We have finally entered the era where we continually deepen our understanding that language is less art and more science. Yet in this modern age of nearly endless access to relevant information, our approach to education and learning is something of the 19th century industrial revolution. So we started by asking why and researching who has it been affecting the most? This then allowed us to start thinking what-if?
We started by collecting some facts (ref. sources):
UN and IMF figures, the United States has the largest GDP in the world at $20.4 trillion (IMF) and $18.6 trillion (UN).
US has a population of 327 million, while China's population is the highest in the world – a massive 1.42 billion (although despite the significant difference, the US also has the third-highest population in the world, behind India in second place with 1.35 billion.
$706 billion, or $13,847 per public school student (aka. per capita) is spent on US education annually ([US] National Assessment of Educational Progress (NCES) fast facts)
The US is consistently out performed in reading, literacy, math, and science by the countries of Japan, Republic of Korea, the Russian Federation, and Singapore (NCES facts on NAEP and TIMSS)
Singapore is consistently the top performing, English speaking country, in reading, literacy, math, and science with 514 thousand primary and second school students, and a national budget of $12.8 billion, or a $24,902 per capita which is 79.8% greater than the US (NCES facts on NAEP and TIMSS)
We are falling behind from the beginning in education of ourselves, the US NAEP reading assessment (literacy) on a scale from 0 - 500 by 4th grade results are: (239) Asian/Pacific Islanders,(232) Whites, (209) Hispanics, (206) Blacks, (202) American Indian/Alaska Native. Note the benchmarks of NAEP are Proficient score of (238) and an Advanced score of (268) (NCES fast facts on NAEP).
We then asked what-if?
Language was less art and more science? Advancements in neuroscience can explain how the brain works and how we learn in various stages of our human development, yet this knowledge is being underutilized in how we restructure education.
Learning was especially tailored to each of us, but on a set of common definitions for achievement? Our advancements in human linguistics, research and development of the foundation and tiers for learning language, and technology growth low-cost scaleable resources like machine learning and artificial intelligence (AI) make for new ingredients in solving this solution recipe.
Machine learning and AI could be used as our teachers instead of our replacements? The days of a machine, or set of code, assisting us to understand, providing us insights into problems, proficiency, and mastery are TODAY.
We can learn how to learn by incrementally assessing one another's progress versus solely analyzing data on assessment? Our US educational approach is learn by assessment, while the world is advancing with learn by doing, measure proficiency and mastery, and continually repeat the process. We believe the time is now to start rapidly innovating in the way we educate ourselves.
We better understood where the single student, small groups of students, and classroom as a whole are struggling, proficient, and have gained mastery? If we measure and collect data on performance indicators, assessments like a quiz or test become obsolete and give back available time for instruction, small group, and peer-to-peer advancement during formal teaching sessions.
Then got to work assembling the most cutting-edge solution conceivable based on the following vision principles:
As personified by the [US] Scripps National Spelling Bee, rote memorization of words is a first step in achieving the recognition in language before deeper levels of definition and contextual meaning. Language is our foundation we build understanding of concepts upon creating one's knowledge, abilities, and potential for contributions and achievement.
Inspired by an alternate parallel to Kurt Vonnegut's Harrison Bergeron, we believe the year 2081 is a Utopian-society in which all people benefit from machine learning, deep learning, and AI in a manner that brings forth heightened levels of achievement, ability, and productivity because the machines and code are assisting us, enhancing us, and working for our greater benefit and prosperity.
A one-for-one (1:1) mission - we believe that it is our personal, social, and corporate responsibility to solve problems for a customer, then pay it forward by assisting a less fortunate individual with a similar problem but without the means to affording their solution.
And the tech stuff.
Our system and solution must be hyper-scale from day ONE and not require a linear employee-to-customer count relationship
We must have a 100% cultural commitment to achieving the best customer journey and keeping it simple
Innovate from the trends of tomorrow to create an ever-standing solution in the fast pace progressions of technology
Our solution must provide insights, reports, and predictions on its interactions and measure against a benchmark of progress
Our system must learn, improve, repeat in a never ending manner amassing a data store of key performance indicators
Users must have affordable access to self-help, do-it-yourself (DIY), and educational training that should be common marketplace