Defining Creativity

Sometimes I’m going to post some old notes here. This is one of them.

There are many different attempts to define creativity, and work on automating creativity is often inhibited by the au­thors’ own definitions. The process of creativity has been divided into several stages. Hadamard’s description of Poincare’s four stages is used by Boden amongst others: these are

  • preparation ­ define the problem, and attempt to solve it by rational means.
  • incubation ­ generate novel patterns or concepts.
  • inspiration ­ recognise a creative solution.
  • verification ­ rationally compare the solution to the problem.

The preparation and verification stages may not exist be­cause there may not always be a given aim to creative work. Incubation and inspiration are however central to creativity: it always contains a two­ part process of generation of concepts then evaluation of how creative those concepts are.

Defining the problem

The act of finding a problem is usually part of the creative process. Some creativity systems are very focussed on this: for example, in flexible means­end analysis (Jones+Langley) the problem is defined as a current world state and a set of goal conditions. This part of creativity is very closely related to conven­tional learning theory: Thornton (C.Thornton, 1998) has argued that the bias inherent in any recursive learning algo­rithm can be viewed as a form of creativity.
Although I have stated that preparation and verification are not necessarily essential to creativity, they are very impor­tant: perhaps the difference between creativity and random­ness, between human creativity and madness is in its con­nection to a purpose or communication (for example, even in describing art, we speak of its expression).

Generating novel concepts

Three main types of creativity are Boden’s (M.Boden, 1990) improbabilist and impossibilist creativity, and a chaotic form of creativity seen in many of the neural­ network based approaches.

  • improbabilist creativity is the construction of new concepts from existing ones, often combining previously­unconnected information to solve a previously­ unseen problem. The lightbulb puzzle (including the information that lightbulbs are hot just after they are on) is an example. Improba­bilist creativity was explored by Koestler (his ‘bisoci­ation of matrices’ (A.Koestler, 1964)) and discussed by Perkins (DN.Perkins, 1981).
  • impossibilist creativity transforms the space in which a concept can exist. This includes widening the frame of information around a concept being examined, and the removal of assumptions or constraints from the en­vironment in which a concept exists. Jackson Pollock putting his paintings on the floor (removing the as­sumption that paintings need to be vertical) is an ex­ample of this. There are many constraints at play in creation. For in­stance, in the creation of prose, the pattern of stresses in a line is as important as the meanings and rhymes and hidden meanings within a stanza. We work within unspoken rules: creativity can work within these rules (using them as guidelines) or on those rules them­ selves (to create new forms or categories of art or sci­ence).
  • chaotic creativity is where a small mutation of an ex­isting concept is allowed. Beethoven’s minute rework­ing of his musical themes until he hit one which was acceptable to him, and Thaler’s creativity machine are examples of this.

This seems a reasonable division to work with, although it would be interesting to see whether, when these three forms of creativity are finally modelled, other forms of creative act and process become apparent.

Measuring creativity

Creativity is often confused with the creation of new things. Creativity is not novelty: while generation of concepts is important, it is not effective without their evaluation. Evaluation consists of deciding which solutions are cre­ative, either by clustering them or by using a measure of surprise. To be creative, we need some sense of the differ­ence between what is truly creative and what is just new. We need to have a sense of how to cluster the mutations generated and how to define the boundaries between those clusters: we need a sense of taste or discrimination. Much of this, we can take from work on concept clustering and in­formation fusion, and work on the difference between cre­ative solutions and novel near­miss solutions to a problem, and the change in process that leads to them. As an example, take the humble paperclip. I can bend a pa­perclip into dozens of minutely different and new shapes, but only a few could be seen (without an explaining con­text, which is in itself a creative act) as creative mutations of its original shape.

Comparing the solution to the problem

If the creative process is used to solve a specific prob­lem, then the problem and its potential solutions need to be matched. Again, this process is closely related to the process of assessing the output from conventional learning algorithms.

What and why creativity?

Creativity is one of those strange human attributes. Like beauty, everyone knows what it is, but can’t quite pin it down (although several psychologists and computer scientists are trying to). Ask someone on the number 8 bus (the number 9 is so passe dahling), and they’re likely to talk about art and genius (Einstein crops up a lot) and those leaps of imagination that take you from a difficult problem to an elegant solution in seconds.

So, assume for a moment that defining creativity is closely bound up with how we view art. Because that takes us to the heart of the problem, for both creativity scholars and curators. Which is how do we know when what we’ve got is good art? Creativity is about creation (of things, concepts, etc), but anyone can do that, and most people create things most of the time, even through mundane things like stepping into puddles and changing the splashmarks around them. What really distinguishes the creative from the creation is an agreement, a sense of the novel, the innovative, even the beautiful. (My personal measure for art btw is “does it tell me something about the world, tell me something about myself or make me smile”).

Creativity is also bound up with how we define ourselves as humans. Douglas Adams linked religion to man’s view of himself as a toolmaker, but I think this definition goes deeper than that. Humans survive in extreme spaces and despite rapid changes in their environments because they can create what they need for that survival (clothing, shelter, food production etc) from the materials around them. I’m saving a discussion of toolmaking (in humans and other animals) for another day, but the point here is that creativity is a fundamental part of human success (and possibly also failure) in the world. So it may be useful to understand how it works, and how we might be able to harness that, in a non-terminators-taking-over-the-world kind of way.

So, how do we define, model and even mimic creativity. I’ll leave that for later posts…

What is Information Fusion, exactly?

I’ve spent half a lifetime thinking about how people create and use mental models of the world, and I still don’t have a good answer for the title question. I’ve had a look around the web and its meaning seems to change with the background of its user. So I’m going to try again.

Information fusion (IF) is what happens when you apply data fusion principles to non-numeric data. Data fusion (DF) takes isolated pieces of sensor output (e.g. a series of sonar or radar plots) and turns them into a situation picture: a human-understandable representation of the objects in the world that created those sensor outputs. Multisensor DF does this with outputs from more than one sensor and/or sensor type.

Most data fusion happens in a nicely constrained and understandable environment where things (e.g. ship, planes) lurk or move around producing recognisable signatures (e.g. shape outlines or frequency lines). And that’s where the problems with IF start. IF deals with non-numeric data, e.g. verbal reports, documents and database entries. The first issue is knowing where the boundaries of the problem are. If we carry on the IF as an extension of DF paradigm, instead of dealing with a set of identifiable things in a known environment producing a known set of possible numerical signatures, IF deals (usually) with information written by humans describing their perception of the world. And that, in DF terms, is a nightmare: the data is of variable and unknown quality: it can be deliberately or innocently false or unreliable, can at the same time cover both much more than the area of interest and much less of the area than is needed to form a situation picture, and is usually presented (except sometimes in the case of data incest; more on that later) in a variety of formats, using a wide variety of terms for similar concepts and classes of concepts.

So what can we start to conclude from this? Well, first, that any IF system that wants to succeed will either have to have an area of interest (e.g. a topic or set of topics, or even a physical area if it’s linked into a DF system), or accept its fate as an unconstrained data mining/ data visualisation or data summary system. IF developers will either have to have very strong words with their system users about the use of precise language (easier with the military; not so easy with commercial or occasional users) and hanging back on the conjectures, or develop a system that is either a smart form of search engine, or very capable at parsing and understanding natural language structures (note the use of structures there: an IF system may not necessarily need to fully understand NL, but may survive on enough understanding to know when to pass something difficult up to a human) and knowing which pieces of information are relevant. IF developers will also have to build systems capable of parsing and merging the different levels of language (e.g. fruit for apple), overlapping meanings (a person is not always a game player) and different language contexts, and deal with all the anaphora, uncertainty and downright error that humans manage to create whenever they’re allowed to interact freely.

But I still haven’t answered the question. And I, as much as anyone else, am guilty of using my own working definition. To me, IF is the combination of all available sources of relevant information (yes, numeric as well) to create a (hopefully stable) concise and usable mental picture of an area of the world that I’m interested in. That, to me, sounds awfully much like thinking, which is the only excuse I have for spending years playing with artificial intelligence.

Hello World

So… a blog. A place to explore whatever interests me about the world. And in no particular order, this includes: risk, knitting, thought, images, power structures, combinations, the meaning and origins of places, languages, lies and growing vegetables. There are other things of interest, but they’ll turn up when they want to.