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 ArduinoCNMAT’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.