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.

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.