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The Architecture of Learning

If we want our systems to automatically learn, how should they be architected? The obvious thing to do is to take a lesson from the one “machine” that we know automatically learns, the brain. And, of course, what we find is that the brain is a connection machine; a vast network of neurons that are inter-connected at synapses. And a closer look reveals that these connections are not just binary in nature, either existing or not, but can take on a range of strength of connection. In other words, the brain can be best represented as a weighted, or “fuzzy,” network. Furthermore, it’s a dynamic fuzzy network in that the behavior of one node (i.e., neuronal “firing”) can cascade throughout the network and interact and integrate with other cascades, forming countless different patterns throughout the network. Out of these patterns (somehow!) emerges our mind, with its wondrous plasticity and ability to so effortlessly learn.


Yes, taking this lesson may seem like, well, a no-brainer, but amazingly the lesson has generally been ignored when it comes to our systems. We’re well over half a century into the information age, but looking inside our organizations, we still find hierarchical system structures predominating (e.g., good old folders). The problem with hierarchy-based structures is that they are inherently brittle. They simply don’t embody enough relationship information to effectively encode learning. They don’t even scale (remember the original Yahoo! site?)—as we have all experienced to our great frustration. There is a reason nature didn’t choose this structure to manage information!

Fortunately there has been a revolution in systems structure over the past decade—the rise of the network paradigm. The internet was, of course, the driver for this revolution, and we now find network-based structures throughout our Web 2.0 world—particularly in the form of social networks. But even these networks are not fuzzy—we are limited to establishing our relationships only in binary terms, yes or no. Sure, we can categorize with lists and groups, but we are still at a loss in representing all of the relationship nuances that range from soul mates to good friends to distant acquaintances. And that makes it difficult to apply sophisticated machine learning techniques that truly add value. What’s the percentage of “recommended for you” suggestions that you receive in your favorite social network system that actually hit the mark, for example?

But the situation is even worse with regard to our organizations’ content. In this land-that-time-forgot, our knowledge remains entombed in the non-fuzzy world of hierarchies, or at best, relational structures. Not only are these systems incapable of learning and adapting, but it is often a struggle to even find what you are looking for.

This sad state of affairs can and must be rectified. All we have to do is take our lesson from the brain, integrate our representations of people (i.e., social networks) with our content, allow the relationships to be fuzzy, and we have something that is architected a whole lot like the brain, i.e., architected for learning. That’s not sufficient—we also need clever algorithms to operate against the structure, to create the necessary dynamics and patterns that deliver the benefits of the learning back to us. But the architecture of learning is the necessary prerequisite, quite doable, and therefore quite inevitable.

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