market relationship between writers and platforms is like, and to what extent and how the government intervenes, and simply inserts materials scraped from the Internet. There is no point in simply doing the work of ‘putting data into’ the models that appear in ‘artificial intelligence’ textbooks. If an understanding of the market can be derived from that data, it would be an attractive data work, but as I keep saying, if the data is not in the form of natural language that follows grammar or a game that follows rules, it will only be a waste of computer resources with no meaning. It's just that.

I don't know whether that student will be able to do some market research to destroy my counterargument at the meeting next month, or whether he will change the detailed structure of the model based on his understanding of the market, or worse, whether he will change the topic. What is certain is that a 'paper' with the name 'data' as a simple way to put the collected data into a coding library will end up being nothing more than a 'mixed-up code' containing only one's own delusions and a 'novel filled with text only'.

Not the quality of teaching, but the way it operates
Easier admission and graduation bar applied to online degrees
Studies show that higher quality attracts more passion from students

Although much of the prejudice against online education courses has disappeared during the COVID-19 period, there is still a strong prejudice that online education is of lower quality than offline education. This is what I feel while actually teaching, and although there is no significant difference in the content of the lecture itself between making a video lecture and giving a lecture in the field, there is a gap in communication with students, and unless a new video is created every time, it is difficult to convey past content. It seems like there could be a problem.

On the other hand, I often get the response that it is much better to have videos because they can listen to the lecture content repeatedly. Since the course I teach is an artificial intelligence course based on mathematics and statistics, I heard that students who forget or do not know mathematical terminology and statistical theory often play the video several times and look up related concepts through textbooks or Google searches. There is a strong prejudice that the level of online education is lower, but since it is online and can be played repeatedly, it can be seen as an advantage that advanced concepts can be taught more confidently in class.

Is online inferior to offline?

While running a degree program online, I have been wondering why there is a general prejudice about the gap between offline and online. The conclusion reached based on experience until recently is that although the lecture content is the same, the operating method is different. How on earth is it different?

The biggest difference is that, unlike offline universities, universities that run online degree programs do not establish a fierce competition system and often leave the door to admission widely open. There is a perception that online education is a supplementary course to a degree course, or a course that fills the required credits, but it is extremely rare to run a degree course that is so difficult that it is perceived as a course that requires a difficult challenge as a professional degree.

Another difference is that there is a big difference in the interactions between professors and students, and among students. While pursuing a graduate degree in a major overseas city such as London or Boston, having to spend a lot of time and money to stay there was a disadvantage, but the bond and intimacy with the students studying together during the degree program was built very densely. Such intimacy goes beyond simply knowing faces and becoming friends on social media accounts, as there was the common experience of sharing test questions and difficult content during a degree, and resolving frustrating issues while writing a thesis. You may have come to think that offline education is more valuable.

Domestic Open University and major overseas online universities are also trying to create a common point of contact between students by taking exams on-site instead of online or arranging study groups among students in order to solve the problem of bonding and intimacy between students. It takes a lot of effort.

The final conclusion I came to after looking at these cases was that the difficulty of admission, the difficulty of learning content, the effort to follow the learning progress, and the similar level of understanding among current students were not found in online universities so far, so we can compare offline and online universities. I came to the conclusion that there was a distinction between .

Would making up for the gap with an online degree make a difference?

First of all, I raised the level of education to a level not found in domestic universities. Most of the lecture content was based on what I had heard at prestigious global universities and what my friends around me had heard, and the exam questions were raised to a level that even students at prestigious global universities would find challenging. There were many cases where students from prestigious domestic universities and those with master's or doctoral degrees from domestic universities thought it was a light degree because it was an online university, but ran away in shock. There was even a community post asking if . Once it became known that it was an online university, there was quite a stir in the English-speaking community.

I have definitely gained the experience of realizing that if you raise the difficulty level of education, the aspects that you lightly think of as online largely disappear. So, can there be a significant difference between online and offline in terms of student achievement?

The table above is an excerpt from a study conducted to determine whether the test score gap between students who took classes online and students who took classes offline was significant. In the case of our school, we have never run offline lectures, but a similar conclusion has been drawn from the difference in grades between students who frequently visited offline and asked many questions.

First, in (1) – OLS analysis above, we can see that students who took online classes received grades that were about 4.91 points lower than students who took offline classes. Various conditions must be taken into consideration, such as the student's level may be different, the student may not have studied hard, etc. However, since it is a simple analysis that does not take into account any consideration, the accuracy is very low. In fact, if students who only take classes online do not go to school due to laziness, their lack of passion for learning may be directly reflected in their test scores, but this is an analysis value that is not reasonably reflected.

To solve this problem, in (2) – IV, the distance between the offline classroom and the students' residence was used as an instrumental variable that can eliminate the external factor of students' laziness. This is because the closer the distance is, the easier it will be to take offline classes. Even though external factors were removed using this variable, the test scores of online students were still 2.08 points lower. After looking at this, we can conclude that online education lowers students' academic achievement.

However, a question arose as to whether it would be possible to leverage students' passion for studying beyond simple distance. While looking for various variables, I thought that the number of library visits could be used as an appropriate indicator of passion, as it is expected that passionate students will visit the library more actively. The calculation transformed into (3) - IV showed that students who diligently attended the library received 0.91 points higher scores, and the decline in scores due to online education was reduced to only 0.56 points.

Another question that arises here is how close the library is to the students' residences. Just as the proximity to an offline classroom was used as a major variable, the proximity of the library is likely to have had an effect on the number of library visits.

So (4) – After confirming that students who were assigned a dormitory by random drawing using IV calculations did not have a direct effect on test scores by analyzing the correlation between distance from the classroom and test scores, we determined the frequency of library visits among students in that group. and recalculated the gap in test scores due to taking online courses.

(5) – As shown in IV, with the variable of distance completely removed, visiting the library helped increase the test score by 2.09 points, and taking online courses actually helped increase the test score by 6.09 points.

As can be seen in the above example, the basic simple analysis of (1) leads to a misleading conclusion that online lectures reduce students' academic achievement, while the calculation in (5) after readjusting the problem between variables shows that online lectures reduce students' academic achievement. Students who listened carefully to lectures achieved higher achievement levels.

This is consistent with actual educational experience: students who do not listen to video lectures just once, but take them repeatedly and continuously look up various materials, have higher academic achievement. In particular, students who repeated sections and paused dozens of times during video playback performed more than 1% better than students who watched the lecture mainly by skipping quickly. When removing the effects of variables such as cases where students were in a study group, the average score of fellow students in the study group, score distribution, and basic academic background before entering the degree program, the video lecture attendance pattern is simply at the level of 20 or 5 points. It was not a gap, but a difference large enough to determine pass or fail.

Not because it is online, but because of differences in students’ attitudes and school management

The conclusion that can be confidently drawn based on actual data and various studies is that there is no platform-based reason why online education should be undervalued compared to offline education. The reason for the difference is that universities are operating online education courses as lifelong education centers to make additional money, and because online education has been operated so lightly for the past several decades, students approach it with prejudice.

In fact, by providing high-quality education and organizing the program in a way that it was natural for students to fail if they did not study passionately, the gap with offline programs was greatly reduced, and the student's own passion emerged as the most important factor in determining academic achievement.

Nevertheless, completely non-face-to-face education does not help greatly in increasing the bond between professors and students, and makes it difficult for professors to predict students' academic achievement because they cannot make eye contact with individual students. In particular, in the case of Asian students, they rarely ask questions, so I have experienced that it is not easy to gauge whether students are really following along well when there are no questions.

A supplementary system would likely include periodic quizzes and careful grading of assignment results, and if the online lecture is being held live, calling students by name and asking them questions would also be a good idea.

Asian companies convert degrees into years of work experience
Without adding extra values to AI degree, it doesn't help much in salary
'Dummification' in variable change is required to avoid wrong conclusion

In every new group, I hide the fact that I have studied upto PhD, but there comes a moment when I have no choice but to make a professional remark. When I end up revealing that my bag strap is a little longer than others, I always get asked questions. They sense that I am an educated guy only through a brief conversation, but the question is whether the market actually values ​​it more highly.

When asked the same question, it seems that in Asia they are usually sold only for their 'name value', and the western hemisphere, they seem to go through a very thorough evaluation process to see if one has actually studied more and know more, and are therefore more capable of being used in corporate work.

Typical Asian companies

I've met many Asian companies, but hardly had I seen anyone with a reasonable internal validation standard to measure one's ability, except counting years of schooling as years of work experience. Given that for some degrees, it takes way more effort and skillsets than others, you may come to understand that Asian style is too rigid to yield misrepresentation of true ability.

In order for degree education to actually help increase wages, a decent evaluation model is required. Let's assume that we are creating a data-based model to determine whether the AI degree actually helps increase wages. For example, a new company has grown a bit and is now actively trying to recruit highly educated talent to the company. Although there is a vague perception that the salary level should be set at a different level from the personnel it has hired so far, there is actually a certain level of salary. This is a situation worth considering if you only have very superficial figures about whether you should give it.

Asian companies usually end up only looking for comparative information, such as how much salary large corporations in the same industry are paying. Rather than specifically judging what kind of study was done during the degree program and how helpful it is to the company, the 'salary' is determined through simple separation into Ph.D, Masters, or Bachelors. Since most Asian universities have lower standard in grad school, companies separate graduate degrees by US/Europe and Asia. They create a salary table for each group, and place employees into the table. That's how they set salaries.

The annual salary structure of large companies that I have seen in Asia sets the degree program to 2 years for a master's and 5 years for a doctoral degree, and applies the salary table based on the value equivalent to the number of years worked at the company. For example, if a student who entered the integrated master's and doctoral program at Harvard University immediately after graduating from an Asian university and graduated after 6 years of hard work gets a job at an Asian company, the human resources team applies 5 years to the doctoral degree program. The salary range is calculated at the same level as an employee with 5 years of experience. Of course, since you graduated from a prestigious university, you may expect higher salary through various bonuses, etc., but as the 'salary table' structure of Asian companies has remained unchanged for the past several decades, it is difficult to avoid differenciating an employee with 6 years of experience with a PhD holder from a prestigious university.

I get a lot of absurd questions about whether it would be possible to find out by simply gathering 100 people with bachelor, master, and doctoral degree, finding out their salaries, and performing 'artificial intelligence' analysis. If the above case is true, then no matter what calculation method is used, be it highly computer resouce consuming recent calculation method or simple linear regression, as long as salary is calculated based on the annualization, it will not be concluded that a degree program is helpful. There might be some PhD programs that require over 6 years of study, yet your salary in Asian companies will be just like employees with 5 years experience after a bachelor's.

Harmful effects of a simple salary calculation method

Let's imagine that there is a very smart person who knows this situation. If you are a talented person with exceptional capabilities, it is unlikely that you will settle for the salary determined by the salary table, so a situation may arise where you are not interested in the large company. Companies looking for talent with major technological industry capabilities such as artificial intelligence and semiconductors are bound to have deeper concerns about salary. This is because you may experience a personnel failure by hiring people who are not skilled but only have a degree.

In fact, the research lab run by some passionate professors at Seoul National University operates by the western style that students have to write a decent dissertation if to graduate, regardless of how many years it takes. This receives a lot of criticism from students who want to get jobs at Korean companies. You can find various criticisms of the passionate professors on websites such as Dr. Kim's Net, which compiles evaluations of domestic researchers. The simple annualization is preventing the growth of proper researchers.

In the end, due to the salary structure created for convenience due to Asian companies lacking the capacity to make complex decisions, the people they hire are mainly people who have completed a degree program in 2 or 5 years in line with the general perception, ignoring the quality of thesis.

Salary standard model where salary is calculated based on competency

Let's step away from frustrating Asian cases. So you get your degree by competency. Let's build a data analysis in accordance with the western standard, where the degree can be an absolute indicator of competency.

First, you can consider a dummy variable that determines whether or not you have a degree as an explanatory variable. Next, salary growth rate becomes another important variable. This is because salary growth rates may vary depending on the degree. Lastly, to include the correlation between the degree dummy variable and the salary growth rate variable as a variable, a variable that multiplies the two variables is also added. Adding this last variable allows us to distinguish between salary growth without a degree and salary growth with a degree. If you want to distinguish between master's and doctoral degrees, you can set two types of dummy variables and add the salary growth rate as a variable multiplied by the two variables.

What if you want to distinguish between those who have an AI-related degree and those who have not? Just add a dummy variable indicating that you have an AI-related degree, and add an additional variable multiplied by the salary growth rate in the same manner as above. Of course, it does not necessarily have to be limited to AI, and various possibilities can be changed and applied.

One question that arises here is that each school has a different reputation, and the actual abilities of its graduates are probably different, so is there a way to distinguish them? Just like adding the AI-related degree condition above, just add one more new dummy variable. For example, you can create dummy variables for things like whether you graduated from a top 5 university or whether your thesis was published in a high-quality journal.

If you use the ‘artificial intelligence calculation method’, isn’t there a need to create dummy variables?

The biggest reason why the above overseas standard salary model is difficult to apply in Asia is that it is extremely rare for the research methodology of advanced degree courses to actually be applied, and it is also very rare for the value to actually translate into company profits.

In the above example, when data analysis is performed by simply designating a categorical variable without creating a dummy variable, the computer code actually goes through the process of transforming the categories into dummy variables. In the machine learning field, this task is called ‘One-hot-encoding’. However, when 'Bachelor's - Master's - Doctoral' is changed to '1-2-3' or '0-1-2', the weight in calculating the annual salary of a doctoral degree holder is 1.5 times that of a master's degree holder (ratio of 2-3). , or an error occurs when calculating by 2 times (ratio of 1-2). In this case, the master's degree and doctoral degree must be classified as independent variables to separate the effect of each salary increase. If the wrong weight is entered, in the case of '0-1-2', it may be concluded that the salary increase rate for a doctoral degree falls to about half that of a master's degree, and in the case of '1-2-3', the same can be said for a master's degree. , an error is made in evaluating the salary increase rate of a doctoral degree by 50% or 67% lower than the actual effect.

Since 'artificial intelligence calculation methods' are essentially calculations that process statistical regression analysis in a non-linear manner, it is very rare to avoid data preprocessing, which is essential for distinguishing the effects of each variable in regression analysis. Data function sets (library) widely used in basic languages ​​such as Python, which are widely known, do not take all of these cases into consideration and provide conclusions at the level of non-majors according to the situation of each data.

Even if you do not point out specific media articles or the papers they refer to, you may have often seen expressions that a degree program does not significantly help increase salary. After reading such papers, I always go through the process of checking to see if there are any basic errors like the ones above. Unfortunately, it is not easy to find papers in Asia that pay such meticulous attention to variable selection and transformation.

Obtaining incorrect conclusions due to a lack of understanding of variable selection, separation, and purification does not only occur among Korean engineering graduates. While recruiting developers at Amazon, I once heard that the number of string lengths (bytes) of the code posted on Github, one of the platforms where developers often share code, was used as one of the variables. This is a good way to judge competency. Rather than saying it was a variable, I think it could be seen as a measure of how much more care was taken to present it well.

There are many cases where many engineering students claim that they simply copied and pasted code from similar cases they saw through Google searches and analyzed the data. However, there may be cases in the IT industry where there are no major problems if development is carried out in the same way. As in the case above, in areas where data transformation tailored to the research topic is essential, statistical knowledge at least at the undergraduate level is essential, so let's try to avoid cases where advanced data is collected and incorrect data analysis leads to incorrect conclusions.

The relationship between a commercial district and the concentration of consumers in a specific generation mostly is not by causal effect
Simultaneity oftern requires instrumental variables
Real cases also end up with mis-specification due to endogeneity

When working on data science-related projects, causality errors are common issues. There are quite a few cases where the variable thought to be the cause was actually the result, and conversely, the variable thought to be the result was the cause. In data science, this error is called ‘Simultaneity’. The first place where related research began was in econometrics, which is generally referred to as the three major data endogeneity errors along with loss of important data (Omitted Variable) and data inaccuracy (Measurement error).

As a real-life example, let me bring in a SIAI's MBA student's thesis . Based on the judgment that the commercial area in front of Hongik University in Korea would have attracted young people in their 2030s, the student hypothesized that by finding the main variables that attract young people, it would be possible to find the variables that make up the commercial area where young people gather. If the student's assumptions are reasonable, those who analyze commercial districts in the future will be able to easily borrow and use the model, and commercial district analysis can be used not only for those who want to open only small stores, but also for various areas such as promotional marketing of consumer goods companies, street marketing of credit card companies, etc.

Simultaneity error

However, unfortunately, it is not the commercial area in front of Hongdae that attracts young people in their 2030s, but a group of schools such as Hongik University and nearby Yonsei University, Ewha Womans University, and Sogang University that attract young people. In addition, the subway station one of the transportation hubs in Seoul. The commercial area in front of Hongdae, which was thought to be the cause, is actually the result, and young people in their 2030s, who were thought to be the result, may be the cause. In cases of such simultaneity, when using regression analysis or various non-linear regression models that have recently gained popularity (e.g. deep learning, tree models, etc.), it is likely that the simultaneity either exaggerates or under-estimates explanatory variables' influence.

The field of econometrics has long introduced the concept of ‘instrumental variable’ to solve such cases. It can be one of the data pre-processing tasks that removes problematic parts regardless of any of the three major data internal error situations, including parts where causal relationships are complex. Since the field of data science was recently created, it has been borrowing various methodologies from surrounding disciplines, but since its starting point is the economics field, it is an unfamiliar methodology to engineering majors.

In particular, people whose way of thinking is organized through natural science methodologies such as mathematics and statistics that require perfect accuracy are often criticized as 'fake variables', but the data in our reality has various errors and correlations. As such, it is an unavoidable calculation in research using real data.

From data preprocessing to instrumental variables

Returning to the commercial district in front of Hongik University, I asked the student "Can you find a variable that is directly related to the simultaneous variable (Revelance condition) but has no significant relationship (Orthogonality condition) with the other variable among the complex causal relationship between the two? One can find variables that have an impact on the growth of the commercial district in front of Hongdae but have no direct effect on the gathering of young people, or variables that have a direct impact on the gathering of young people but are not directly related to the commercial district in front of Hongdae.

First of all, the existence of nearby universities plays a decisive role in attracting young people in their 2030s. The easiest way to find out whether the existence of these universities was more helpful to the population of young people, but is not directly related to the commercial area in front of Hongdae, is to look at the youth density by removing each school one by one. Unfortunately, it is difficult to separate them individually. Rather, a more reasonable choice of instrumental variable would be to consider how the Hongdae commercial district would have functioned during the COVID-19 period when the number of students visiting the school area while studying non-face-to-face has plummeted.

In addition, it is also a good idea to compare the areas in front of Hongik University and Sinchon Station (one station to east, which is another symbol of hipster town) to distinguish the characteristics of stores that are components of a commercial district, despite having commonalities such as transportation hubs and high student crowds. As the general perception is that the commercial area in front of Hongdae is a place full of unique stores that cannot be found anywhere else, the number of unique stores can be used as a variable to separate complex causal relationships.

How does the actual calculation work?

The most frustrating part from engineers so far has been the calculation methods that involve inserting all the variables and entering all the data with blind faith that ‘artificial intelligence’ will automatically find the answer. Among them, there is a method called 'stepwise regression', which is a calculation method that repeats inserting and subtracting various variables. Despite warnings from the statistical community that it should be used with caution, many engineers without proper statistics education are unable to use it. Too often I have seen this calculation method used haphazardly and without thinking.

As pointed out above, when linear or non-linear series regression analysis is calculated without eliminating the 'error of simultaneity', which contains complex causal relationships, events in which the effects of variables are over/understated are bound to occur. In this case, data preprocessing must first be performed.

Data preprocessing using instrumental variables is called ‘2-Stage Least Square (2SLS)’ in the data science field. In the first step, complex causal relationships are removed and organized into simple causal relationships, and then in the second step, the general linear or non-linear regression analysis we know is performed.

In the first stage of removal, regression analysis is performed on variables used as explanatory variables using one or several instrumental variables selected above. Returning to the example of the commercial district in front of Hongik University above, young people are the explanatory variables we want to use, and variables related to nearby universities, which are likely to be related to young people but are not expected to be directly related to the commercial district in front of Hongik University, are used. will be. If you perform a regression analysis by dividing the relationship between the number of young people and universities before and after the COVID-19 pandemic period as 0 and 1, you can extract only the part of the young people that is explained by universities. If the variables extracted in this way are used, the relationship between the commercial area in front of Hongdae and young peoplecan be identified through a simple causal relationship rather than the complex causal relationship above.

Failure cases of actual companies in the field

Since there is no actual data, it is difficult to make a short-sighted opinion, but looking at the cases of 'error of simultaneity' that we have encountered so far, if all the data were simply inserted without 2SLS work and linear or non-linear regression analysis was calculated, the area in front of Hongdae is because there are many young people. A great deal of weight is placed on the simple conclusion that the commercial district has expanded, and other than for young people, monthly rent in nearby residential and commercial areas, the presence or absence of unique stores, accessibility near subway and bus stops, etc. will be found to be largely insignificant values. This is because the complex interaction between the two took away the explanatory power that should have been assigned to other variables.

There are cases where many engineering students who have not received proper education in Korea claim that it is a 'conclusion found by artificial intelligence' by relying on tree models and deep learning from the perspective of 'step analysis', which inserts multiple variables at intersections, but there is an explanation structure between variables. There is only a difference in whether it is linear or non-linear, and therefore the explanatory power of the variable is partially modified, but the conclusion is still the same.

The above case is actually perfectly consistent with the mistake made when a credit card company and a telecommunications company jointly analyzed the commercial district in the Mapo-gu area. An official who participated in the study used the expression, 'Collecting young people is the answer,' but then as expected, there was no understanding of the need to use 'instrumental variables'. He simply thought data pre-processing as nothing more than dis-regarding missing data.

In fact, the elements that make up not only Hongdae but also major commercial districts in Seoul are very complex. The reason why young people gather is mostly because the complex components of the commercial district have created an attractive result that attracts people, but it is difficult to find the answer through simple ‘artificial intelligence calculations’ like the above. When trying to point out errors in the data analysis work currently being done in the market, I simply chose 'error of simultaneity', but it also included errors caused by missing important variables (Omitted Variable Bias) and inaccuracies in collected variable data (Attenuation bias by measurement error). It requires quite advanced modeling work that requires complex consideration of such factors.

We hope that students who are receiving incorrect machine learning, deep learning, and artificial intelligence education will learn the above concepts and be able to do rational and systematic modeling.