So I promised to write about innovation - what it is, how to identify it. In particular, with regards to interdisciplinary computing. The more I have pondered this issue, the more it feels like a trick question.
Innovation is something unusual, different, new - pick your vocabulary, but the basic idea is that it is something no one has thought of before. Now there is successful innovation and unsuccessful innovation, a distinction that sometimes gets forgotten. After all, there are plenty of innovative ideas that don't gather traction. Purple star shaped twinkies anyone? Some ideas are unique, but unlikely to gather a following.
It is easy to identify an innovative idea in retrospect. Interdisciplinary computing programs and activities such as I have discussed across many blog posts provide some excellent examples. Pattern recognition and computational emotion, computational journalism, bio-informatics, Charles Babbage in the front seat of your car. Successful innovations eventually mainstream themselves. For example, Bio-Informatics as an interdisciplinary field is becoming perceived as mainstream. But it wasn't originally - not when I was in grad school not tooo many years ago. Conversely, the talking Babbage GPS has a ways to go before we all have one in the passenger seat. Assuming we ever do.
When each of these areas of innovative computing appeared they were probably only recognized as interesting by a limited number of people. That is the nature of true innovation - if everyone could think of it then it wouldn't be innovative. It is easier to discuss what successful innovation is using past examples than it is to identify it at the moment of inception. (hence the feeling of a trick question)
For quite a while interdisciplinary computing as a concept didn't exist. It was (and in ground breaking areas arguably still is) considered a strange term. Before we recognized the idea of equal contribution of two fields to create a new creative field, we tended to think in terms of: computing; other field; applications of computing in other field.
The whole idea of interdisciplinary computing was very innovative. It was disruptive to traditional computing as evidenced by objections, denials, ignoring, "it isn't real computer science" types of reactions. The Innovator's Dilemma book I have been jumping off from for the past several posts calls this being "trapped in reactivity" - perfectly normal, predictable, human, and a great way to miss the boat. To not recognize the force of merging fields until they are upon us.
Interdisciplinary computing on a high level is becoming mainstream and the innovations are occurring within the divisions - what new fields will emerge and gain traction? What do we need to be on the look out for?
We can't rely on psychic powers to spot significant innovative potential in interdisciplinary computing. However, there are a few guidelines to identifying these ideas if we continue to follow the theory presented in The Innovator's Dilemma. As we cruise along in our professional lives, we can keep alert to changes around us and ask questions.
1. Is there a problem? In other words, is something not right? For example, there has been a "problem" for many years with declining interest in studying computing in school (at any level) and an increase in computing savvy students choosing to study other fields. We knew that a long time ago. It can take a while, and it did I believe, for the severity of the problem to be acknowledged - and this is "the real problem". Seemingly logical hypotheses occupied our energies: "enrollments are down because of the booming economy and thus students don't need a computing degree to get a job in a computing field" or "enrollments are down because of the poor economy and the perception that all the computing jobs are going overseas". The economy definitely has an effect on computing enrollment - no argument there. However it can't always be the economy! If that were the case, we could all throw in the towel, because the economy will ALWAYS be either good or bad or heading between one and the other!
So. First: recognize a problem. Second, recognize when the common responses may be reactive and not fully understanding the problem.
2. Do I understand the problem? Really understand the problem? If you aren't sure, how do you come to understand it? You don't run a lot of surveys and focus groups (so goes the theory) and ask people what they think or want. Instead you watch what they do. In our example, we would have watched and seen that students were going to study biology/law/engineering/economics (whatever) and learning the computing they needed for that field through those studies, or on the job. Taking it one step further, watching many of those people would have shown that many of them *enjoy* computing though they might have said otherwise if you asked the question. This is still the case: many of the cross over people don't consider themselves computer scientists but they thoroughly enjoy computational thinking and using sophisticated computing skills. Hmm.... Realizing that early on might have triggered a different way of thinking about how to tackle computing enrollment challenges. Many in the computing community are now understanding the problem and taking action. The truly innovative ones recognized it early and jumped on the opportunity.
3. How can I view the problem as an opportunity? Once you understand, really understand the problem, look at it as a chance to think outside the box, take risks and do something really different in response. Be prepared to learn as you go - as many successful interdisciplinary computing programs have in fact done (here was one nice example I profiled a few months ago). The challenge/opportunity includes searching out the market (substitute: students) for your new idea rather than trying to convert the existing market (substitute: students who have expressed interest in computing but not followed up). In our example, that might mean looking for the students who demonstrate through their actions that they are multi-disciplinary by nature and interested in the intersection of fields. Many successful interdisciplinary programs have done just that: attract people who are deeply interested in subjects that appear unrelated to computing.
In other words: look for the direction that people are already going (into biology/law etc) and aim your program or project at them. They might very well love your idea. And if you don't succeed the first time, try again - plan for this. Keep it simple. Assume you won't get your program or project right the first time and reserve resources (people, time, energy, money etc) for re-tooling and re-tooling. Acknowledge you don't know where you will end up other than that you will end up somewhere new where there is a need now. Not sometime in the future.
4. How can I view the plan's so-called weaknesses as strengths? There will be doubters and the weaknesses of your interdisciplinary efforts will be pointed out to you. Turn it around. Because the weaknesses may well be unrecognized strengths - that is part of what makes the plan innovative!
Reading The Innovator's Dilemma has been one of the more fascinating and eye opening ways to look at computing, whether in industry or academia. Interdisciplinary computing is a perfect ongoing case study to test the book's ideas and perhaps move computing forward in new ways.
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