MMU: how does it work? And why as the AI-induced massive unemployments are coming

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MMU (mini mini USL): how and why?

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Basic Highlight: Although MMU or USL sound like just education programs, they are far more than EDU programs. MMU is designed to radically reduce the massive unemployments due to the AI-induced automations. So, MMU is equally relevant for the Ministries/Departments of Economy, of Finance, of Labor or work, of the national security of virtually every country except perhaps the top 5-7 math countries in the world. Over the next 3-8 years, this global crisis will hit the western OECD countries first. Within several years, the massive unemployment rates will hit the richer cities or states of the less developed or less rich countries, e.g. northern states or Mexico City in the case of Mexico. Most of the western OECD governments try hard to reskill their workers (as some researches suggest that about 50% of workers in the developed countries need the reskilling. Unfortunately, the people who have the math poverty cannot be reskilled easily, meaning that they become basically unemployable. As such, they will require the massive financial or educational resources and will take exceedingly long time, not just several months. This is where the MMU operations can bring the miracles.

 

For the big picture as to why your governments (with or without South Korean government), NGOs, and enterprises, should embrace the initial phase of the MMU and SEF as soon as possible (as Lee plans to create 4+ new Korean waves that will be far larger than anything that are currently existing, including the BTS, K dramas, K movies, etc. Click here.

 

I will give you the glimpse of stagnating math poverties of the richer half about 10 Latin American countries (1 Caribbean included) with their 15 year track records. Please don’t lie to yourself and your president and citizens. It is the time to be humble, to accept the brutal reality so that we can overcome together. I put the math poverty fluctuations of Spain which has been around 25% of their student population for decades. And Spain’s math poverty is slightly below the western OECD average. They are about to face the hellish scenarios this decade without the MMU interventions.

If you really want to know the global math poverty percent share stagnations, I have compiled most of the available data.

For our initial MMU operation targets, check out the links here to get the ideas. This is the real pandemic, equal or even far worse than the COVID pandemic by the end of this decade if you are still sleeeping.

Question: for whom are the MMU series?

My answers with a few big map for the leaders of the countries and NGOs.

• Ministry of Work or Labor: because the AI-induced massive job destructions are inevitable. Imagine that in your countries (or at least in the richer quarter or half of your countries), 20-30% of all jobs (averaging around 25% of all) will be gone by around 2030. This is shocking, considering the fact that even at the brief peak of the 2020 COVID19 pandemic year, the global unemployment rate average was about 25% (including the typical national 5-10% unemployment rates). For the typical OECD countries have the unemployment rates of about 5-7%. Imagine that this becomes 30-35% by the end of this decade.
• Ministry of Education: And the current reskilling schemes from most of the Ministries of the OECD countries will fail as their math poverty reductions have failed for decades in most countries. Historically speaking, the vast majority of them not only fail, but also impressively and miserably fail. It is the time to stop the wishful thinking. Math education should be scientific, not a religious dogmas of methodologies that have proven to fail in the vast majority of countries, including the vast majority of the OECD countries.
• Ministry of Economy: when in the western OECD countries the new 20-30% unemployments arrive on top of the typical 5-7% unemployments by late this decade, their total unemployment rates will become 30-35% as a whole, almost 1 out of 3 workers. For the average income countries – including the top 5 richest in the Latin America (e.g. Mexico, Costa Rica, Uruguay, Chile), by the end of this decade, their richest cities will have the similar unemployments because their top cities have GDP per capita slightly lower than the western OECD’s income per capita average. Honestly ask yourself how the best economists in your country will handle. They can’t. Their whole economy will basically collapse unless they minimize the math poverty over the next 3-7 years.
• Ministry of Foreign Relations: as Lee is trying to make a few new Korean waves and one of them is to end the global math poverty by collaborating with the cities, states, national governments as well as  the international NGOs and the UN (via UNESCO), it is the urgent time to collaborate together instead of getting bogged down to the status quo that goes nowhere.
• And your presidents or prime ministers of your countries together with all the 4 ministers of the above-mentioned ministries: After the 2030, the situations will still get worse every year. My first phase MMU opearations are to minimize the giga unemployment crises by ending the math poverty for the alliance countries as much as possible. If the ministers of the education refuse to collaborate with those of economy, of work, and of the foreign relations together strongly with their presidents, the future will be very ugly. And the time bombs are ticking everywhere.

About the AI-induced automations destroying 20-30% of all jobs (while in these countries, their math poverty percent shares are 20-30% overall) in the western OECDs between 2025-2030 (about AI’s killing jobs starting 2 minutes 34 seconds)

The key summary: for the vast majority of countries with the population size of at least several millions, the  math poverty percent shares of the countries have been quasi-flat almost universally at least for the past 10 years. For many others, the math average growth have been flat for the past 15 or even 20 years. The AI-induced job killing is arriving soon, starting at the richer countries first and will start killing the conventional jobs. Between 2025 and 2030, this was estimated to be 20-30% of the current jobs, which is also the size of the math poverty in most of the western OECD countries and other richer countries EXCEPT the top 5-7 math countries in North Eastern Asia where their math poverty is 5-10-15% usually. With the conventional wisdom and operations of the governments, for the MOEs, DOEs in most developed or richer OECD countries, the global data show that they cannot reduce their math poverty percent shares from about 25% average to below 10% within next 5-8 years by the time AI kills many manual jobs, let alone in 15-25 years even if they are super-lucky as a few decades will be still failing for the vast majority of the MOEs and DOEs around the world. Historically speaking, for the vast majority of the OECD level countries except Far Eastern Asian countries, most OECD countries may need even 40-100+ years to reduce their math poverty percent share to 5-10% of the population.

So, how can we make most of the western OECD countries or developed countries survive and overcome the pending destructions of the manual jobs before the end of this decade as they have at most 7-8 years so far? Lee’s solution first is MMU series. 1) to end the math poverty – be it 25% or 50% or 70% of the population over the next 3-7 years time frame, NOT over the next 20-50-100 years. 2) Once the quasi-ending of the math poverty is achieved, after 2030, AI will continue killing jobs much higher rates of 30-40-50% by 2035 or 2040. So, when the needs come, the upgrading to 30-50% of the population of the OECD level countries can take place too. But the first comes first; we need to end the math poverty in your countries as soon as possible and as early as possible. If you look at the PISA math data, you will begin to see that your hopes to quickly reduce the math poverty in your countries seem to be nothing more than the collective wishful thinking? There have been only few minor exceptions in the western Europe over the past10 to 20 years. So, Lee is proposing a few different proposals of MMU series for the governments of interest.  To reduce their math poverty to below 5-10% of the student population at least before the AI destructions of jobs arrive in your countries. Please watch this chart below with your own eyes. Compare the final results of the ministries of education in virtually any country with the math poverty percent shares that are almost horizontal vs. the MMU to make the reduction very steep in just 3-6 years.

Notice that the math poverty reductions of the student percent shares are barely changing in most cases vs. the MMU 0.5-0.5’s steep math poverty reductions over the next 3-6 years.

• For the visual data of the math stagnation nations, please try this (Click here).
• for the economic impacts by MMU, try this.
• Original USL evidences
• Informercial videos below.

MMU proposal for the Ministries of Education, of Economy, of Labor, and the governments around the world:

Original MMU1 summaries, the first 3 minutes 40 seconds of the video is relevant for any country as it includes the short summary and evidences).

The key motivations as to why I try to make MMU empower the countries that embrace MMU operations?

Below is a bit more detailed explanations as to what, how, and why for MMU series for your country.

1. The automations and the job killing and their arrivals between 2025 and 2030 for the developed countries first:
   1. The AI-induced automations are supposed to kill 20-30% of the current jobs between 2025-2030, starting with the developed countries such as the original 30 OECD countries.
   2. Even for the less developed countries, for their main cities where their local GDP per capita are compatible to the western OECD countries, the automations may start killing their jobs soon.
   3. According to some reports, by around 2025 or 2030, the 50% of the jobs require significant retraining.
   4. After 2030 when 20-30% of people are expected to lose their current jobs or at least to require the reskilling, as time goes by in 2035-2040+, the chance to kill the jobs will increase to 30-40-50%+ although most people are not aware.
   5. For the OECD level developed countries, there is an intriguing coincidence: their expected job losses are about 20-30% of all jobs vs. their math poverty (equivalent to about PISA math 420 or TIMSS math 450) is about 20-30% as well. NOT A COINCIDENCE.
   6. When all the companies have to reskill their employees, if they don’t even have the basic math skills to overcome their math poverty, this author is exceedingly skeptical about the efficiency of the job reskilling, which mostly will fail in the long run.
2. There have been persistent math growth stagnations in the vast majority of the OECD countries for the past 10-25 years.
3. The brutal reality of taking too long, too much efforts, and too much money to make significant progress in math learning at the national levels.
4. I firmly believe that for the Ministries of Education to advance their national math average, they MUST reduce their math poverty first. (I have prepared to release a series of journal articles with regard to this issue and they are coming soon.) Otherwise, their entire efforts will be useless in the short term and in the long term. As my original USL (Unified Super Learning) was too good to be true and was systematically ignored by the educational establishments, I focus on MMU (mini mini USL) instead of the original USL. Still MMU can change the future of math education of most countries 10-20 times faster than what they can achieve without MMU series.
5. Even for the math improving countries, the changes have been mostly have been very slow. I shared the visual data in this web page. For instance, to reduce the math poverty from 50-60% to 20-30% normally take 40-80-100+ years in the vast majority of countries. The main motivation of MMU series is to super-quickly reduce the math poverty. For the visual evidences and data for the math poverty percent shares of these regions can be found at the following links(Click).
   1. MMU for the western OECD countries (where the math poverty is 20-30%): MMU will reduce the math poverty from the average of about 25% from these countries to about 5-10% (which is the math poverty level of the top 5 math countries for TIMSS or PISA for the past 10-20 years). The vast majority of the western OECD countries have failed to reach there at least 90-95% failure rates.
   2. MMU for the 5-7 oil-richest Persian gulf countries: to reduce their current math poverty of 45-75% (averaging about 60%) to 20-30% or less (at the western OECD’ math poverty or less) in just several years, not 30-50-100 years.
   3. MMU for the mid-tier countries where their math poverty’s population share is 50%+ to below 20% if their government can commit for the MMU.
   4. MMU for the Latin American countries (where their math poverty mostly ranges between 45% to 90%): to reduce this much (averaging around 70-75% of the student population in Latin America) to the math poverty level of less than 30-50%. Say from the 70% average to below 30-40% over the next 5-10 years.
   5. MMU for the poor regions such as Sub-Sahara Africa or South Asia (where their math poverty percent shares are typically estimated to be 80-97% of the student population): it is urgent for them to reduce their math poverty below 30-40%. They have no chance to reduce their math poverty that low within the next 10-20 years even if they get very lucky. For the poor regions, Lee seeks to supports and collaborations with the UN, education NGOs, and other like-minded people to minimize the economic and job crises from 2030 in.
   6. MMU for the Caribbeans: there are only 2 countries from Caribbeans that have participated in either PISA or TIMSS. They were Puerto Rico and Dominican Republic. Dominican Republic’s math poverty % share was about 90% twice in PISA. Puerto Rico’s math poverty (based on its PISA and NAEP tests) seems to be between 70-90% (averaging about 80%). For almost all Caribbean countries that have participated in CXC tests such as CSEC’s general math, their current pass rates have been 30-60%, averaging about 40-45%. The pass rates of CSEC math have improved very slowly. Although their math pass rates regionally from CXC seems to be 40-45%, if most of these countries take PISA or TIMSS math, I am almost sure that their math poverty percent shares will be not 50-60%, but 70-90%. Due to the lack of the PISA or TIMSS equivalent math data, for now, I intend to make their math pass rates from the current about 40% to 70-80% over the next several years with the MMU operations.
   7. MMU’s manner of operations: we intend to make collaborate with both the public and the private sectors. The main focus is the efficiency to minimize the bureaucracy and the status quo in terms of the wasted efforts and money.

The high correlations between the TIMSS math grade 4th maths vs the grade 8th maths.

What happen if the math worst 5-10% of students quickly rise to the level of the average math 25th percentile? This is extremely relevant for the western OECD countries because their national math poverty average is about 20-30% of their student population for the past 10-20+ years. The first round MMU is to achieve this much change quickly. For the charts below, notice that hyper strong correlations between the math 25th percentile and the 5th percentile (which is extremely relevant for the western OECD countries as their math poverty % share is about 25%). For the right chart below, you can see another super strong correlations between the math 50th percentile and the 25th percentile , which is super relevant for the top math oil-richest countries (e.g. UAE, Qatar, Bahrain), and the top 5 richest Latin American countries (e.g. Uruguay, Chile, Mexico, Costa Rica, Trinadad & Tobago). Aside from these, we can similarly apply for all the typically math average, GDP per capita average countries around the world. Regardless, my focus is to quasi-end their math poverty this decade before the hells break through. It is up to you to invite the MMU operations as soon as possible before it is too late.

The economic impacts of MMU series for more details, click here. Over the past 15+ years, the traditional human capital definitions using the mean schooling years has been gradually replaced by the knowledge capital which is critically based on the cognitive skills of the nations, especially math and science. When the national math poverty percent shares are high, the future economic outlooks of the countries may be grim except perhaps the oil-richest countries for now and perhaps the next a decade, but not 15 or 20 years.

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