Friday, November 7, 2014

Skeletons of Ideas or Cross-Field is the Future

The building blocks of knowledge

      Picture this: you divide the knowledge of a field into its building blocks (the atomic "things" that knowledge is made of). I call these "things" concepts because they are the basic ideas from which that knowledge is built. For instance, Quick Sort is such a building block for computer science because it is a concept = it's a way of doing / describing something. In social psychology, the Abilene Paradox is a concept - a building block for its field - since it describes a phenomena that can happen in groups of people. In origami, the Mountain Fold is a concept for its field since it identifies a certain action to achieve a new shape (it is a way of doing something in order to get a result, just like Quick Sort is for sorting numbers).

Why combine these concepts cross-field?

      Remaining in one's field is beginning to be dangerous and it will be even more so in the future as problems grow in difficulty and size and complexion. The solutions will have to be creative, inventive, plausible to implement, ideally simple. This can be achieved not by getting a team of laser junkies, but by combining the strengths and cultures of specialists from many fields. There is no field that has all concepts in it. For instance computer science doesn't have an Abilene Paradox concept and you might need it should you ever design a system that simulates group decision making. The only way to learn about it is by peeking over into psychology's concepts.

How can we combine these concepts cross-field?

      If you just start combining the concepts described above and more like them, you will only end up with something pretty in your hand, but useless. It will be useless because it is not placed in a context and not given a problem to solve. Therefore it cannot bring value if it just sits around.
      Here's a great example:
      In this TED Talk, Robet Lang tells how Lawrence Livermore National Lab used origami to fold the oversized mirrors for its newest, huge telescope.
      This is an inspiring example of cross-field in practice. The context is given by the telescope itself and the problem is how to get those immense mirrors out into space provided that no new, larger rocket is built to transport them. 

      So how do we combine the concepts? I see two ways thus far.
      Either way, you must have the context and the problem so that the fitting of concepts is not done randomly, but in a certain direction.

1. Intuition

      Everything starts from the humble, manual labour. You just need access to all of this knowledge: The Concepts, The Contexts and The Problems.
      For play, let's say there is a bowl with contexts written on cards, then there's another bowl with problems and then there's a bowl for each field with its concepts in them. You get a context and a problem and then you can browse through the concepts from the bowls and see what you can build.
      The human brain is wired to spot patterns and connections in order to solve problems. We are born problem solvers. Once you read the context and the problem, your mind immediately starts searching for solutions - it's in our nature. And browsing through concepts at this moment (known or unknown to you) is like choosing the lego pieces to build what you have in mind. Understanding the concept you picked up is done nearly on the spot* (like understanding the shape of a lego piece to determine whether or not it will help you).
      Also, you could be shown related concepts to the one you picked (e.g.: if you choose First Order Markov Processes, it will point you to the Second Order Markov Processes or to Trigrams from Natural Language Processing).
      *Let's say the simplified concepts are written on cards (they can be expanded by connecting, for instance, to your phone during play so you can interact with the concept and see examples of its usage for a deeper understanding).

2. Algorithms

      Soon enough algorithms that can combine concepts that solve the problems in the given contexts should emerge. Maybe expert systems. Or maybe some Artificial Intelligence algorithm that is trained by observing the human intuition of matching these concepts. 

The Future of Learning

      I also see applications in the future of learning because if knowledge is delivered in this brain friendly form, one could learn them much faster. As I've seen in school, my main trouble was using the concept I had just been taught (that is generalizing it and placing it in a context other than the didactic example). This happened because that concept was taught too specifically to its field, without any other more intuitive explanations such as examples from real life or metaphors. I don't think math is supposed to be left in its own language - if it's also explained intuitively (aside from specific notations) it will lose its intimidating look and one could start using it on his/her own in other fields - not just the semester test or exam. And this goes for pretty much every field - physics, chemistry, psychology and so on. But the most urgent are the ones which hide in their own language like math and physics.