Yesterday I attended the Tuttle Club’s What are we going to do about the Internet of Things. This was the first real open space event I’ve attended (more on that in the future), and it was fantastic! The richness of discussion among people who are passionate about a topic was phenomenal – I’m already thinking of places and ways it might work in future.
So on to the Internet of Things (IoT). I’ve written about the theory in the past, covering the difficulties with making sense of it, and the changes it will bring for many industries. But this night was a chance to work through questions and ideas:
- What does the rural IoT look like?
- The IoT is actually not about things at all – discuss.
- I own a Raspberry Pi – what now?
- Who should own my data?
- So you’ve put a chip in it – what else do you need to make a “smart” object?
- Why do we think the IoT is limited to human intelligence?
The group I sat down with settled on the last of these; here’s the course we took:
1. What is intelligence?
Humans and machines learn and process information in very different ways. Computer intelligence is data and statistics driven, which can be used to calculate risk, likelihood, and analyse information. Human intelligence can only compete where there is uncertainty, and through making connections between seemingly unconnected areas.
2. Connected vs empowered devices
When is a toothbrush not a toothbrush? When it is making decisions for you. For a device to be more than “connected / digitized” it needs to have the agency to respond and make decisions based on inputs.
3. Empowering who?
Pretty early on we touched on the question of rights, values and data ownership. We all generate so much data, but how often are we aware of how it is used? How much of it do we own?
4. What are the alternatives?
5. The other alternative
If you can’t beat the system, game it. The idea of “throwing dust” into the machine is not a new one, but could be increasingly common as people learn how to circumvent undesirable outcomes. Lovely example here from Unfitbit.
6. Inaccuracies are still a problem
Especially when they impact the services and opportunities available to you. Take examination marking – a mistake / uncorrected entry error, could change the course of your life.
7. Beyond the data, we still need people
To develop fair systems, introduce safeguards and deliver the right outcome. Medical diagnostic tools still rely on the support of professionals and patients to treat a condition. The last mile is still the most difficult to get right, however robust the data.