Internet is all around us. Ubiquitous computing power and telecommunication technologies are growing at an astonishing rate. At the same time, the world is changing rapidly.
Computer simulation, artificial intelligence techniques, cloud computing, green tech-solutions, metaheuristics, location based systems… In this blog, we invite you to explore how technological advances and engineering new knowledge can help us to build new societies, new enterprises, new horizons; a SMARTer ones.
But… are we ready?
Are we ready for a smarter world? Well, first, let’s ask: is our world becoming smarter? Several “hot” trends claim it is: big data, internet of things, and the quantified self.
Big data enthusiasts offer vivid examples of how we can harvest, process, and enjoy the fruits of the huge quantities of data which are waiting at the tip of our mouse. We can predict market trends, analyse weather patterns, monitor government spending. In my area — education — we are told that MOOCs (massive open on-line courses) will allow us to apply the same techniques that Amazon uses when it recommend products to guide learners in their education.
The internet of things, a term coined by author Bruce Sterling, portrays a future where more and more objects in the world around us have an on-line presence. Imagine an egg stamped with a QR code as it is collected in the hen-house. At that moment, that egg can acquire an on-line identity, and can be tracked all the way to your kitchen, where it is cracked into an omelette. The farmer can see where his eggs are going, perhaps choose middle-men that market his products locally (reducing environmental impact). Likewise, you can choose eggs that travelled the shortest distance to your grocers. Finally, your fridge can notice that you’re running low on eggs, and add a dozen to your next grocery order.
The third trend — the quantified self — has the potential to make the previous two very personal. It highlights the growing variety of devices which allow us to constantly collect, analyse, and visualise data about ourselves — biometric data, behavioural data, mood and social data, health data.
The combination of these three can literally make the world around us smarter. We are all aware of the physical landscape we live in, its hills and valleys, roads and gardens, private and public land. The manner in which the resources in this landscape are managed, used, abused or protected is visible to all. But, increasingly we are also surrounded by a data landscape, overlapping and interacting with the physical one.
Devices, objects, and indeed our bodies and minds, are constantly emitting data — and this data is piling up in mountains and flowing in rivers around us. Just like the resources in the physical landscape, this data landscape needs to be managed — so that we ensure that its potential value is shared responsibly and fairly for the benefit of all. Yet, in contrast with the physical landscape, most of us don’t see the data landscape in which we live. This creates a huge advantage for the few who do. Those who have access to our data, and those who know how to mine it.
The only possible remedy for this inequality is education. In my school years, I was taught to cook, sew, and saw, so that I would be able, should I wish, to produce the artefacts I need — and more importantly — understand how they come to be if I prefer to purchase them from others. Nowadays, we need to teach our kids to sew physical objects with digital systems and to cook data streams they produce so that they can always outsmart the world in which they live, rather than others using it to outsmart them.
Inga has written a very nice (and short!) post, reflecting on Stephen Downes’ presentation at LSE yesterday.
I agree with much (most?) of what Stephen says, and I recommend this slidedeck to anyone interested in MOOCs. There are a few issues which raise some questions, so I thought I’d mention those. Stephen Downes refers to #designpatterns and Diana Laurillard ‘s Learning Designer in slide 11. Diana had explored #designpatterns in her book, and we did in ours. Together with Steven Warburton, I’m currently running a project on MOOC design patterns. The examples on slides 12 and 13 are close to proto-patterns.
I would take an issue with slide 30. Very interesting, and important argument – and very timely in relation to #MOOCs. Bird formations are designed by evolution: not the actual pattern, which is dynamical, but the simple rules of behaviour which make it possible. There’s a great analogy to #MOOCs, except that there we’re talking about artificial systems, and hence we have a responsibility to design them. Emergent behaviour will not emerge out of chaos, only out of carefully managed chaos.
As for slide 52, I have an issue with the analogy between neural networks and social networks. Neurons are governed by complex electro-chemical interactions, but have no autonomy. Humans in online social networks are governed by pathetically simplistic interaction mechanisms, but have autonomy. Both are complex, dynamic systems – but they are very, very different. Neural networks have evolved to be amazingly powerful pattern recognition machines. See Andy Clark’s work on brains and predictive coding. Online social networks are good at generating patterns, e.g. #MEMEs, not necessarily at spotting them. This is the root cause of bubbles – whether market bubbles or filter bubbles.