‘Machine Learning for Designers’ workshop

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.

Photo credits: Holly Robbins
Photo credits: Holly Robbins


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.

Photo credits: Holly Robbins
Photo credits: Holly Robbins


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.

Photo credits: Holly Robbins
Photo credits: Holly Robbins


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.

Next steps

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.

  1. 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.
  2. We can build examples that are geared towards the practice of designing IoT products and are ready for adaptation and hacking.
  3. 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.
  • Machine Learning for Designers – my preferred introduction to the technology from a designerly perspective.
  • A Visual Introduction to Machine Learning – a very accessible visual explanation of the basic underpinnings of computers applying statistical learning.
  • Remote Control Theremin – an example project I prepared for the workshop demoing how to have the Wekinator talk to an Arduino MKR1000 with OSC over UDP.

Rough notes for Julian Bleecker and Nicolas Nova – Networked objects and the new ecology of things

Anything can have meaning.


  • Internet of things report by ITU
  • Shaping things — Sterling, “spimes”
  • Thinglink
  • Manifesto of networked objects

Number of conversations. ITU report: philosophy related to biz efficiency, what about social dimension?

Blogjects Does the thing itself participate in content circulation? E.g.: AIBO blog platform, indicator of how an instrument can engage in social web.

Geospatial traces E.g.: Flight aware, see airplane trajectories. Interesting thing is how bodies become digital manifestations, the other way around is exciting too. Occupying the world in a more sustainable way.

Showing where objects are. Currently this stuff comes from military or art world. They also know where they’ve been.

Tail number is like thinglink for planes. CIA terror planes – plane-spotters – unmarked planes.

If objects start blogging we might get new insights in how the world works.

Blogjects know their origin. E.g.: “How is stufff made”

Blogjects have agency, they can trigger actions and shape social practice. E.g.: TripSense. Tracking driving habits. Provides access to how he moved about SF. Impact he had on environment. How many trips he made. Insight on how he could be more eco-friendly.

Blogjects provide for new logistics.

So what?

It’s not just about technical communication and interfacing of objects. It’s about the social dimension.

This is part of a global trend, things being part of a larger ecology.

Creating legibility and transparency

Blogging pigeon. GSM backpack. Nice because it elevates the pigeon and is very low-tech.

Sending purchased objects to MySpace.

Establishing relationships between physical and virtual worlds. Barcodes, tags, stickers, etc. Nabaztag. Weak signals > blogjects.

Moving ahead

  • series of workshops to design blogjects
  • online and offline discourse
  • cataloguing and track blogjects

Their questions

  • how to go from biz efficiency to social sustainability?
  • are blogjects up to the challenge?
  • are social beings prepared to interact with blogjects?

Our questions Q Maybe it’s like objects turning them more into nature in the sense that bthey talkj back and are not part of us. A We’re cohabiting with our artefacts.

Q Are objects now doing ethnography of us? A Companies are interested in that idea, even beyond ethnography, usage tracking. Is that s’thing we want?


Een internet der dingen (open.info.nl)

“De web 2.0-hype een beetje beu? Moe van het bijhouden van alle start-ups die als paddestoelen uit de grond blijven schieten? Maak je borst maar nat, want er volgt spoedig (weer) iets nieuws. Niet web 3.0 (het web-OS), maar iets écht nieuws: het internet der dingen Parallel aan al het social software-geweld en de semantische web-discussies doen interactieve media al enige tijd moedige pogingen om de sprong van bits naar atomen te maken. Dit veld wordt gedomineerd door een wirwar aan termen, zoals: “tangible computing” (populair gemaakt door Nokia-researcher Chris Heathcote), “ubiquitous computing” (vaak afgekort tot het onooglijke “ubicomp”), “everyware” (een vrij nieuwe term) en lastpakken als “blogjects” en “spimes” (bedankt Bruce Sterling). Mijn voorkeur gaat uit naar “internet of things”, getuige de titel van deze post.”

Lees verder op open.info.nl »