How and Why We Started
For those who have been asking, here’s a bit about our background, where we’re headed (our “master plan”), and just a bit of why we are doing what we’re doing.
Diagknowstics started off rather humbly, in a 300 sq foot office, focused on using and improving our teaching methods through in person tutoring. We also began developing our online platform with a beta subject — Microeconomics, targeted toward the AP and early College level students. With a positive response through two semesters of data, we began improving the online platform and expanding to other subjects.
The future of Diagknowstics
Our master plan is fairly simple, and comes in three stages:
1. Generate revenue through in person tutoring and selected subject platforms.
2. Use that revenue and experience to build better AI into our platforms and improve learning methods, while supplementing the online experience with in-person tutoring as necessary.
3. Use that revenue to build truly intelligent, fully online and fully customized educational programs, making it available at low cost to learners globally, all while adding content based on changing economic circumstances.
History: Let’s Take a Step Back…
Since the dawn of man-kind, education has primarily been a one-on-one relationship, with distribution mechanism comprised of mentor and pupil, dedicated time spent catering to addressing each student’s individual needs. Somewhere in the 19th and 20th centuries, with the productionization of the pupil and commercialization of the classroom, we lost that individuality. The customization that was so important to addressing each student’s weakpoints and cultivating each students strengths became a set of standardized tests and common curriculum.
Before we understand what we need to do, or how we need to do it, we should examine why we need to do it in the first place. This helps ground our decisions with a set of guiding principles. In the context of recent history, as the US economy, and indeed the richer echelons of all economies, shifted towards a service oriented structure requiring a white-collar workforce, the educational establishment adapted to those needs. This meant a full k-12 curriculum focused on arts and science based content designed to gear kids up for college, as opposed to former efforts to steer interested students into vocation based trade schools that acted as a feeder to the manufacturing bedrock that used to buttress the US economy. As employment needs in the Service Sector economies accelerated, so too did the industrialization of the educational system, prepping students for such jobs like finance, engineering and law. This included great STEM initiatives, controversial requirements like the Common Core, and well intentioned policies like the No Child Left Behind program. This represents the first input into our worldview — the emergence of a productionized educational system — designed to produce students with measured levels of ability, that feed into an economic engine with very specific white collar jobs.
The second input into our worldview is that those white collar jobs are cognitive in nature — but at various degrees of routine/requiring varying degrees of creativity.
The third input into our worldview is the accelerating increase in computational ability — both at the individual ‘computer’ basis and on a distributed basis. Take for example the fact that even through the era of the Apollo Moon Missions, ‘computers’ were actually people (primarily women, by the way) who were really really really good at math. Slowly this function was overtaken by microchip based computers, who could do that job better faster and cheaper — and freed up the original computers to move on to better things. A similar phenomenon can be seen in more ‘creative’ fields like engineering, and film, where you may have needed a team of people to design a particular system or produce a particular shoot. Now, with the advent of sophisticated CAD programs and media production software, a single person can do a job which took tens of people in a much shorter time. So there is a decrease in the demand for labor. But so far, there has generally been a partially offsetting increase in (a) volume of output in the form of additional engineering feats, or more entertainment content produced, (b) time available for leisure (and consumption of that entertainment content via Netflix…)
Our fourth input, which leads naturally to a conclusion — is how the following three inputs come together in the context of AI, and the “future of human activities”. However, artifical intelligence is not simply a continuous extrapolation of the trend of increasing computing power. True AI (i.e. general AI) can do not just the computational chores of traditional computers and thus reduce demand for labor, but it can also increase the volume of output (“a” above) by itself with limited human intervention. So there is little corresponding amplification in volume of output per human labor input by using AI (a) — but there is definitely a big corresponding increase in leisure (b).
Our fifth and final input is the other dimension to the future of education. Like someone said, “the future is already here, it is just unevenly distributed.” Meaning, so far we have discussed only the vertical axis of societal and economic progression, but not the horizontal axis — the globalization of education. Solving the accessibility problem is especially important for the short run scenario discussed above — for a variety of political and socioeconomic and moral reasons that are beyond the scope of this founding statement. Even so, the horizontal dimension is equally important to us, and perhaps more so in the short run, as the evolution of the economic and educational systems will be slow.
Our conclusion from inputs Four and Five above, is not new — the labor market will be changing both in the short run (~100 years, as AI is adopted) as well as the long run. So what is one to do with all that free time (long run), or the shifting goal posts in the labor market (short run)? Well for the long run, we imagine one might look to what a plurality of kids with leisure time enjoy — coloring and dreaming of being an astronaut. More formally, pursuit of liberal arts and sciences, and travel or exploration. Specifically we imagine a future where the human race is, for example, creating new art and art forms, exploring the final frontier along with the boundaries of physics (like unification of quantum physics and gravity, which AI is unlikely to be able to solve without physical observations). What about the short run? After all, the Long Run doesn’t happen if the human race starves and civilization collapses in the Short Run. We’ll need a lot of labor market retooling for changing circumstances — including jobs that (while the AI is still less generally intelligent than a human) augment the AI with creativity, or help it get smarter and more capable through human-computer collaboration. What is common in both situations? The need to educate — and re educate quickly. Because whether it is needed to adapt to changing circumstances in the labor market, or because the student wants to pursue a different path of leisure or exploration — education is core to the process. Hand-in-hand goes a revamped system that can deliver that education*. For the immediate future (~5 years) education will not change drastically — there will still be subjects and exams, much as they exist today. This is the reality, and is the base that we start off with to develop our education system. Both as a company and as a community, we can use this base to perfect our teaching methods, prepare our technology, create our content — to shape the education system of the future, and actually build a foundation on which to grow.
*As an illustration, consider the beginning of the “high school movement” in the agricultural states in the US in the early 20th century. As industrialization eliminated the need for farm hands, youth who would otherwise have left school to become farm-hands now were essentially unemployed. Instead of letting these early years go wasted, these states required all youth to attend school until age 16 (which unlike today, was not common back then). This was a huge shift in labor and economics, but it paid off — the skills those youth learned constituted the most skilled and productive workforce in the world — and powered the US through the first half of the 20th century.
