Archive for October, 2013

Food for Thought

October 7, 2013

Starvation Diet

Imagine if someone said to you: “Oh I gorged myself for four years back in the eighties. I ate so much then that I don’t have to eat ever again.” What would you say to them?

Some people are thin, others are fat. Some eat healthy, some not so much, but all of us need to eat every day (give or take a day or two) in order to survive. No one can live, let alone thrive, if they ate for the first twenty years of their lives and then had to starve themselves.

Why is education any different? Why do we believe that education stops (for most people) at age 17 or 21 and then we have to survive on what we learned then for the rest of our lives?

Personalized Learning

Actually, very few of us believe that education stops at 21. All of us understand that we learn continuously, throughout our lives. In some professions – academia, music, high technology – continuous learning is encouraged by the profession itself. Still, even in these professions, we don’t necessarily learn how to learn. For the most part people stick to a narrow, disciplinary idea of learning. Rare is the scientist who learns an entirely new area after they are tenured. Disciplinary learning is important, but it’s not enough to address the complex challenges of the future.

Let’s go outside the learned professions and see what curious people, especially children, do when they want to learn something. What they do is closer to play than to work. Making new things is fun, even when the new thing is an abstract commodity like knowledge.

The idea that learning is making knowledge isn’t new; Socrates knew about it when he called himself the midwife of wisdom. These basic human qualities – curiosity, questioning, dialog are still at the foundation of learning, except that we now have technology to amplify, aggregate and distribute learning across vast numbers of people. How do we make use of technology, while building on our natural, human capacities for learning. That’s the challenge.

People + Technology –> Continuous Personalized Learning

Let us now look at some major shifts that a continuous personalized learning model entails. How can we build a lifelong learning society?

Lifelong learning societies

First, it’s clear that continuous learning cannot be learning removed from society. We cannot sustain a world in which everyone’s a full time college student from the time they are born until they die. We have to be producers as much as consumers of knowledge.

Lesson 1: Lifelong learning societies will mix work and learning throughout their citizens’ lives.

Second, it’s not clear that being a full-time student is the right thing – as we are discovering in the cognitive sciences, our mind is both embodied and embedded; we have minds precisely because we are embedded in a world where those minds are put to use. Natural learning, i.e., learning that happens as a matter of course because we are hardwired to do so (examples: learning to walk, learning to see, learning to speak) is based on tight feedback loops from mind to body and world and back. That sensorimotor loop is crucial; we can’t separate out the learning from the feedback loop. Why should artificial learning, i.e., learning that has to be taught explicitly in school and college, be any different? We should embed explicit learning into the same feedback loop of knowledge, practice and mentor feedback. Musicians and monks already behave that way. The rest of us should ape their behavior.

Lesson 2: Just as real reporting requires embedded journalists, real knowledge requires embedded students.

Third, once we step away from theoretical knowledge, for which we go to university, and look at the distribution of expertise and wisdom in all its forms, it’s clear that knowledge is spread throughout society. Our collective ability to aggregate distributed wisdom has been rather poor – most of these knowledge networks didn’t write books, and the libraries of the world were built to satisfy the demands of professors, not the universal learner.

The internet gives us the ability to aggregate human wisdom at a new scale, and to do it in a manner that enables different modes of expertise to coexist. Why should I choose between learning statistics and self-transformation? Why can’t I engage with a full spectrum of human wisdom from diverse sources, driven by distributed networks of mentors, peers and learning media? I believe that a new era of learning is well within our reach.

Lesson 3: Use technology to aggregate wisdom from all corners of the world, high and low.

However, in order to do so, we need to look beyond the MOOC, which is the last gasp of a one-way, top-down model of learning where the elite instruct the masses


Making People

October 4, 2013

Let me start with a series of claims:

  1. Disciplines don’t scale
  2. Techniques don’t scale
  3. People do scale

Depending on your viewpoint, these claims might strike you as tautological or as patently false, but let me illustrate these claims with a question and a plausible answer to that question and then a commentary that illustrates the three claims above. Question first:

What is the most general science of all?

An obvious answer to this question: physics. Another obvious answer: mathematics. Certainly, it seems that physics and mathematics are the languages in which the universe is written, the subjects with the most general scope of all.

I beg to differ. If we replace an abstract concern for the universe as a whole with problems that human beings actually want to solve (usually ones that matter to us in our own lives) then mathematics and physics are handmaidens at best and often outright harmful. Try passing a bill through parliament using mathematics and you will know what I mean. Human problems are complex, dynamic, not bound by disciplinary boundaries and in need of everything from emotion intelligence to technical skills.

Fortunately, we evolved to solve human problems. Unfortunately, our theories did not. The fact is: our subtlest theories, even the ones that have an unreasonable effectiveness in the world, depend on the tacit backdrop of human capacities that we take for granted.

You would think that a rational education system would help us build upon our natural capacities as efficiently as possible and scale these capacities across large groups of people – a genuine collective mind. That assumes that education was designed, if one want’s to call it that, by a Plato’s republic of cognitive scientists. We all know the distance between Plato’s academy and the academy down the street.

Instead modern education was built for an industrial era driven by print media. That model of education tries to scale subjects and techniques, which is fine; that was the need of the times. However, the industrial era was a stage in human history where we treated humans beings as widgets and nature as a resource to be plundered. We are now entering an era where we can’t do so anymore.

For one, we can’t take nature for granted because by doing so we have almost destroyed the planet. Second, the frontiers of knowledge have reached those very problems such as the study of the brain where science is inquiring into the very thing that we took for granted. Third, most business and social activity requires complex, adaptive behavior of the kind that isn’t inculcated by learning physics or mathematics. In fact, let me make a bold claim:

For much human endeavor, we only need a good enough (satisficing, to use a technical term) understanding of what we now call science, but not more. In fact, knowing more might be harmful, for it gives us false confidence in knowledge gathering traditions that are deeply flawed.

Yes, I know that depth is greatly prized and so is expertise, but sometimes too much learning is a bad thing too. Nevertheless, I am not arguing that shallow knowledge replace deep knowledge. Instead, I want deep problem based knowledge to replace deep discipline based knowledge.

For example, suppose you want to build a better traffic system in Bangalore. This is not a physics problem but it needs sophisticated traffic modeling. It’s not a political problem alone, though politics is at it’s core. It’s not an engineering problem, but re-engineering traffic is essential. And so on. People who come together to address the problem of Bangalore traffic will have to have individual and collective expertise in all its aspects. This is what I call the Sherlock Holmes theory of knowledge[^1] – a clear sense for what is general culture – mostly to be ignored – and at the same time, keen eye for what’s important.

Where do we go for training for such problems? More importantly, what kind of person is likely to solve such problems? I will be exploring these questions in future posts.

Let me end by reiterating my main point: we can’t scale disciplines or techniques quickly. In fact, we may not want to, for that’s an enormous investment of effort in a world where problems change overnight and new fields of knowledge arise every day. Instead of training people in tools and techniques that are applied to a narrow range of problems, let’s invert the gaze: invent tools and techniques that enhance people instead and let them adapt to the problems that face them. That’s the angle I am pushing.

[^1]: If you want to know why I call it the Sherlock Holmes’ theory of knowledge, you might want to read this list.