Design and machine learning – an annotated reading list

Ear­li­er this year I coached Design for Inter­ac­tion mas­ter stu­dents at Delft Uni­ver­si­ty of Tech­nol­o­gy in the course Research Method­ol­o­gy. The stu­dents organ­ised three sem­i­nars for which I pro­vid­ed the claims and assigned read­ing. In the sem­i­nars they argued about my claims using the Toul­min Mod­el of Argu­men­ta­tion. The read­ings served as sources for back­ing and evi­dence.

The claims and read­ings were all relat­ed to my nascent research project about machine learn­ing. We delved into both design­ing for machine learn­ing, and using machine learn­ing as a design tool.

Below are the read­ings I assigned, with some notes on each, which should help you decide if you want to dive into them your­self.

Hebron, Patrick. 2016. Machine Learn­ing for Design­ers. Sebastopol: O’Reilly.

The only non-aca­d­e­m­ic piece in this list. This served the pur­pose of get­ting all stu­dents on the same page with regards to what machine learn­ing is, its appli­ca­tions of machine learn­ing in inter­ac­tion design, and com­mon chal­lenges encoun­tered. I still can’t think of any oth­er sin­gle resource that is as good a start­ing point for the sub­ject as this one.

Fiebrink, Rebec­ca. 2016. “Machine Learn­ing as Meta-Instru­ment: Human-Machine Part­ner­ships Shap­ing Expres­sive Instru­men­tal Cre­ation.” In Musi­cal Instru­ments in the 21st Cen­tu­ry, 14:137–51. Sin­ga­pore: Springer Sin­ga­pore. doi:10.1007/978–981–10–2951–6_10.

Fiebrink’s Wek­ina­tor is ground­break­ing, fun and inspir­ing so I had to include some of her writ­ing in this list. This is most­ly of inter­est for those look­ing into the use of machine learn­ing for design and oth­er cre­ative and artis­tic endeav­ours. An impor­tant idea explored here is that tools that make use of (inter­ac­tive, super­vised) machine learn­ing can be thought of as instru­ments. Using such a tool is like play­ing or per­form­ing, explor­ing a pos­si­bil­i­ty space, engag­ing in a dia­logue with the tool. For a tool to feel like an instru­ment requires a tight action-feed­back loop.

Dove, Gra­ham, Kim Hal­skov, Jodi For­l­izzi, and John Zim­mer­man. 2017. UX Design Inno­va­tion: Chal­lenges for Work­ing with Machine Learn­ing as a Design Mate­r­i­al. The 2017 CHI Con­fer­ence. New York, New York, USA: ACM. doi:10.1145/3025453.3025739.

A real­ly good sur­vey of how design­ers cur­rent­ly deal with machine learn­ing. Key take­aways include that in most cas­es, the appli­ca­tion of machine learn­ing is still engi­neer­ing-led as opposed to design-led, which ham­pers the cre­ation of non-obvi­ous machine learn­ing appli­ca­tions. It also makes it hard for design­ers to con­sid­er eth­i­cal impli­ca­tions of design choic­es. A key rea­son for this is that at the moment, pro­to­typ­ing with machine learn­ing is pro­hib­i­tive­ly cum­ber­some.

Fiebrink, Rebec­ca, Per­ry R Cook, and Dan True­man. 2011. “Human Mod­el Eval­u­a­tion in Inter­ac­tive Super­vised Learn­ing.” In, 147. New York, New York, USA: ACM Press. doi:10.1145/1978942.1978965.

The sec­ond Fiebrink piece in this list, which is more of a deep dive into how peo­ple use Wek­ina­tor. As with the chap­ter list­ed above this is required read­ing for those work­ing on design tools which make use of inter­ac­tive machine learn­ing. An impor­tant find­ing here is that users of intel­li­gent design tools might have very dif­fer­ent cri­te­ria for eval­u­at­ing the ‘cor­rect­ness’ of a trained mod­el than engi­neers do. Such cri­te­ria are like­ly sub­jec­tive and eval­u­a­tion requires first-hand use of the mod­el in real time.

Bostrom, Nick, and Eliez­er Yud­kowsky. 2014. “The Ethics of Arti­fi­cial Intel­li­gence.” In The Cam­bridge Hand­book of Arti­fi­cial Intel­li­gence, edit­ed by Kei­th Frank­ish and William M Ram­sey, 316–34. Cam­bridge: Cam­bridge Uni­ver­si­ty Press. doi:10.1017/CBO9781139046855.020.

Bostrom is known for his some­what crazy but thought­pro­vok­ing book on super­in­tel­li­gence and although a large part of this chap­ter is about the ethics of gen­er­al arti­fi­cial intel­li­gence (which at the very least is still a way out), the first sec­tion dis­cuss­es the ethics of cur­rent “nar­row” arti­fi­cial intel­li­gence. It makes for a good check­list of things design­ers should keep in mind when they cre­ate new appli­ca­tions of machine learn­ing. Key insight: when a machine learn­ing sys­tem takes on work with social dimensions—tasks pre­vi­ous­ly per­formed by humans—the sys­tem inher­its its social require­ments.

Yang, Qian, John Zim­mer­man, Aaron Ste­in­feld, and Antho­ny Toma­sic. 2016. Plan­ning Adap­tive Mobile Expe­ri­ences When Wire­fram­ing. The 2016 ACM Con­fer­ence. New York, New York, USA: ACM. doi:10.1145/2901790.2901858.

Final­ly, a feet-in-the-mud explo­ration of what it actu­al­ly means to design for machine learn­ing with the tools most com­mon­ly used by design­ers today: draw­ings and dia­grams of var­i­ous sorts. In this case the focus is on using machine learn­ing to make an inter­face adap­tive. It includes an inter­est­ing dis­cus­sion of how to bal­ance the use of implic­it and explic­it user inputs for adap­ta­tion, and how to deal with infer­ence errors. Once again the lim­i­ta­tions of cur­rent sketch­ing and pro­to­typ­ing tools is men­tioned, and relat­ed to the need for design­ers to devel­op tac­it knowl­edge about machine learn­ing. Such tac­it knowl­edge will only be gained when design­ers can work with machine learn­ing in a hands-on man­ner.

Supplemental material

Floyd, Chris­tiane. 1984. “A Sys­tem­at­ic Look at Pro­to­typ­ing.” In Approach­es to Pro­to­typ­ing, 1–18. Berlin, Hei­del­berg: Springer Berlin Hei­del­berg. doi:10.1007/978–3–642–69796–8_1.

I pro­vid­ed this to stu­dents so that they get some addi­tion­al ground­ing in the var­i­ous kinds of pro­to­typ­ing that are out there. It helps to pre­vent reduc­tive notions of pro­to­typ­ing, and it makes for a nice com­ple­ment to Buxton’s work on sketch­ing.

Ble­vis, E, Y Lim, and E Stolter­man. 2006. “Regard­ing Soft­ware as a Mate­r­i­al of Design.”

Some of the papers refer to machine learn­ing as a “design mate­r­i­al” and this paper helps to under­stand what that idea means. Soft­ware is a mate­r­i­al with­out qual­i­ties (it is extreme­ly mal­leable, it can sim­u­late near­ly any­thing). Yet, it helps to con­sid­er it as a phys­i­cal mate­r­i­al in the metaphor­i­cal sense because we can then apply ways of design think­ing and doing to soft­ware pro­gram­ming.

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.

Design × AI coffee meetup

If you work in the field of design or arti­fi­cial intel­li­gence and are inter­est­ed in explor­ing the oppor­tu­ni­ties at their inter­sec­tion, con­sid­er your­self invit­ed to an infor­mal cof­fee meet­up on Feb­ru­ary 15, 10am at Brix in Ams­ter­dam.

Erik van der Plui­jm and myself have for a while now been car­ry­ing on a con­ver­sa­tion about AI and design and we felt it was time to expand the cir­cle a bit. We are very curi­ous who else out there shares our excite­ment.

Ques­tions we are mulling over include: How does the design process change when cre­at­ing intel­li­gent prod­ucts? And: How can teams col­lab­o­rate with intel­li­gent design tools to solve prob­lems in new and inter­est­ing ways?

Any­way, lots to chew on.

No need to sign up or any­thing, just show up and we’ll see what hap­pens.

High-skill robots, low-skill workers

Some notes on what I think I under­stand about tech­nol­o­gy and inequal­i­ty.

Let’s start with an obvi­ous big ques­tion: is tech­nol­o­gy destroy­ing jobs faster than they can be replaced? On the long term the evi­dence isn’t strong. Humans always appear to invent new things to do. There is no rea­son this time around should be any dif­fer­ent.

But in the short term tech­nol­o­gy has con­tributed to an evap­o­ra­tion of mid-skilled jobs. Parts of these jobs are auto­mat­ed entire­ly, parts can be done by few­er peo­ple because of high­er pro­duc­tiv­i­ty gained from tech.

While pro­duc­tiv­i­ty con­tin­ues to grow, jobs are lag­ging behind. The year 2000 appears to have been a turn­ing point. “Some­thing” hap­pened around that time. But no-one knows exact­ly what.

My hunch is that we’ve seen an emer­gence of a new class of pseu­do-monop­o­lies. Oli­gop­o­lies. And this is com­pound­ed by a ‘win­ner takes all’ dynam­ic that tech­nol­o­gy seems to pro­duce.

Oth­ers have point­ed to glob­al­i­sa­tion but although this might be a con­tribut­ing fac­tor, the evi­dence does not sup­port the idea that it is the major cause.

So what are we left with?

His­tor­i­cal­ly, look­ing at pre­vi­ous tech­no­log­i­cal upsets, it appears edu­ca­tion makes a big dif­fer­ence. Peo­ple neg­a­tive­ly affect­ed by tech­no­log­i­cal progress should have access to good edu­ca­tion so that they have options. In the US the access to high qual­i­ty edu­ca­tion is not equal­ly divid­ed.

Appar­ent­ly fam­i­ly income is asso­ci­at­ed with edu­ca­tion­al achieve­ment. So if your fam­i­ly is rich, you are more like­ly to become a high skilled indi­vid­ual. And high skilled indi­vid­u­als are priv­i­leged by the tech econ­o­my.

And if Piketty’s is right, we are approach­ing a real­i­ty in which mon­ey made from wealth ris­es faster than wages. So there is a feed­back loop in place which only exac­er­bates the sit­u­a­tion.

One more bul­let: If you think trick­le-down eco­nom­ics, increas­ing the size of the pie will help, you might be mis­tak­en. It appears social mobil­i­ty is helped more by decreas­ing inequal­i­ty in the dis­tri­b­u­tion of income growth.

So some pre­lim­i­nary con­clu­sions: a pro­gres­sive tax on wealth won’t solve the issue. The edu­ca­tion sys­tem will require reform, too.

I think this is the cen­tral irony of the whole sit­u­a­tion: we are work­ing hard to teach machines how to learn. But we are neglect­ing to improve how peo­ple learn.

Move 37

Design­ers make choic­es. They should be able to pro­vide ratio­nales for those choic­es. (Although some­times they can’t.) Being able to explain the think­ing that went into a design move to your­self, your team­mates and clients is part of being a pro­fes­sion­al.

Move 37. This was the move Alpha­Go made which took every­one by sur­prise because it appeared so wrong at first.

The inter­est­ing thing is that in hind­sight it appeared Alpha­Go had good rea­sons for this move. Based on a cal­cu­la­tion of odds, basi­cal­ly.

If asked at the time, would Alpha­Go have been able to pro­vide this ratio­nale?

It’s a thing that pops up in a lot of the read­ing I am doing around AI. This idea of trans­paren­cy. In some fields you don’t just want an AI to pro­vide you with a deci­sion, but also with the argu­ments sup­port­ing that deci­sion. Obvi­ous exam­ples would include a sys­tem that helps diag­nose dis­ease. You want it to pro­vide more than just the diag­no­sis. Because if it turns out to be wrong, you want to be able to say why at the time you thought it was right. This is a social, cul­tur­al and also legal require­ment.

It’s inter­est­ing.

Although lives don’t depend on it, the same might apply to intel­li­gent design tools. If I am work­ing with a sys­tem and it is offer­ing me design direc­tions or solu­tions, I want to know why it is sug­gest­ing these things as well. Because my rea­son for pick­ing one over the oth­er depends not just on the sur­face lev­el prop­er­ties of the design but also the under­ly­ing rea­sons. It might be impor­tant because I need to be able to tell stake­hold­ers about it.

An added side effect of this is that a design­er work­ing with such a sys­tem is be exposed to machine rea­son­ing about design choic­es. This could inform their own future think­ing too.

Trans­par­ent AI might help peo­ple improve them­selves. A black box can’t teach you much about the craft it’s per­form­ing. Look­ing at out­comes can be inspi­ra­tional or help­ful, but the process­es that lead up to them can be equal­ly infor­ma­tive. If not more so.

Imag­ine work­ing with an intel­li­gent design tool and get­ting the equiv­a­lent of an Alpha­Go move 37 moment. Huge­ly inspi­ra­tional. Game chang­er.

This idea gets me much more excit­ed than automat­ing design tasks does.

Adapting intelligent tools for creativity

I read Alper’s book on con­ver­sa­tion­al user inter­faces over the week­end and was struck by this para­graph:

The holy grail of a con­ver­sa­tion­al sys­tem would be one that’s aware of itself — one that knows its own mod­el and inter­nal struc­ture and allows you to change all of that by talk­ing to it. Imag­ine being able to tell Siri to tone it down a bit with the jokes and that it would then actu­al­ly do that.”

His point stuck with me because I think this is of par­tic­u­lar impor­tance to cre­ative tools. These need to be flex­i­ble so that a vari­ety of peo­ple can use them in dif­fer­ent cir­cum­stances. This adapt­abil­i­ty is what lends a tool depth.

The depth I am think­ing of in cre­ative tools is sim­i­lar to the one in games, which appears to be derived from a kind of semi-ordered­ness. In short, you’re look­ing for a sweet spot between too sim­ple and too com­plex.

And of course, you need good defaults.

Back to adap­ta­tion. This can hap­pen in at least two ways on the inter­face lev­el: modal or mod­e­less. A sim­ple exam­ple of the for­mer would be to go into a pref­er­ences win­dow to change the behav­iour of your draw­ing pack­age. Sim­i­lar­ly, mod­e­less adap­ta­tion hap­pens when you rearrange some pan­els to bet­ter suit the task at hand.

Return­ing to Siri, the equiv­a­lence of mod­e­less adap­ta­tion would be to tell her to tone it down when her sense of humor irks you.

For the modal solu­tion, imag­ine a humor slid­er in a set­tings screen some­where. This would be a ter­ri­ble solu­tion because it offers a poor map­ping of a con­trol to a per­son­al­i­ty trait. Can you pin­point on a scale of 1 to 10 your pre­ferred amount of humor in your hypo­thet­i­cal per­son­al assis­tant? And any­way, doesn’t it depend on a lot of sit­u­a­tion­al things such as your mood, the par­tic­u­lar task you’re try­ing to com­plete and so on? In short, this requires some­thing more sit­u­at­ed and adap­tive.

So just being able to tell Siri to tone it down would be the equiv­a­lent of rear­rang­ing your Pho­to­shop palets. And in a next inter­ac­tion Siri might care­ful­ly try some humor again to gauge your response. And if you encour­age her, she might be more humor­ous again.

Enough about fun­ny Siri for now because it’s a bit of a sil­ly exam­ple.

Fun­ny Siri, although she’s a bit of a Sil­ly exam­ple, does illus­trate anoth­er prob­lem I am try­ing to wrap my head around. How does an intel­li­gent tool for cre­ativ­i­ty com­mu­ni­cate its inter­nal state? Because it is prob­a­bilis­tic, it can’t be eas­i­ly mapped to a graph­ic infor­ma­tion dis­play. And so our old way of manip­u­lat­ing state, and more specif­i­cal­ly adapt­ing a tool to our needs becomes very dif­fer­ent too.

It seems to be best for an intel­li­gent sys­tem to be open to sug­ges­tions from users about how to behave. Adapt­ing an intel­li­gent cre­ative tool is less like rear­rang­ing your work­space and more like coor­di­nat­ing with a cowork­er.

My ide­al is for this to be done in the same mode (and so using the same con­trols) as when doing the work itself. I expect this to allow for more flu­id inter­ac­tions, going back and forth between doing the work at hand, and meta-com­mu­ni­ca­tion about how the sys­tem sup­ports the work. I think if we look at how peo­ple col­lab­o­rate this hap­pens a lot, com­mu­ni­ca­tion and meta-com­mu­ni­ca­tion going on con­tin­u­ous­ly in the same chan­nels.

We don’t need a self-aware arti­fi­cial intel­li­gence to do this. We need to apply what com­put­er sci­en­tists call super­vised learn­ing. The basic idea is to pro­vide a sys­tem with exam­ple inputs and desired out­puts, and let it infer the nec­es­sary rules from them. If the results are unsat­is­fac­to­ry, you sim­ply con­tin­ue train­ing it until it per­forms well enough.

A super fun exam­ple of this approach is the Wek­ina­tor, a piece of machine learn­ing soft­ware for cre­at­ing musi­cal instru­ments. Below is a video in which Wekinator’s cre­ator Rebec­ca Fiebrink per­forms sev­er­al demos.

Here we have an intel­li­gent sys­tem learn­ing from exam­ples. A per­son manip­u­lat­ing data in stead of code to get to a par­tic­u­lar desired behav­iour. But what Wek­ina­tor lacks and what I expect will be required for this type of thing to real­ly catch on is for the train­ing to hap­pen in the same mode or medi­um as the per­for­mance. The tech­nol­o­gy seems to be get­ting there, but there are many inter­ac­tion design prob­lems remain­ing to be solved.

Generating UI design variations

AI design tool for UI design alternatives

I am still think­ing about AI and design. How is the design process of AI prod­ucts dif­fer­ent? How is the user expe­ri­ence of AI prod­ucts dif­fer­ent? Can design tools be improved with AI?

When it comes to improv­ing design tools with AI my start­ing point is game design and devel­op­ment. What fol­lows is a quick sketch of one idea, just to get it out of my sys­tem.

Mixed-ini­tia­tive’ tools for pro­ce­dur­al gen­er­a­tion (such as Tana­gra) allow design­ers to cre­ate high-lev­el struc­tures which a machine uses to pro­duce full-fledged game con­tent (such as lev­els). It hap­pens in a real-time. There is a con­tin­u­ous back-and-forth between design­er and machine.

Soft­ware user inter­faces, on mobile in par­tic­u­lar, are increas­ing­ly fre­quent­ly assem­bled from ready-made com­po­nents accord­ing to more or less well-described rules tak­en from design lan­guages such as Mate­r­i­al Design. These design lan­guages are cur­rent­ly pri­mar­i­ly described for human con­sump­tion. But it should be a small step to make a design lan­guage machine-read­able.

So I see an oppor­tu­ni­ty here where a design­er might assem­ble a UI like they do now, and a machine can do sev­er­al things. For exam­ple it can test for adher­ence to design lan­guage rules, sug­gest cor­rec­tions or even auto-cor­rect as the design­er works.

More inter­est­ing­ly, a machine might take one UI mock­up, and pro­vide the design­er with sev­er­al more pos­si­ble vari­a­tions. To do this it could use dif­fer­ent lay­outs, or alter­na­tive com­po­nents that serve a same or sim­i­lar pur­pose to the ones used.

In high pres­sure work envi­ron­ments where time is scarce, cor­ners are often cut in the diver­gence phase of design. Machines could aug­ment design­ers so that gen­er­at­ing many design alter­na­tives becomes less labo­ri­ous both men­tal­ly and phys­i­cal­ly. Ide­al­ly, machines would sur­prise and even inspire us. And the final say would still be ours.

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.

Artificial intelligence as partner

Some notes on arti­fi­cial intel­li­gence, tech­nol­o­gy as part­ner and relat­ed user inter­face design chal­lenges. Most­ly notes to self, not sure I am adding much to the debate. Just sum­maris­ing what I think is impor­tant to think about more. Warn­ing: Dense with links.

Matt Jones writes about how arti­fi­cial intel­li­gence does not have to be a slave, but can also be part­ner.

I’m per­son­al­ly much more inter­est­ed in machine intel­li­gence as human aug­men­ta­tion rather than the oft-hyped AI assis­tant as a sep­a­rate embod­i­ment.

I would add a third pos­si­bil­i­ty, which is AI as mas­ter. A com­mon fear we humans have and one I think only grow­ing as things like Alpha­Go and new Boston Dynam­ics robots keep hap­pen­ing.

I have had a tweet pinned to my time­line for a while now, which is a quote from Play Mat­ters.

tech­no­logy is not a ser­vant or a mas­ter but a source of expres­sion, a way of being”

So this idea actu­al­ly does not just apply to AI but to tech in gen­er­al. Of course, as tech gets smarter and more inde­pen­dent from humans, the idea of a ‘third way’ only grows in impor­tance.

More tweet­ing. A while back, short­ly after AlphaGo’s vic­to­ry, James tweet­ed:

On the one hand, we must insist, as Kas­parov did, on Advanced Go, and then Advanced Every­thing Else https://en.wikipedia.org/wiki/Advanced_Chess

Advanced Chess is a clear exam­ple of humans and AI part­ner­ing. And it is also an exam­ple of tech­nol­o­gy as a source of expres­sion and a way of being.

Also, in a WIRED arti­cle on Alpha­Go, some­one who had played the AI repeat­ed­ly says his game has improved tremen­dous­ly.

So that is the promise: Arti­fi­cial­ly intel­li­gent sys­tems which work togeth­er with humans for mutu­al ben­e­fit.

Now of course these AIs don’t just arrive into the world ful­ly formed. They are cre­at­ed by humans with par­tic­u­lar goals in mind. So there is a design com­po­nent there. We can design them to be part­ners but we can also design them to be mas­ters or slaves.

As an aside: Maybe AIs that make use of deep learn­ing are par­tic­u­lar­ly well suit­ed to this part­ner mod­el? I do not know enough about it to say for sure. But I was struck by this piece on why Google ditched Boston Dynam­ics. There appar­ent­ly is a sig­nif­i­cant dif­fer­ence between holis­tic and reduc­tion­ist approach­es, deep learn­ing being holis­tic. I imag­ine reduc­tion­ist AI might be more depen­dent on humans. But this is just wild spec­u­la­tion. I don’t know if there is any­thing there.

This insis­tence of James on “advanced every­thing else” is a world view. A pol­i­tics. To allow our­selves to be increas­ing­ly entan­gled with these sys­tems, to not be afraid of them. Because if we are afraid, we either want to sub­ju­gate them or they will sub­ju­gate us. It is also about not obscur­ing the sys­tems we are part of. This is a sen­ti­ment also expressed by James in the same series of tweets I quot­ed from ear­li­er:

These emer­gences are also the best mod­el we have ever built for describ­ing the true state of the world as it always already exists.

And there is over­lap here with ideas expressed by Kevin in ‘Design as Par­tic­i­pa­tion’:

[W]e are no longer just using com­put­ers. We are using com­put­ers to use the world. The obscured and com­plex code and engi­neer­ing now engages with peo­ple, resources, civics, com­mu­ni­ties and ecosys­tems. Should design­ers con­tin­ue to priv­i­lege users above all oth­ers in the sys­tem? What would it mean to design for par­tic­i­pants instead? For all the par­tic­i­pants?

AI part­ners might help us to bet­ter see the sys­tems the world is made up of and engage with them more deeply. This hope is expressed by Matt Webb, too:

with the re-emer­gence of arti­fi­cial intel­li­gence (only this time with a bud­dy-style user inter­face that actu­al­ly works), this ques­tion of “doing some­thing for me” vs “allow­ing me to do even more” is going to get even more pro­nounced. Both are effec­tive, but the first sucks… or at least, it sucks accord­ing to my own per­son­al pol­i­tics, because I regard indi­vid­ual alien­ation from soci­ety and com­plex sys­tems as one of the huge threats in the 21st cen­tu­ry.

I am remind­ed of the mixed-ini­tia­tive sys­tems being researched in the area of pro­ce­dur­al con­tent gen­er­a­tion for games. I wrote about these a while back on the Hub­bub blog. Such sys­tems are part­ners of design­ers. They give some­thing like super pow­ers. Now imag­ine such pow­ers applied to oth­er prob­lems. Quite excit­ing.

Actu­al­ly, in the afore­men­tioned arti­cle I dis­tin­guish between tools for mak­ing things and tools for inspect­ing pos­si­bil­i­ty spaces. In the first case design­ers manip­u­late more abstract rep­re­sen­ta­tions of the intend­ed out­come and the sys­tem gen­er­ates the actu­al out­put. In the sec­ond case the sys­tem visu­alis­es the range of pos­si­ble out­comes giv­en a par­tic­u­lar con­fig­u­ra­tion of the abstract rep­re­sen­ta­tion. These two are best paired.

From a design per­spec­tive, a lot remains to be fig­ured out. If I look at those mixed-ini­tia­tive tools I am struck by how poor­ly they com­mu­ni­cate what the AI is doing and what its capa­bil­i­ties are. There is a huge user inter­face design chal­lenge there.

For stuff focused on get­ting infor­ma­tion, a con­ver­sa­tion­al UI seems to be the cur­rent local opti­mum for work­ing with an AI. But for tools for cre­ativ­i­ty, to use the two-way split pro­posed by Vic­tor, dif­fer­ent UIs will be required.

What shape will they take? What visu­al lan­guage do we need to express the par­tic­u­lar prop­er­ties of arti­fi­cial intel­li­gence? What approach­es can we take in addi­tion to per­son­i­fy­ing AI as bots or char­ac­ters? I don’t know and I can hard­ly think of any good exam­ples that point towards promis­ing approach­es. Lots to be done.