Tega Brain: Hi, every­one. I’m Tega. I’m an artist and envi­ron­men­tal engi­neer. And I call what I do eccen­tric engi­neer­ing. So, I do exper­i­ments with data and tech­nolo­gies and com­put­ers that ask ques­tions of how we design and engi­neer the sys­tems with­in which we live.

I’m par­tic­u­lar­ly con­cerned with how to design from the dystopi­an posi­tion of the Anthropocene. How to make cre­ative work that is simul­ta­ne­ous­ly crit­i­cal and gen­er­a­tive. We’re scram­bling to fig­ure out what it means to have moved an enor­mous amount of car­bon from the ground into the atmos­phere large­ly via com­bus­tion. And so this talk con­sid­ers some of my work deal­ing with envi­ron­men­tal data and a chang­ing cli­mate, and hope­ful­ly points to some of what is at stake as we col­lec­tive­ly face this. What does it mean to have aug­ment­ed the atmos­phere and the ocean’s capac­i­ty to absorb heat? And to be rapid­ly increas­ing the entropy of those systems? 

The term entropy of course has two def­i­n­i­tions, one in ther­mo­dy­nam­ics and one infor­ma­tion the­o­ry. So to start with the ther­mo­dy­nam­ic def­i­n­i­tion, entropy is the ran­dom­ness of the con­stituents in a sys­tem. In oth­er words, if you heat or increase the tem­per­a­ture of a gas, it increas­es its entropy, its randomness.

And so what ways can we see this in our shared envi­ron­men­tal sys­tems? So first of all, we can look at the bio­log­i­cal inter­faces that sur­round us and how they change through time. This is the study of phe­nol­o­gy. Phenology’s the tim­ing of recur­ring bio­log­i­cal events such as flow­er­ing and migra­to­ry pat­terns in dif­fer­ent ecosys­tems. And it’s of great inter­est to researchers because it’s show­ing how the bios­phere is chang­ing with cli­mate change. Observing phe­nol­o­gy is to observe a com­plex, rhyth­mic, and cycli­cal rela­tion­ship between tem­per­a­ture and time.

The old­est writ­ten bio­log­i­cal data set on record is the cher­ry blos­soms bloom in Kyoto, Japan. This is of inter­est because it her­alds the begin­ning of the cher­ry blos­som fes­ti­val, and it’s being pieced togeth­er since 1800 AD, from the records of emper­ors ands aris­to­crats in Japanese history.

This has become an impor­tant cli­mate record because the cher­ry blos­som bloom starts after a series of warm days at the begin­ning of spring. And so it’s very clear from look­ing at this record that the bloom is becom­ing soon­er and hap­pen­ing soon­er and soon­er in the year as our sys­tem is warm­ing up.

So look­ing at some of these phe­nol­o­gy data sets is at the heart of this project, which was a col­lab­o­ra­tive project I did at the Environmental Health Clinic at NYU called The Phenology Clock.” This is visu­al­iza­tion soft­ware that visu­al­izes phe­nol­o­gy data from dif­fer­ent ecosys­tems. And it pro­duces twelve-month clocks. So January’s at the twelve o’clock mark. And each band of col­or shows a flow­er­ing pat­tern from a par­tic­u­lar species of plant. So it shows the dura­tion of that flow­er­ing event. 

And what is revealed is tem­po­ral rela­tion­ships with­in the ecosys­tem. So you can see Sydney on the left, flow­ers all year round. New York on the right, like noth­ing hap­pens for six months of the yeah, right? As an Australian I’m hor­ri­fied by this.

So I’ve used this soft­ware to look at a num­ber of phe­nol­o­gy data sets from dif­fer­ent places, and here is a clock gen­er­at­ed from a fam­i­ly of euca­lypt trees. And what’s amaz­ing about this image is you’ll notice that there’s always five or six species in flower at any one time. Why would this be? Why would a fam­i­ly of trees dis­trib­ute them­selves tem­po­ral­ly through­out the year?

The rea­son being is it’s thought that they’re coe­volved with this species, the fly­ing fox. The fly­ing fox is the dat­ing ser­vice for the trees; it pol­li­nates them. And the trees pro­vide a food source. So, by tem­po­ral­ly dis­trib­ut­ing flow­er­ing pat­terns, this guy has a food source through­out the year.

Plants medi­ate the atmos­phere. Increasing entropy flows on to desta­bi­lize mutu­al­is­tic rela­tion­ships such as these. Increasing over­all tem­per­a­ture increas­es the unpre­dictabil­i­ty of these species synchronicities.

Studies of the Jacaranda, the Cowslip Orchid, the Texan Blue Bonnett and the Sturt’s Desert Pea for the peri­od from 20022013. from Keeping Time

A project fol­low­ing this, also explor­ing phe­nol­o­gy was a project I did scrap­ing the Flickr data­base, look­ing to see if I could see these pat­terns in messy, crowd­sourced data. The result­ing images are made up of thou­sands of images that are tagged with par­tic­u­lar species name and laid out accord­ing to time­stamp. So January on the left, December on the right, and each band is a year. And here we can see a very clear Southern hemi­sphere flow­er­ing pat­tern. This is the jacaran­da, which is an Australian flower.

But these images are large­ly made up of things that look like this, right, a Texan fam­i­ly goes into the field to be pho­tographed with the blue­bon­nets because you’re not allowed to pick them. So these images actu­al­ly became… What stood out for me was they were about how we actu­al­ly relate to these dif­fer­ent species, how we see them and how we use a data­base such as Flickr. They’re as much a result of our social rela­tion­ships with species as they are index­es of a chang­ing environment. 

Environmental obser­va­tion has of course become increas­ing­ly com­pu­ta­tion­al. And we no longer rely on direct obser­va­tion of the bios­phere to under­stand cli­mate. Instead, we use a net­work of satel­lites, weath­er sta­tions, data cen­ters and so forth to do this. We process unthink­able amounts of data, and our com­pu­ta­tion­al sys­tems burn up mil­lions of dol­lars of elec­tric­i­ty every month to do this, pro­duc­ing enor­mous amounts of heat. The trans­fer of infor­ma­tion can­not take place with­out a cer­tain expen­di­ture of ener­gy, which is what Norbert Wiener said, the father of cybernetics.

Processing data is nev­er ther­mo­dy­nam­i­cal­ly neu­tral. Any active orga­niz­ing push­es against a ten­den­cy for every­thing to degrade, and cool­ing has always set the lim­its on com­pu­ta­tion­al design. Computation is about man­ag­ing heat. It deter­mines how dense­ly com­po­nents can be packed and where data cen­ters are to be built. If you put them in the Arctic, it real­ly reduces your ener­gy costs.

In oth­er words, cyber­space is hot. Computing is an exother­mic reac­tion. Of course, despite the over­due push to increase the use of renew­ables, data cen­ters and com­put­ers are still run on coal and use [?]. And they use some­where between 13% of our elec­tri­cal out­put, and this is ris­ing. Every video, image, Google search we make has an envi­ron­men­tal effect. Every com­pu­ta­tion­al automa­tion, every machine learn­ing inno­va­tion, relies on com­bus­tion some­where. Shouldn’t these costs be con­sid­ered in how we assess the suc­cess or fail­ures of our com­pu­ta­tion­al sys­tems? And how might we rethink our net­work inter­faces to make these mate­r­i­al costs more tangible?

These ques­tions are posed by a series of exper­i­men­tal WiFi routers, eccen­tric WiFi routers that I made in a series called Radiotropisms. This one, called Open Flame is a router that is paired with a can­dle. To bring up the wire­less net­work, you have to light the can­dle. When you blow it out, your net­work dis­ap­pears. Wax is laid down over time, depend­ing on your online life. 

Each WiFi router also offers a net­work. Another one in the series, An Orbit, oscil­lates its sig­nal strength with the orbit of the moon. So for one day a month you get real­ly strong, great Internet, and for one day a month you get none. And it changes over a twenty-eight day peri­od. Again, how might we invite our envi­ron­men­tal sys­tems into our networks?

Finally, this is a WiFi router con­trolled by a house­plant. And the plan is equipped with a cam­era. It can take pho­tos of itself and replace images in your net­work feed. So any unen­crypt­ed data ends up with a pho­to of this plant.

So those are provo­ca­tions. How can design of our net­work inter­face empha­size our ecol­o­gy rather than hide it behind seam­less user interfaces?

So to con­clude, I want to return to this term entropy.” But I want to con­sid­er it from the point of view of infor­ma­tion the­o­ry. In infor­ma­tion the­o­ry, entropy is a mea­sure of the loss of infor­ma­tion con­tent in a sig­nal or a sys­tem. A term devel­oped by the father of infor­ma­tion the­o­ry Claude Shannon to describe the prob­a­bilis­tic mea­sure of uncer­tain­ty in a system. 

Or to break it down, what does this mean? It’s ambigu­ous. If we think about the entropy of a coin, we can get either a head or a tail when we toss it, right. This is known as one Shannon of entropy. If the coin was to have two heads on both sides, the entropy of the sys­tem is zero because it’s com­plete­ly pre­dictable what the out­come would be. If the coin was to have three or four or more faces, the amount of infor­ma­tion con­tained in the sys­tem is greater, and so the entropy of the sys­tem is greater because it’s hard­er to predict.

The ulti­mate infor­ma­tion sci­ence is mete­o­rol­o­gy, and the project of weath­er pre­dic­tion is an ongo­ing attempt to low­er the entropy of our weath­er sys­tem, in an infor­ma­tion­al sense. We use data to build mod­els to hope­ful­ly bet­ter pre­dict the behav­ior of our sys­tem. And as his­to­ri­ans like Paul Edwards have shown, cli­mate pre­dic­tion is inti­mate­ly tied with the devel­op­ment of plan­e­tary com­pu­ta­tion. Computer resources are always inef­fi­cient in mete­o­rol­o­gy, and it is the ulti­mate big data sci­ence. And to me there’s a sort of dark irony that we’ve built sophis­ti­cat­ed glob­al sys­tems for col­lect­ing Earth and cli­mate obser­va­tions to pre­dict cli­mate, at the very time where our col­lec­tive impacts on human soci­ety are active­ly desta­bi­liz­ing it.

One con­se­quence of this could be observed ear­li­er this year at Oroville Dam in California, when the catch­ment received more than twice its annu­al rain­fall last win­ter. As the emer­gency spill­way was engaged for the first time in the forty-nine-year life of the dam, it began to erode, the engi­neers became con­cerned, and 200,000 peo­ple were evac­u­at­ed from the water­shed with less than an hour’s notice. Although the inci­dent is indeed a fail­ure in ade­quate engi­neer­ing and main­te­nance of that spill­way, to call it sole­ly an engi­neer­ing prob­lem over­looks it also as being a cli­mate problem. 

So, a water engi­neer’s job is to size stuff. The height of a dam, the size of a cul­vert, the depth of the drain…these things are sized to pre­vent flood­ing at cer­tain storm inter­vals. You gen­er­al­ly think about how big your catch­ment is upstream of the thing you’re design­ing. You think about what sort of rain pat­terns are going to hap­pen in that catch­ment and how fre­quent­ly it’s okay for it to flood. The best prac­tice guide­lines in stormwa­ter mod­el­ing used to be to mod­el ten rain­fall pat­terns for each thing you’re design­ing. And just this year this was upgrad­ed to twen­ty, so this is like dou­bling the amount of human labor and com­pu­ta­tion­al work that’s going into mod­el­ing for these sorts of water infrastructures.

So what’s crit­i­cal here is that at best we only have about 150 years of rain­fall data. Much less in some regions. And this is being tak­en from a sta­ble, pre­dictable cli­mate. At the heart of this sort of engi­neer­ing is an assump­tion that past rain is a good indi­ca­tion of future rain­fall, of future weath­er. What hap­pened at Oroville is out­side of what the his­tor­i­cal data would have indi­cat­ed could have hap­pened. They got a lot more rain because their mod­el said that this would fall as snow rather than rain in the catch­ment. And so call­ing Oroville an engi­neer­ing fail­ure is actu­al­ly not accu­rate because it’s also a fail­ure in our abil­i­ty to pre­dict climate.

So the work of a water engi­neer’s become increas­ing­ly dif­fi­cult as pre­cip­i­ta­tion data drawn from a sta­ble cli­mate is becom­ing less and less indica­tive of things to come. And the stakes are high. If you look around you’ll notice that all of our infra­struc­tures, our dams, our cul­verts, but also our media and com­pu­ta­tion­al tech­nolo­gies, are specif­i­cal­ly cal­i­brat­ed to sta­ble con­di­tions of the past 12,000 years. And cli­mate change promis­es to decou­ple us and these sys­tems from their cli­mac­tic nich­es. Even with the rapid advance of com­pu­ta­tion, cli­mate desta­bi­liz­ing is increas­ing the entropy of our sys­tem and reduc­ing our capac­i­ty to pre­dict it. And with all of our tech­nolo­gies, we need to be con­sid­er­ing their increas­ing ener­gy demands and their cli­mate affor­dances. Thank you.

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