The Nobel Prize–winning biologist François Jacob wrote that; “Scientists apply themselves to what they believe to be the most important of the problems that seem tractable, — those that rightly or wrongly they think they will be able to solve.” Peter Medawar, another biologist, labelled science as “the art of the soluble.”
But, of course, this presents a problem for humans generally, if the problems are intractable but represent a growing or ongoing existential threat.
And it presents another one for science itself. If science only picks the low hanging fruit, how it is going to get any better?
Life begins in puzzlement and confusion. The technologies we use to navigate this confused puzzle- thought, affect, language, writing, telescopes, microscopes, smartphones, search engines — provide us with the data which we use to build and test the models we use to control and organise it.
But are we asking the ‘right’ questions to build the ‘right’ models in ways that ensure these models ‘work’- for all of us?
One technology which has achieved primacy over the last half century is digital technology in the form of data network and storage retrieval devices ranging from smartphones to potential quantum supercomputers. This technology is essentially one of control. From spreadsheets through word processing to multilevel data analytics the target is to place the device user in control of the data.
But what does data control mean?
Control systems are generally bidirectional. What you control, also controls you to a degree. Anyone who has looked after a toddler will tell you that whilst you may be the responsible adult, the toddler may be more in control that you are. Similarly, with digital technology. Your control the spreadsheet, and the spreadsheet organises the data but the spreadsheet also controls you.
And while we have lots of HCI/UI/UX design to make that control as acceptable as possible, you are still developing skills and executing tasks using a device and the software that shapes its’ operations which was designed by someone else.
And okay, it’s a lot easier and less time consuming than writing numbers in a ledger for an accounting system or results on a notepad for a research project. But these are still tools which have been designed using personas, focus groups and design thinking. At best they hit a mean representation of the user.
And people are different.
Martin Heidegger suggested that we need not be limited to questions that are answerable (fraglich) within a consensual framework of proof and refutation. They can address questions worth asking (fragwürdig) even in the absence of such a framework. Of course, we need credibility badly as we uncover more data and the world becomes more- not less- puzzling and confusing.
And while it seems only answers to fraglich questions win Nobel Prizes, the answers to fragwürdig questions create and destroy paradigms. But increasingly we seem trapped in a digital cage.
So, whether you are a scientist, a technologist, a philosopher or just a standard grade human, let me ask.
Are you asking the right questions?
How do you know?
And if you aren’t what questions should you be asking?