This is not exactly a now page, but I thought I would write up what I am doing at the moment since last reporting on my status in my end-of-year report.
The majority of my workdays are spent doing freelance design consulting. My primary gig has been through Eend at the Dutch Victim Support Foundation, where until very recently I was part of a team building online services. I helped out with product strategy, setting up a lean UX design process, and getting an integrated agile design and development team up and running. The first services are now shipping so it is time for me to move on, after 10 months of very gratifying work. I really enjoy working in the public sector and I hope to be doing more of it in future.
So yes, this means I am available and you can hire me to do strategy and design for software products and services. Just send me an email.
Shortly before the Dutch national elections of this year, Iskander and I gathered a group of fellow tech workers under the banner of “Tech Solidarity NL” to discuss the concerning lurch to the right in national politics and what our field can do about it. This has developed into a small but active community who gather monthly to educate ourselves and develop plans for collective action. I am getting a huge boost out of this. Figuring out how to be a leftist in this day and age is not easy. The only way to do it is to practice and for that reflection with peers is invaluable. Building and facilitating a group like this is hugely educational too. I have learned a lot about how a community is boot-strapped and nurtured.
And finally, the last major thing on my plate is a continuing effort to secure a PhD position for myself. I am getting great support from people at Delft University of Technology, in particular Gerd Kortuem. I am focusing on internet of things products that have features driven by machine learning. My ultimate aim is to develop prototyping tools for design and development teams that will help them create more innovative and more ethical solutions. The first step for this will be to conduct field research inside companies who are creating such products right now. So I am reaching out to people to see if I can secure a reasonable amount of potential collaborators for this, which will go a long way in proving the feasibility of my whole plan.
If you know of any companies that develop consumer-facing products that have a connected hardware component and make use of machine learning to drive features, do let me know.
That’s about it. Freelance UX consulting, leftist tech-worker organising and design-for-machine-learning research. Quite happy with that mix, really.
On Wednesday Péter Kun, Holly Robbins and myself taught a one-day workshop on machine learning at Delft University of Technology. We had about thirty master’s students from the industrial design engineering faculty. The aim was to get them acquainted with the technology through hands-on tinkering with the Wekinator as central teaching tool.
The reasoning behind this workshop is twofold.
On the one hand I expect designers will find themselves working on projects involving machine learning more and more often. The technology has certain properties that differ from traditional software. Most importantly, machine learning is probabilistic in stead of deterministic. It is important that designers understand this because otherwise they are likely to make bad decisions about its application.
The second reason is that I have a strong sense machine learning can play a role in the augmentation of the design process itself. So-called intelligent design tools could make designers more efficient and effective. They could also enable the creation of designs that would otherwise be impossible or very hard to achieve.
The workshop explored both ideas.
The structure was roughly as follows:
In the morning we started out providing a very broad introduction to the technology. We talked about the very basic premise of (supervised) learning. Namely, providing examples of inputs and desired outputs and training a model based on those examples. To make these concepts tangible we then introduced the Wekinator and walked the students through getting it up and running using basic examples from the website. The final step was to invite them to explore alternative inputs and outputs (such as game controllers and Arduino boards).
In the afternoon we provided a design brief, asking the students to prototype a data-enabled object with the set of tools they had acquired in the morning. We assisted with technical hurdles where necessary (of which there were more than a few) and closed out the day with demos and a group discussion reflecting on their experiences with the technology.
As I tweeted on the way home that evening, the results were… interesting.
Not all groups managed to put something together in the admittedly short amount of time they were provided with. They were most often stymied by getting an Arduino to talk to the Wekinator. Max was often picked as a go-between because the Wekinator receives OSC messages over UDP, whereas the quickest way to get an Arduino to talk to a computer is over serial. But Max in my experience is a fickle beast and would more than once crap out on us.
The groups that did build something mainly assembled prototypes from the examples on hand. Which is fine, but since we were mainly working with the examples from the Wekinator website they tended towards the interactive instrument side of things. We were hoping for explorations of IoT product concepts. For that more hand-rolling was required and this was only achievable for the students on the higher end of the technical expertise spectrum (and the more tenacious ones).
The discussion yielded some interesting insights into mental models of the technology and how they are affected by hands-on experience. A comment I heard more than once was: Why is this considered learning at all? The Wekinator was not perceived to be learning anything. When challenged on this by reiterating the underlying principles it became clear the black box nature of the Wekinator hampers appreciation of some of the very real achievements of the technology. It seems (for our students at least) machine learning is stuck in a grey area between too-high expectations and too-low recognition of its capabilities.
These results, and others, point towards some obvious improvements which can be made to the workshop format, and to teaching design students about machine learning more broadly.
We can improve the toolset so that some of the heavy lifting involved with getting the various parts to talk to each other is made easier and more reliable.
We can build examples that are geared towards the practice of designing IoT products and are ready for adaptation and hacking.
And finally, and probably most challengingly, we can make the workings of machine learning more transparent so that it becomes easier to develop a feel for its capabilities and shortcomings.
We do intend to improve and teach the workshop again. If you’re interested in hosting one (either in an educational or professional context) let me know. And stay tuned for updates on this and other efforts to get designers to work in a hands-on manner with machine learning.
Special thanks to the brilliant Ianus Keller for connecting me to Péter and for allowing us to pilot this crazy idea at IDE Academy.
Sources used during preparation and running of the workshop:
The Wekinator – the UI is infuriatingly poor but when it comes to getting started with machine learning this tool is unmatched.
Arduino – I have become particularly fond of the MKR1000 board. Add a lithium-polymer battery and you have everything you need to prototype IoT products.
OSC for Arduino – CNMAT’s implementation of the open sound control (OSC) encoding. Key puzzle piece for getting the above two tools talking to each other.