In his excellent book, 'Where Good Ideas Come From', Steven Johnson uses the phrase ‘the adjacent possible’ to denote how some ideas are just too innovative, too off-the-wall, too ahead-of-their-time to be successful. The classic example of this is Babbage's Difference Engine. This was the earliest invention of an algorithmic computer, but because it was built in Victorian times, before the existence of electronics, it never stood a chance of actually being used. It was brilliant, innovative, remarkable, but it also was impractical and would never work. It lay too far beyond what was possible at the time it was created. We had to wait for transistors to be invented before the invention of practical computing machinery became possible. Steven Johnson reminds us that we can't look too far afield for discovery. We have to look just over the brow of the next hill (not behind the looming mountain in the distance).
The word 'paradigm' was presented by Thomas Kuhn to denote 'coherent traditions of scientific work’ made up of laws, theories, applications and instrumentation that reflect a way of thinking about a specific domain of knowledge. Kuhn describes paradigms as largely remain static and stable, gradually expanding the boundaries of knowledge at their edge. But, when scientific explanations and predictions don't match our observations about what is happening in the real world, a wonderful schism occurs. Theories break down. Scientists tear their hair out in frustration. Nothing seems to make sense until finally, the domain's theory, explanation and practices have to change. It's this occasional disconnect between interpretation and observation that powerfully churns the creative process of scientific work to sometimes trigger ‘paradigm shifts’. Under normal processes within a paradigm, the area of investigation available to us is incremental and predictable. We see the adjacent possible with no mystery. Under the disrupted conditions of a paradigm shift, we don't know where we might end up. The boundaries of the adjacent possible expand in an abrupt, disruptive, non-linear and unpredictable way.
Understanding and harnessing the underlying dynamic of this sudden fracturing, and restructuring of a body of knowledge under a paradigm shift would have to lie at the central heart of the inner workings of a breakthrough machine. This means that perhaps the central construct of our representation of scientific knowledge should be a paradigm itself. This is not presently the case. The most common prevailing view of way for bioinformatics researchers to define knowledge are based on attempts to define large-scale logical schema (called ‘ontologies’, a word derived from the name for the philosophical study of existence itself) that are intended to define universals rather than scoped, domain-specific assertions limited to describe a locally defined phenomenon.
I feel that we should adjust our knowledge representation to focus on paradigms. Like an expert scientist in a given field, our technology must analyze our existing knowledge so that we can ask important questions that that can be tested experimentally. To be able to do this, we have to focus on details that are directly in front of us: a cancer specialist does not take into account remote astrophysical knowledge of distant galaxies when attempting to find binding sites for her drugs to bind to; a geologist attempting to predict when an earthquake will occur probably does not use information about weather patterns in his calculations (although you never know, he might). It is important to scope the way our knowledge engineering and management technology represents the boundaries that frame the way that we ask questions effectively and we don’t currently have a good methodology for this.
Thus, a interesting thread to work on in scientific knowledge engineering is just simply to ask "How should we represent and process paradigms within informatics systems?". When paradigms duel for supremacy in important fields, epic battles are fought and great careers are either made or destroyed. We might also ask "How do we know when experimental evidence stands between two battered and bruised paradigms and declares one of them the winner?". In particular, probably the the most important and interesting research question we should think about is: "How can we recognize when paradigms fail to provide a good model of reality, tempting us with the scent of a possible underlying breakthrough to be made?".
These are the tiny tears in the curtain that we need to latch onto and pull on with all our might to reveal the truth that lurks hidden beneath. This is where the magic happens and I feel that Kuhn's brilliant notion of scientific paradigms and paradigm shifts could provide us with a powerful unifying concept to provide the underlying blueprint of a breakthrough machine.