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

On SIAI's website, we can see most wannabe students go to MSc AI/Data Science program intro page and almost never visit MBA AI program pages. We have a shorter track for MSc that requires extensive pre-study, and much longer version that covers missing pre-studies. Over 90% of wannabes just take a quick scan on the shorter version and walk away. Less than 10% to the longer version, and almost nobody to the AI MBA.

We get that they are 'wannabe' data scientists with passion, motivation, and dream with self-confidence that they are the top 1%. But the reality is harsh. So far, less than 5% applicants have been able to pass the admission exam to MSc AI/Data Science's longer version. Almost never we have applicants who are ready to do the shorter one. Most, in fact, almost all students should compromise their dream and accept the reality. The fact that the admision exam is the first two courses of the AI MBA, lowest tier program, already bring students to senses that over a half of applicants usually disappear before and after the exam. Some students choose to retake the exam in the following year, but mostly end up with the same score. Then, they either criticize the school in very creative ways or walk away with frustrated faces. I am sorry for keeping such high integrity of the school.

Data Scientific Intuition that matters the most

The school focuses on two things in its education. First, we want students to understand the thought processes of data science modelers. Support Vector Machine (SVM), for example, reflects the idea that fitting can be more generalized if a separating hyperplane is bounded with inequalities, instead of fixed conditions. If one can understand that the hyperplane itself is already a generalization, it can be much easier to see through why SVM was introduced as an alternative to linear form fitting and what are the applicable cases in real life data science exercises. The very nature of this process is embedded in the school's motto, 'Rerum Cognoscere Causas' ((Felix, qui potuit rerum cognoscere causas - Wikipedia)), meaning a person pursuing the fundamental causes.

The second focus of the school is to help students where and how to apply data science tools to solve real life puzzles. We call this process as the building data scientific instuition. Often, math equations in the textbooks and code lines in one's program console screens do not have any meaning, unless it is combined in a way to solve a particular problem in a peculiar context with a specific object. Unlike many amateur data scientists' belief, coding libraries have not democratized data science to untrained students. In fact, the codes copied by the amateurs are evident examples of rookie failures that data science tools need must deeper background knowledge in statistics than simple code libraries.

Our admission exam is designed to weed out the dreamers or amateurs. After years of trials and errors, we have decided to give a full lecture of elementary math/stat course to all applicants so that we can not only offer them a fair chance but also give them a warning as realistic as our coursework. Previous schooling from other schools may help them, but the exam help us to see if one has potential to develop 'Rerum Cognoscere Causas' and data scientific intuition.

Intution does not come from hard study alone

When I first raised my voice for the importance of data scientific intution, I had had severe conflicts with amateur engineers. They thought copying one's code lines from a class (or a github page) and applying it to other places will make them as good as high paid data scientists. They thought these are nothing more than programming for websites, apps, and/or any other basic programming exercises. These amateurs never understand why you need to do 2nd-stage-least-square (2SLS) regression to remove measurement error effects for a particular data set in a specific time range, just as an example. They just load data from SQL server, add it to code library, and change input variables, time ranges, and computer resources, hoping that one combination out of many can help them to find what their bosses want (or what they can claim they did something cool). Without understanding the nature of data process, which we call 'data generating process' (DGP), their trials and errors are nothing more than higher correlation hunting like untrained sociologists do in their junk researches.

Instead of blaming one code library worse performing than other ones, true data scientists look for embedded DGP and try to build a model following intuitive logic. Every step of the model requires concreate arguments reflecting how the data was constructed and sometimes require data cleaning by variable re-structuring, carving out endogeneity with 2SLS, and/or countless model revisions.

It has been witnessed by years of education that we can help students to memorize all the necessary steps for each textbook case, but not that many students were able to extend the understanding to ones own research. In fact, the potential is well visible in the admission exam or in the early stage of the coursework. Promising students always ask why and what if. Why SVM's functional shape has $1/C$ which may limit the range of $C$ in his/her model, and what if his/her data sets with zero truncation ends up with close to 0 separating hyperplane? Once the student can see how to match equations with real cases, they can upgrade imaginative thought processes to model building logic. For other students, I am sorry but I cannot recall successful students without that ability. High grades in simple memory tests can convince us that they study hard, but lack of intuition make them no better than a textbook. With the experience, we design all our exams to measure how intuitive students are.

Intuition that frees a data scientist

In my Machine Learning class for tree models, I always emphasize that a variable with multiple disconnected effective ranges in trees has a different spanned space from linear/non-linear regressions. One variable that is important in a tree space, for example, may not display strong tendency in linear vector spaces. A drug that is only effective to certain age/gender groups (say 5~15, 60~ for male, 20~45 female) can be a good example. Linear regression hardly will capture the same efffective range. After the class, most students understand that relying on Variable Importances of tree models may conflict with p-value type variable selections in regression-based models. But only students with intuition find a way to combine both models that they find the effective range of variables from the tree and redesign the regression model with 0/1 signal variables to separate the effective range.

The extend of these types of thought process is hardly visible from ordinary and disqualified students. Ordinary ones may have capacity to discern what is good, but they often have hard time to apply new findings to one's own. Disqualified students do not even see why that was a neat trick to the better exploitation of DGP.

What's surprising is that previous math/stat education mattered the least. It was more about how logical they are, how hard-working they are, and how intuitive they are. Many students come with the first two, but hardly the third. We help them to build the third muscle, while strenghtening the first. (No one but you can help the second.)

The re-trying students ending up with the same grades in the admission exam are largely because they fail to embody the intuition. It may take years to develop the third muscle. Some students are smart enough to see the value of intuition almost right away. Others may never find that. For failing students, as much as we feel sorry for them, we think that their undergraduate education did not help them to build the muscle, and they were unable to build it by themselves.

The less chanllenging tier programs are designed in a way to help the unlucky ones, if they want to make up the missing pieces from their undergraduate coursework. Blue pills only make you live in fake reality. We just hope our red pill to help you find the bitter but rewarding reality.

AI talks turned the table and become more pessimistic
It is just another correction of exorbitant optimism and realisation of AI's current capabilities
AI can only help us to replace jobs in low noise data
Jobs needing to find new patterns and from high noise data industry, mostly paid more, will not be replaceable by current AI

There have been pessimistic talks about the future of AI recently that have created sudden drops in BigTech firms' stock prices. In all of a sudden, all pessimistic talks from Investors, experts, and academics in reputed institutions are re-visited and re-evaluated. They claim that ROI (Return on Investment) for AI is too low, AI products are too over-priced, and economic impact by AI is minimal. In fact, many of us have raised our voices for years with the exactly same warnings. 'AI is not a magic wand'. 'It is just correlation but not causality / intelligence'. 'Don't be overly enthusiastic about what a simple automation algorithm can do'.

As an institution with AI in our name, we often receive emails from a bunch of 'dreamers' that they wonder if we can make a predictive algorithm that can foretell stock price movements with 99.99% accuracy. If we could do that, why do you think we would share the algorithm with you? We should probably keep it for secret and make billions of dollars just for ourselves. As much as the famous expression by Milton Friedman, a Nobel economist, there is no such thing as a free lunch. If we have a perfect predictability and it is widely public, then the prediction is no longer a prediction. If everyone knows the stock A's price goes up, then everyone would buy the stock A, until it reaches to the predicted value. Knowing that, the price will jump to the predicted value, almost instantly. In other words, the future becomes today, and no one gets benefited.

AI = God? AI = A machine for pattern matching

A lot of enthusiasts have exorbitant optimism that AI can overwhelm human cognitivie capacity and soon become god-like feature. Well, the current forms of AI, be it Machine Learning, Deep Learning, and Generative AI, are no more than a machine for pattern matching. You touch a hot pot, you get a burn. It is painful experience, but you learn that you should not touch when it is hot. The worse the pain, the more careful you become. Hopefully it does not make your skin irrecoverable. The exact same pattern works for what they call AI. If you apply the learning processes dynamically, that's where Generative AI comes. The system is constantly adding more patterns into the database.

Though the extensive size of patterns does have great potential, it does not mean that the machine has cognitive capacity to understand the pattern's causality and/or to find new breakthrough patterns from list of patterns in the database. As long as it is nothing more than a pattern matching system, it never will.

To give you an example, can it be used what words you are expected to answer in a class that has been repeated for thousand times? Definitely. Then, can you use the same machine to predict the stock price? Aren't the stock market repeating the same behavior over a century? Well, unfortunately it is not, thus you can't be benefited by the same machine for financial investments.

Two types of data - Low noise vs. High noise

On and near the Wall Street, you can sometimes meet an excessively confident hedge fund manager with claims on near perfect foresight for financial market movements. Some of them have outstanding track records, and surprisingly persuasive. In New York Times archive back in 1940s, or even as early as 1910s, you can see people with similar claims were eventually sued by investors, arrested due to false claims, and/or just disappeared from the street within a few years. If they were that good, why then they lost money and got sued/arrested?

There are two types of data. One set of data that you can see from machine (or highly controlled environment) is called 'Low-noise' data. It has high predictability. Even in cases where embedded patterns are invisible by bare eyes, you either need more analytic brain or a machine to test all possibilities within the possible sets. For the game of Go, the brain was Se-dol Lee and the machine was Alpha-Go. The game needs to test 19x19 possible sets with around 300 possible steps. Even if your brain is not as good as Se-dol Lee, as long as your computer can find the winning patterns, you can win. This is what has been witnessed.

The other set of data comes from largely uncontrolled environment. There potentially is a pattern, but it is not the single impetus that drives every motion of the space. There are thousands, if not millions, of patterns that the driver is not observable. This is where randomness is needed for modeling, and it is unfortunately impossible to predict accurate move, because the driver is not observable. We call this set of data 'High-noise'. The stock market is the very example of such. There are millions of unknown, unexpectable, and at least unmeasurable influences that disable any analyst or machine to predict with accuracy level upto 100%. This is why financial models are not researched for predictability but used only to backtest financial derivatives for reasonable pricing.

Natural language process (NLP) is one example of low noise. Our language follows a certain set of rules (or patterns), which are called grammar. Unless you are uneducated or intentionally out of grammar (or make mistakes), people generally follow grammar. Weather is mostly low noise, but it has high noise components. Sometimes typhoons are unpredictable, or less predictable. Stock market? Be my guest. There have been 4 Nobel Prizes given to financial economists by year 2023, and all of them are based on the belief that stock markets follow random processes, be it Gaussian, Poisson, and/or any other unknown random distributions. (Just in case, if a process follows any known distribution, that means it is probabilistic, which means it is random.)

Potential benefits of AI

We as an institution hardly believe current forms of AI will make any significant changes in businesses and our life in short term. The best we can expect is automation of mundane tasks. Like laundary machine in early 20th century. ChatGPT already has shown us a path. Soon, CS operators will largely be replaced by LLM based chatbots. US companies actively outsourced the function from India for the past a few decades, thanks to cheaper international connectivity via internet. It will still remain, but human actions will be needed way less than before. In fact, we already get machine generated answers from a number of international services. If we complain about a program's malfunction on a WordPress plugin, for instance, established services email us machine answers first. For a few cases, it actually is enough. The practice will become more popular to less-established services as it becomes easier and cheaper to implement.

Teamed up with EduTimes, we also are working on a research to replace 'Copy Boys/Girls'. Journalists that we know from large news magazines are not always running on the street to find new and fascinating stories. In fact, most of them read other newspapers and rewrite the contents as if they were the original sources. Although it is not an important job, it is still needed for the newspaper to run. They need to keep up the current events, accoring to the EduTimes journalists from other renouned newspapers. The copy team is usually paid the least and seen a death sentence as a journalist. What makes the job more sympathetic on top of the least respect, it will soon be replaced by LLM based copywriters.

In fact, any job that generates patterned contents without much of cognitivie functions will gradually be replaced.

What about automotive driving? Is it a low-noise pattern job or a high-noise complicated cognitive job? Well, although Elon Musk claims high possibility of Lv. 4 auto-driving within next a few years, we don't believe so. None of us at GIAI have seen any game theorists have solved multi-agent ($n$>2) Bayesian belief game with imperfect information and unknown agent types by computer so that the automotive driving algorithm can predict what other drivers on the road will do. Without the right prediction of others on the fast moving vehicles, it is hard to tell if your AI will help you successfully avoid other crazy drivers. The driving job for those eventful cases needs 'instinct', which requires another set of bodily function different from cognitive intelligence. The best that the current algorithm can do is to tighten it up to perfection for a single car, which already needs to go over a lot of mathematical, mechanical, organisational, legal, and commercial (and many more) challenges.

Don't they know all that? Aren't the Wall Street investors self-confident, egocentric, but ultra smart that they already know all the limitations of AI? We believe so. At least we hope so. Then, why do they pay attention to the discontentful pessimism now, and create heavy drops in tech stock prices?

Guess the Wall Street hates to see Silicon Valley to be paid too much. American East often think the West too unrealistic and floating in the air. OpenAI's next round funding may surprise us in a totally opposite direction.

Top brains in AI/Data Science are driven to challenging jobs like modeling
Seldom a 2nd-tier company, with countless malpractices, can meet the expectations
Even with $$$, still they soon are forced out of AI game

A few years ago, a large Asian conglomerate acquired a Silicon Valley's start-up just off an early Series A funding. Let's say it is start-up $\alpha$. The M&A team leader later told me that the acquisition was mostly to hire the data scientist in the early stage start-up, but the guy left $\alpha$ on the day the M&A deal was announced.

I had an occation to sit down with the data scientist a few months later, and asked him why. He tried to avoide the conversation, but it was clear that the changing circumstances definitely were not within his expectation. Unlike other bunch of junior data scientists in Silicon Valley's large firms, he did signal me his grad school training in math and stat that I had a pleasant half an hour talk about models. He was mal-treated in large firms that he was given to run SQL queries and build Tableau-based graphes, like other juniors. His PhD training was useless in large firms, so he had decided to be a founding member of $\alpha$ that he can build models and test them with live data. The Asian acquirer with bureaucratic HR system wanted him to give up his agenda and to transplant the Silicon Valley large firm's junior data scientist training system to the acquirer firm.

Brains go for brains

Given tons of other available positions, he didn't waste his time. Personall,y I also have lost some months of my life for mere SQL queries and fancy graphes. Well, some people may still go for 'data scientist' title, but I am my own man. So was the data scientist from $\alpha$.

These days, Silicon Valley firms call the modelers as 'research scientists', or simliar names. There also are positions called 'machine learning engineers' whose jobs somewhat related to 'research scientists', but may disinclude mathematical modeling parts and way more software engineering in it. The title 'Data Scientists' are now given to jobs that were used to be called 'SQL monkeys'. As the old nickname suggests, not that many trained scientists would love to do the job, even with competitive salary package.

What companies have to understand is that we, research scientists, are not trained for SQL and Tableau, but mathematical modeling. It's like a hard-trained sushi cook(将太の寿司, shota no sushi) is given to make street food like Chinese noodle.

Let me give you an example in real corporate world. Let's say a semi-conductor company, $\beta$ wants to build a test model for a wafer / subsctrate. What I often hear from those companeis are that they build a CNN model that reads the wafer's image and match it with pre-labeled 0/1 for error detection. In fact, similar practices have been widely adapted practice among all Neural Network maniacs. I am not saying it does not work. It works. But then, what would you do, if the pre-label was done poorly? Say, the 0/1 entries were like over 10,000 and hardly any body double checked the accruracy. Can you rely on that CNN-based model? In addition to that, the model probably require enourmous amount of computational costs to build, let alone test and operating it daily.

Wrong practice that drives out brain

Instead of the costly and less scientific option, we can always build a model that captures data's generated process(DGP). The wafer is composed of $n \times k$ entries, and issues emerge when $n \times 1$ or $1 \times k$ entries go wrong altogether. Given the domain knowledge, one can build a model with cross-products between entries in the same row/column. If it is continuously 1 (assume 1 for error), then it can easily be identified as a defect case.

Cost of building a model like that? It just needs your brain. There is a good chance that you don't even need a dedicated graphics card for that calculation. Maintenance costs are also incomparably smaller than the CNN version. The concept of computational cst is something that you were supposed to learn in any scientific programming classes at school.

For companies sticking to the expensive CNN options, I always can spot followings:

• The management has little to no sense of 'computational cost'
• The manaement cannnot discern 'research scientists' and 'machine learning engineers'
• The company is full of engineers without the sense of mathematical modeling

If you want to grow up as a 'research scientist', just like the guy at $\alpha$, then run. If you are smart enough, you must have already run, like the guy at $\alpha$. After all, this is why many 2nd-tier firms end up with CNN maniacs like $\beta$. Most 2nd-tier firms are unlucky that they cannot keep research scientists due to lack of knowledge and experience. Those companies have to spend years of time and millions of wasted dollars to find that they were so long. By the time that they come to senses, it is mostly already way too late. If you are good enough, don't waste your time on a sinking ship. The management needs so-called cold-turkey type shock treatment as a solution. In fact, there was a start-up that I stayed only for a week, which lost at least one data scientist in everyweek. The company went to bankrupt in 2 years.

What to do and not to do

At SIAI, I place Scientific Programming right after elementary math/stat training. Students see that each calculation method is an invention to overcome earlier available options' limitations but simultanesouly the modification bounds the new tactic in another directions. Neural Networks are just one of the many kinds. Even with the eye-opening experience, some students still remain NN maniacs, and they flunk in Machine Learning and Deep Learning classes. Those students believe that there must exist a grand model that is univerally superior to all other models. I wish the world is that simple, but my ML and DL courses break the very belief. Those who are awaken, usually become excellent data/research scientists. Many of them come back to me that they were able to minimize computational costs by 90% just by replacing blindly implemented Neural Network models.

Once they see that dramatic cost reduction, at least some people understand that the earlier practice was wrong. The smarty student may not be happy to suffer from poor management and NN maniacs for long. Just like the guy at $\alpha$, it is always easier to change your job than fighting to change your incapable management. Managers moving fast maybe able to withhold the smarty. If not, you are just like the $\beta$. You invest a big chunk of money for an M&A just to hire a smarty, but the smarty disappears.

So, if you want to keep the smarty? Your solution is dead simple. Test math/stat training levels in scientific programming. You will save tons of $$$ in graphic card purchase.