‘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

Background

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

Format

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

Results

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.

References

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.

Artificial intelligence, creativity and metis

Boris pointed me to CreativeAI, an interesting article about creativity and artificial intelligence. It offers a really nice overview of the development of the idea of augmenting human capabilities through technology. One of the claims the authors make is that artificial intelligence is making creativity more accessible. Because tools with AI in them support humans in a range of creative tasks in a way that shortcuts the traditional requirements of long practice to acquire the necessary technical skills.

For example, ShadowDraw (PDF) is a program that helps people with freehand drawing by guessing what they are trying to create and showing a dynamically updated ‘shadow image’ on the canvas which people can use as a guide.

It is an interesting idea and in some ways these kinds of software indeed lower the threshold for people to engage in creative tasks. They are good examples of artificial intelligence as partner in stead of master or servant.

While reading CreativeAI I wasn’t entirely comfortable though and I think it may have been caused by two things.

One is that I care about creativity and I think that a good understanding of it and a daily practice at it—in the broad sense of the word—improves lives. I am also in some ways old-fashioned about it and I think the joy of creativity stems from the infinitely high skill ceiling involved and the never-ending practice it affords. Let’s call it the Jiro perspective, after the sushi chef made famous by a wonderful documentary.

So, claiming that creative tools with AI in them can shortcut all of this life-long joyful toil produces a degree of panic for me. Although it’s probably a Pastoral worldview which would be better to abandon. In a world eaten by software, it’s better to be a Promethean.

The second reason might hold more water but really is more of an open question than something I have researched in any meaningful way. I think there is more to creativity than just the technical skill required and as such the CreativeAI story runs the risk of being reductionist. While reading the article I was also slowly but surely making my way through one of the final chapters of James C. Scott’s Seeing Like a State, which is about the concept of metis.

It is probably the most interesting chapter of the whole book. Scott introduces metis as a form of knowledge different from that produced by science. Here are some quick excerpts from the book that provide a sense of what it is about. But I really can’t do the richness of his description justice here. I am trying to keep this short.

The kind of knowledge required in such endeavors is not deductive knowledge from first principles but rather what Greeks of the classical period called metis, a concept to which we shall return. […] metis is better understood as the kind of knowledge that can be acquired only by long practice at similar but rarely identical tasks, which requires constant adaptation to changing circumstances. […] It is to this kind of knowledge that [socialist writer] Luxemburg appealed when she characterized the building of socialism as “new territory” demanding “improvisation” and “creativity.”

Scott’s argument is about how authoritarian high-modernist schemes privilege scientific knowledge over metis. His exploration of what metis means is super interesting to anyone dedicated to honing a craft, or to cultivating organisations conducive to the development and application of craft in the face of uncertainty. There is a close link between metis and the concept of agility.

So circling back to artificially intelligent tools for creativity I would be interested in exploring not only how we can diminish the need for the acquisition of the technical skills required, but to also accelerate the acquisition of the practical knowledge required to apply such skills in the ever-changing real world. I suggest we expand our understanding of what it means to be creative, but without losing the link to actual practice.

For the ancient Greeks metis became synonymous with a kind of wisdom and cunning best exemplified by such figures as Odysseus and notably also Prometheus. The latter in particular exemplifies the use of creativity towards transformative ends. This is the real promise of AI for creativity in my eyes. Not to simply make it easier to reproduce things that used to be hard to create but to create new kinds of tools which have the capacity to surprise their users and to produce results that were impossible to create before.