Pattern matching

By Gordon Rugg

Note: This article is a slightly edited version of an article originally posted on our Search Visualiser blog on May 17, 2012. I’ve updated it to address recent claims about how Artificial Intelligence might revolutionise research.

So what is pattern matching, and why should anyone care about it?

First picture: Two individuals who don’t care about pattern matching (Pom’s the mainly white one, and Tiddles is the mainly black one (names have been changed to protect the innocent…)

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Pattern matching is important because it’s at the heart of the digital revolution. Google made its fortune largely from the simplest form of pattern matching. Computers can’t manage the more complex forms of pattern matching yet, but humans can handle them easily. A major goal in computer science research is finding a way for computers to handle those more complex forms of pattern matching. A major challenge in information management is figuring out how to split a task between what the computer does and what the human does.

So, there are good reasons for knowing about pattern matching, and for trying to get a better understanding of it.

As for what pattern matching is: The phrase is used to refer to several concepts which look similar enough to cause confusion, but which are actually very different from each other, and which have very different implications.

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Creativity and idea generation

By Gordon Rugg

So what is creativity, and how can you generate more and better ideas?

There’s pretty general agreement that:

  • Creativity is a Good Thing
  • Thinking outside the box is a Good Thing
  • Thinking laterally is a Good Thing

That’s a good start.

However, when you start asking about how creativity works, or just how you’re supposed to think outside the box, or think laterally, an element of vagueness starts to roll in, like a dense bank of fog off the Atlantic at the start of a horror movie…

You start hearing stories of people and organisations that thought successfully and laterally outside the box, in a way that solved their problems with designing better elevators. You encounter puzzles involving people and items being found in improbable situations, such as stabbed to death with no weapon visible, in the middle of a field of unsullied snow. It’s all very edifying and interesting, but it doesn’t get to grips with what creativity really is, or how to do anything systematic about creating new ideas.

This article gives a brief overview of a systematic framework for making sense of creativity, and for choosing appropriate methods for generating new ideas.

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People in architectural drawings, part 6; conclusion

By Gordon Rugg

This article is the last in a short series about finding out what people really want. I’ve explored that topic via discussion of idealised dream buildings, to see what regularities emerge and what insights they provide into people’s dreams and desires.

In today’s article, I’ll pull together strands from those discussions, and see what patterns emerge.

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Detail from: “Neuschwanstein Castle above the clouds” by Arto Teräs – Own work. Licensed under CC BY-SA 4.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Neuschwanstein_Castle_above_the_clouds.jpg#/media/File:Neuschwanstein_Castle_above_the_clouds.jpg
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Things people think

By Gordon Rugg

There’s a wryly humorous summary of models of humanity that floats around in academia. It appears in various forms; the one below has an astute punch line that highlights the amount of implicit assumption in the early models.

Models of humankind:

  • Man the fallen creation (the Bible)
  • Man the thinker (the Enlightenment)
  • Heroic man (Nietzsche)
  • Economic man (Marx)
  • Man the rat (Skinner)
  • Man the woman (feminism)

It’s humorous, but it cuts to the heart of the matter. The models that shape our lives – political models, religious models, economic models – are based on underlying assumptions about how people think and what people want. As is often the case with models, these assumptions are often demonstrably wrong.

In this article, I’ll examine some common assumptions, and I’ll discuss some other ways of thinking about what people are really like.

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Images from Wikipedia and Wikimedia; details at the end of this article

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Connectionism and neural networks

By Gordon Rugg

There have been a lot of major changes in cognitive psychology over the last thirty-odd years. One of the biggest involves the growth of connectionist approaches, which occur at the overlap between neurophysiology and Artificial Intelligence (AI), particularly Artificial Neural Networks (ANNs).

Research in these areas has brought about a much clearer understanding of the mechanisms by which the brain operates. Many of those mechanisms are profoundly counter-intuitive, and tend to be either misunderstood or completely ignored by novices, which is why I’m writing about them now, in an attempt to clarify some key points.

There are plenty of readily available texts describing how connectionist approaches work, usually involving graph theory diagrams showing weighted connections. In my experience, novices tend to find these explanations hard to follow, so in this article, I’ll use a simple but fairly solid analogy to show the underlying principles of connectionism, and of how the brain can handle tasks without that handling being located at any single point in the brain.

bannerOriginal images from Pinterest and from Wikipedia; details at the end of the article.

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Parallel processing and “natural” learning: Inside the black box

By Gordon Rugg

There’s a widespread idea that before entering formal education, people learn via “natural” learning.

It’s a warm, cosy concept; “natural” evokes thoughts of wildflowers and meadows and beauty and fluffy kittens. There’s even a certain amount of truth in it; formal education does generally involve something different from non-formal education. However, when you start looking for clear, practical, explanations of how “natural” learning actually works, you encounter a sudden silence.

There are plenty of descriptions of what “natural learning” looks like, but there’s very little discussion of how it might work, in terms of plausible cognitive or neurophysiological mechanisms. This absence makes a sceptical reader start to wonder whether there actually is such a thing as “natural learning” and whether this strand of education theory is chasing something that doesn’t exist.

In fact, there is a well-understood mechanism that accounts for the phenomena being lumped together as “natural learning” and “formal learning” (or whatever term is being used in juxtaposition to “natural learning”). However, when you look in detail at this mechanism, it soon becomes apparent that using a two-way distinction between “natural” and “non-natural” is simplistic and misleading. This is one reason that the “natural/non-natural” debate in education theory is still rumbling on, after more than two thousand years of fruitless and inconclusive argument.

In this article, I’ll discuss the mechanisms of parallel processing and serial processing, and I’ll outline some implications for education theory and practice.

The joys of nature and of fluffy kittens – not always quite the same thing…

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Original images from Wikimedia

 

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Parsing designs, and making designs interesting

By Gordon Rugg

Making a design interesting can be a significant challenge for designers, particularly when working in a well-established field where most of the obvious approaches have already been tried.

Two simple but effective ways of making a design interesting are:

  • making the design novel, in terms of deep structure and/or surface structure
  • making the design difficult or impossible to parse.

The companion article to this one examines ways of making a design novel. This article looks at ways of making a design interesting by making it difficult or impossible to parse.

800px-Delos_cubic_floor_mosaic

https://commons.wikimedia.org/wiki/File:Delos_cubic_floor_mosaic.jpg

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