Samim Winiger: Welcome to Ethical Machines. We are your hosts…

Roelof Pieters: Roelof.

Winiger: And Samim.

Pieters: Ethical Machines is a series of con­ver­sa­tions about humans, machines, and ethics. It aims at start­ing a deep­er, better-informed debate about impli­ca­tions of intel­li­gent sys­tems for soci­ety and individuals.

Winiger: For this episode, we invit­ed David J. Klein to talk to us about machine learn­ing, con­ser­va­tion, and cli­mate change. Let’s dive in.

Thanks for mak­ing the time. Welcome to Ethical Machines. It’s a plea­sure to have you on. Maybe we can start with the obvi­ous ques­tion, could you tell us who you are, your back­ground, and what brings you here, basically.

David J. Klein: Sure. Well, I grew up on a ranch in Florida, and I spent many years sort of mar­veling at nature. At the same time I was a huge sci­ence fic­tion fan, so I’d come back in and take apart all of my motor­ized toys and put them back togeth­er, and watch Doctor Who and read Asimov and Bradbury.

I even­tu­al­ly decid­ed to go in a tech­nol­o­gy direc­tion in my career, although I could’ve eas­i­ly gone in a dif­fer­ent direc­tion. And I went to Georgia Tech. But after a while I became increas­ing­ly unin­spired by the work I was learn­ing about in elec­tri­cal engi­neer­ing. So I start­ed look­ing for a way to keep myself inter­est­ed. So I was tak­ing cours­es in psy­chol­o­gy and genet­ics, and a cou­ple of real­ly impor­tant things hap­pened as I was searching. 

First of all, I hap­pened upon a course called Sensory Ecology. It was taught by a pro­fes­sor at Georgia Tech; the name of that the pro­fes­sor was David Dusenbery. Sensory Ecology is real­ly about infor­ma­tion trans­mis­sion in bio­log­i­cal sys­tems, and how behav­ior and mor­phol­o­gy of organ­isms coe­volves with their infor­ma­tion trans­mis­sion and recep­tion sys­tems. And I was huge­ly inspired by that course.

And so I start­ed look­ing at ways of com­bin­ing Double E with stud­ies of the brain. So the sec­ond impor­tant event was I asked around and I found a young pro­fes­sor at Georgia Tech who had recent­ly come there out of the lab of Carver Mead from Caltech. And Carver Mead and his stu­dents were the pio­neers of this field of neu­ro­mor­phic engi­neer­ing. And so I was able to land an under­grad­u­ate research assist­ant­ship in Steve’s lab, and I was doing research and devel­op­ment on neu­ro­mor­phic vision chips. So we were design­ing vision chips that were mim­ic­k­ing the pro­cess­ing being done in the mam­malian retina.

And real­ly since then, every­thing I’ve done has been in that inter­sec­tion area between neu­ro­science and engi­neer­ing. My grad­u­ate work was in a Double E lab of Shihab Shamma’s in University Maryland, but we were doing exper­i­men­tal neu­ro­science there, study­ing the pro­cess­ing of sound in the audi­to­ry cor­tex. From there on I was a post­doc­tor­al researcher at the Institute for Neuroinformatics in Zurich. So I was work­ing on audi­to­ry AI projects there an audi­to­ry rep­re­sen­ta­tion learning. 

And then I came out to Silicon Valley about a decade ago and I joined a com­pa­ny Audience, where we had the vision of reverse engi­neer­ing the human audi­to­ry sys­tem in order to do a bet­ter job of speech enhance­ment and audi­to­ry source sep­a­ra­tion and robust speech recog­ni­tion. And we devel­oped a chip that went into the iPhone and went into the Samsung Galaxy, and it was a great suc­cess. We were the first com­pa­ny to pull mul­ti­ple micro­phones into a smart­phone. And as these sig­nals are com­ing in they’re first trans­formed by com­pu­ta­tion­al mod­els of the mam­malian cochlea. So not using a Fourier trans­form but actu­al­ly using a fil­ter back inspired by the mam­malian cochlea.

So from there on, I’ve been work­ing on var­i­ous projects in var­i­ous star­tups includ­ing my own. I was using autoen­coders start­ing in 2008 to beat the state of the art stan­dards in video com­pres­sion. And I’ve been work­ing on a bunch of dif­fer­ent projects as a con­sul­tant, adding most­ly deep learning-fueled intel­li­gence to var­i­ous prod­ucts rang­ing from face recog­ni­tion to snor­ing recognition.

So on the con­ser­va­tion side, I was lucky to get con­nect­ed to these researchers at University California Santa Cruz. They were start­ing a com­pa­ny sev­er­al years ago which is called Conservation Metrics. And this com­pa­ny, it was based on their work apply­ing pas­sive acoustic mon­i­tor­ing tech­nol­o­gy to mon­i­tor and help save endan­gered sea birds, most­ly. Over time, I devel­oped tech­nol­o­gy for them and now they have this analy­sis pipeline for all the acoustic data that they get in. So the biol­o­gists who are ana­lysts in the com­pa­ny have the abil­i­ty to build deep learn­ing mod­els to detect endan­gered species of inter­est and get to a more detailed under­stand­ing of how these pop­u­la­tions are doing and how they’re respond­ing to con­ser­va­tion interventions.

And so that’s been excit­ing in that it’s had a very large impact on their work. Their analy­sis through­put has increased by ten times com­pared to meth­ods they were using before using deep learn­ing mod­els. And I think we’re just scratch­ing the sur­face. We’ve expand­ed from audio pro­cess­ing to image pro­cess­ing, most­ly land-based cam­era net­works that are used by con­ser­va­tion­ists today. And there’s a lot of poten­tial. I mean, the vision going beyond that is inte­grat­ing all kinds of sen­sor sources, all the way from envi­ron­men­tal DNA sam­pling all the way up to satellite-based imagery. All of these sen­sor types have a bear­ing on the wildlife con­ser­va­tion and more broad­ly envi­ron­men­tal con­ser­va­tion problem. 

Winiger: So fol­low­ing up from there, the research paper that you pub­lished a while back called Deep Learning for Large Scale Biodiversity Monitoring”. How does this play into the work you just mentioned?

Klein: It real­ly has to do with the vision. So Conservation Metrics, we’ve been solv­ing very spe­cif­ic prob­lems in the con­ser­va­tion sec­tor using deep learn­ing. And it’s great to be able to work with con­ser­va­tion sci­en­tists and their exist­ing projects today and to see what prob­lems they have and how can the process be stream­lined using machine intel­li­gence. And that’s why Conservation Metrics was labeled as a laser” in this recent TechCrunch arti­cle by Shivon Zilis. We’re very much focused on spe­cif­ic prob­lems that exist in projects today.

But there’s this broad­er vision, the idea that we can lever­age these sen­sor net­works that we’re putting out in these remote areas. So we have these things across the world. I mean, we have them in Australia, we have them in Hawaiʻi, we have them in coastal areas of the United States. And there’s at least an order of mag­ni­tude greater need for mon­i­tor­ing. I mean right now we’re using these micro­phones and cam­eras net­works on the ground, but the con­ser­va­tion sec­tor believes that there’s a lot of val­ue that can be gleaned from for exam­ple satel­lite imagery or DNA sam­pling called eDNA. 

If we real­ly want to have a detailed enough under­stand­ing of these ecosys­tems so that we can real­ly engi­neer solu­tions on less than a ten-year run­way— I mean, right now we don’t real­ly have that under­stand­ing. It’s actu­al­ly one of the most impor­tant insights that I’ve got­ten in work­ing with biol­o­gists and ecol­o­gists, is that today it’s actu­al­ly not real­ly known on a sci­en­tif­ic basis how well dif­fer­ent con­ser­va­tion inter­ven­tions will work. And it’s because we just don’t have a lot of data. I mean, these con­ser­va­tion projects—think about try­ing to save pop­u­la­tions of endan­gered sea birds that might feed in islands close to Japan but breed in islands close to Hawaiʻi. I mean it’s a huge area; there’s no way you can send peo­ple out there to get enough data to devel­op a sci­en­tif­ic under­stand­ing of the prob­lems. How these species are being impact­ed by human actions.

So we need tech­nol­o­gy, we need to deploy sen­sors and many dif­fer­ent types of sen­sors to mon­i­tor these pop­u­la­tions and mon­i­tor these ecosys­tems. And we need algo­rithms like deep learning-based algo­rithms that we can use to dis­till insights from this data. Because it’s way too much data. I mean, step one is get­ting the data but it’s way way too much data for peo­ple to look at direct­ly. We have a project in Kauaʻi where we’re detect­ing the sound of an endan­gered bird col­lid­ing with pow­er lines there. And we’ve dis­cov­ered that it’s a much big­ger prob­lem than it was pre­vi­ous­ly thought because we were able to extend the tem­po­ral scale of the mon­i­tor­ing using these micro­phone networks. 

When we get data back from the lab, it’s hun­dreds of thou­sands of hours of audio. It would take a sin­gle per­son ten years just to lis­ten to that, let alone find things of inter­est. So that’s where we’re apply­ing deep learn­ing, where we’re enabling these biol­o­gists through var­i­ous inter­est­ing means to build mod­els and then dis­till these hun­dreds of thou­sands of hours, or many mil­lions of images, down to a small sub­set that they can use in their back­end analy­sis of how pop­u­la­tion den­si­ties are changing.

Pieters: One more ques­tion, like how this relates not just to extinc­tion of glob­al ani­mal pop­u­la­tions but also to things like the state of bio­di­ver­si­ty or cli­mate change? Shat would you gen­er­al­ly say is the impact machine intel­li­gence in a kind of broad sense can have in this area, and is it already hav­ing enough impact?

Klein: The sit­u­a­tion appears to be very dire. I mean, if you look at global.biodiversity[?], the last about half of the world’s ani­mal pop­u­la­tions since 1970, species extinc­tions are orders of mag­ni­tude above the nat­ur­al back­ground rate. And a lot of sci­en­tists are call­ing this the sixth extinc­tion, and there’s no debat­ing that it’s due to human caus­es. And it’s a bunch of dif­fer­ent human caus­es. The num­ber one cause today is not cli­mate change, it’s direct exploita­tion. It’s farm­ing, it’s fish­ing. Then we have pol­lu­tion. Amphibians and birds are being just wiped out. Insect pop­u­la­tions as well.

And you know, glob­al spend­ing on this has increased in recent decades. So the UN has rec­og­nized that this is going down­hill at an alarm­ing rate, and right now we’re spend­ing about $20 bil­lion a year glob­al­ly. But all met­rics are show­ing that it’s not real­ly help­ing. When you ask, Well why isn’t it help­ing? What could we be doing bet­ter?” And there’s not real­ly great answers out there right now. Funding is being dri­ven by emo­tion and log­ic and some mod­els. But it’s not real­ly being based on data. You know, we can argue that cer­tain species that we care about because they’re cute, or we can make impas­sioned arguments. 

And that’s real­ly the kind of thing that’s dri­ving mon­ey flow today. But there’s not a lot of data that’s show­ing us how well we’re doing with a par­tic­u­lar kind of inter­ven­tion ver­sus anoth­er kind. Let’s talk about birds again. You know, should we remove an inva­sive snake or we should we build arti­fi­cial nests? Which one of those is more effec­tive? Usually we just argue for one, we do it, and then many years lat­er we deter­mine if it worked or not. 

So your ques­tion’s about how could tech­nol­o­gy help. So, we do expect that cli­mate change will become the num­ber one prob­lem, pret­ty quick­ly. Because it’s shift­ing habi­tats at a rate that nat­ur­al ecosys­tems can­not keep up with. We’re erod­ing the val­ue of nature. So we can get into how do we mea­sure the val­ue of nature. Actually that’s a real­ly inter­est­ing topic. 

The two things we can do obvi­ous­ly on the tech­nol­o­gy side, one is try­ing to slow cli­mate change. And we can do that by var­i­ous means. We can inno­vate on tech­nol­o­gy for ener­gy; clean tech­nol­o­gy that is much less destruc­tive to our atmos­phere. We can inno­vate on solu­tions for trans­porta­tion that uses less ener­gy. So that’s one side of things, try­ing to slow the degradation.

And the oth­er side of things, where I’ve been more focused, is devel­op­ing sys­tems that enable sci­en­tists to under­stand these sys­tems that we’re mod­i­fy­ing so that as we make con­ser­va­tion inter­ven­tions we can say on a more fine-grained basis how well they’re work­ing. Ultimately, we might need to under­stand these sys­tems so that we can restore them. 

Winiger: I mean, the ques­tion you just raised, how do you val­ue nature. I want to dive into that as you brought it. What is the cost func­tion of valu­ing nature? It seems like a real­ly hard prob­lem to crack. Is there any think­ing around this?

Klein: There’s been quite a bit of work in this area called ecosys­tem ser­vices. It’s econ­o­mists and biol­o­gists, ecol­o­gists, are get­ting togeth­er and per­form­ing an increas­ing­ly detailed account of how nature serves us. You know, what more tan­gi­ble val­ue do we derive from ecosys­tems? And there’s a bunch of dif­fer­ent ways. If you look at how we use bee pop­u­la­tions to pol­li­nate crops and all the val­ue we would get from those crops. The fact that ecosys­tems form nat­ur­al buffers that pro­tect our pop­u­la­tions from storms. The fact that trees take a lot of car­bon out of the atmos­phere and there­fore reg­u­late our plan­e­tary tem­per­a­ture. And many many many oth­er ways. I mean, we even derive pes­ti­cides and med­i­cines from nature. So if you add all that up, we’re cur­rent­ly at an esti­mate of around $125 tril­lion a year of val­ue that we’re extract­ing from nature. That’s rough­ly dou­ble glob­al GDP

So that work is going on. But of course there’s a great debate about ecosys­tem ser­vices. You know, can you actu­al­ly quan­ti­fy? Because a lot of peo­ple will say, A future with no nature is not a future I want to be in.” How do you quan­ti­fy life? That’s a real­ly great ques­tion. I’m not aware of work beyond brain­storm­ing. When you start look­ing at the uses of rein­force­ment learn­ing for mon­i­tor­ing and maybe main­tain­ing nat­ur­al sys­tems, it does beg the ques­tion okay, what’s the rein­force­ment sig­nal? What are the objec­tive func­tions here that are being opti­mized? And I don’t have a great answer for that. But it’s a great area of debate and dis­cus­sion because that may be one of the only solu­tions we have.

The approach that I’ve been tak­ing right now is okay, we’re get­ting in these petabytes of data from sen­sors and we’re get­ting that down to a very very small sub­set. But that may not end up work­ing out. The scale of the prob­lem may be too large. I mean, how many hun­dreds of tril­lions of dol­lars are we going to have to spend to restore these sys­tems? I think the much bet­ter approach would be to let these sys­tems care of themselves. 

But what is the objec­tive func­tion? It’s not just the pres­ence of activ­i­ty, like life activ­i­ty. One of the great exam­ples is the con­cept of the eco­log­i­cal trap. So, we have areas like Central Park in New York which were thought to be eco­log­i­cal traps. So it attracts ani­mals, there’s a lot of life there. But there’s not a lot of renew­al of life; it’s kind of a dead end. So if we just designed a rein­force­ment learn­ing sys­tem to say okay, let’s find auto­mat­ic actions that will increase the diver­si­ty and plen­ti­ful­ness of life in a cer­tain loca­tion, that in itself is not enough. We need to have a much more detailed under­stand­ing of what is a healthy ecosys­tem. There’s always a bal­ance there. Today we don’t have a detailed sci­en­tif­ic under­stand­ing. We’re just scratch­ing the sur­face. So that’s why I’m excit­ed about devel­op­ing tech­nol­o­gy that can help us see what’s going on.

Pieters: What would you say, because one of the argu­ments being made, the Singularity will take care of it, Moore’s Law will auto­mat­i­cal­ly solve cli­mate change, ani­mal extinc­tion and relat­ed prob­lems. Or almost like the invis­i­ble hand of the mar­ket will fix it. You know—

Klein: Yeah.

Pieters: So what would you say to these kind of…

Klein: Yeah… I think that’s… I think it’s pret­ty dan­ger­ous think­ing. I mean you know, can any­body point to any kind of tech­nol­o­gy pro­jec­tion of more than thir­ty years that’s end­ed up being accu­rate in any sig­nif­i­cant way, any action­able way? I mean, why do we think this is dif­fer­ent now? Basing our future on a wait-and-see atti­tude, it’s like…it’s just dan­ger­ous. I think prob­lems like this are going to be solved with a lot of work, and work on all these three things: tech­nol­o­gy, sci­ence, and pol­i­tics you know, pol­i­cy. We need to tack­le all these prob­lems in a very method­i­cal and a coor­di­nat­ed way. And the thing is even as we fail—there’ll be a lot of fail­ures, but we’ll be learn­ing a lot, and we’ll be cre­at­ing an under­stand­ing that will be much more broad­ly use­ful for humankind. So the idea that tech­nol­o­gy inno­va­tion is passed down humans from the moun­tain and it’s just going to solve every­thing to me does­n’t ring true.

Winiger: You hint­ed at pol­i­tics and pol­i­cy­mak­ing, and I’m going to frame that as culture.

Klein: Yeah.

Winiger: This notion that large-scale change will hap­pen, it’s tech­no­log­i­cal change and cul­tur­al change go hand in hand. And so do you actu­al­ly see machine learn­ing help change our cul­ture? So our beliefs, our caus­es, our priorities.

Klein: That’s such an inter­est­ing ques­tion. I think that as we get a more detailed under­stand­ing of nat­ur­al ecosys­tems, in part by attack­ing this prob­lem, that we’ll start to be able to have the abil­i­ty to cre­ate these cyborg ecosys­tems. So I would rec­om­mend you look­ing at the work of Brad Cantrell. He’s an archi­tect. And there’s oth­ers like him, they envi­sion this future where we have this kind of con­flu­ence of intel­li­gence in mon­i­tor­ing the envi­ron­ment, and also robot­ics, and also if you look at advances in mate­r­i­al sci­ences, we can start to cre­ate cities that are much more tight­ly inte­grat­ed with nature. Cities where nature more flows through cities and we under­stand how to inter­face with nature in a much more fine-grained way. 

The aes­thet­ic that dri­ves me in that area has been sci­ence fic­tion depic­tions of future Earth. You know, future Earths that are very green, where we have nature inte­grat­ed with cities, down to our ener­gy inno­va­tions are inspired by nature. You know, our archi­tec­ture’s inspired by nature.

There’s anoth­er part to it that’s a lit­tle bit more weird but I think also worth dis­cussing. Because now we have a much more detailed under­stand­ing of genes, so how to inter­pret genes and how to mod­i­fy them. That’s actu­al­ly one of the big impact areas right now of deep learn­ing. And if you look at the work that’s being done in image pro­cess­ing, the gen­er­a­tive and cre­ative art that’s com­ing out of that, there’s a future in which I believe that the inter­face with nature could become a lot more inti­mate at the genet­ic lev­el. So we’ll be able to start envi­sion­ing hybrid struc­tures between humans and the nat­ur­al world that we cre­ate with these gen­er­a­tive models.

Pieters: So this would give a new mean­ing to personalization. 

Klein: Yes!

Pieters: A very dif­fer­ent kind of gen­er­a­tive rec­om­mender systems.

Klein: Yeah. I bring this up as kind of like a vision and aes­thet­ic fuel. I go about my day-to-day being the laser. You know, look­ing at prob­lems and solv­ing spe­cif­ic prob­lems. But as you go along you need some­thing that is dri­ving you in a direction.

Pieters: All these humans are dri­ving a lot of these prob­lems we’ve been dis­cussing. And so I think prob­a­bly a log­i­cal way of approach­ing a solu­tion is social engi­neer­ing, in a sense. I sup­pose using machine learn­ing to influ­ence pop­u­la­tions, maybe we’ll see some of that or we’re already see­ing some of that.

Klein: That’s a very inter­est­ing point. I would love to be able to use tools, and they’re start­ing to mature you know, where we can start to under­stand the whole chain from how ener­gy is derived and how it’s used, and then ulti­mate­ly what you’re using that ener­gy for. Andrej Karpathy had an inter­est­ing tweet a few months ago that got me think­ing. He made some cal­cu­la­tions that showed how much equiv­a­lent wood he was burn­ing in pow­er­ing a GPU to solve a par­tic­u­lar machine learn­ing prob­lem. I think it’d be fas­ci­nat­ing to have a more detailed under­stand­ing of that whole chain. Where the ener­gy’s com­ing from, how the ener­gy’s formed, and how we’re using it.

And so with that visu­al­iza­tion, I think as a soci­ety we’ll start seem more opti­mal ways of liv­ing. The one that we dis­cuss a lot, obvi­ous­ly, is trans­porta­tion. I mean, the fact that cities—well, in the United States in par­tic­u­lar and in China—are just ludi­crous in how much ener­gy we spend get­ting around to buy milk and to work at our desk jobs with no thought about con­se­quences on a day-to-day basis. It’s inter­est­ing to dis­cuss how there’ll be a cul­tur­al change for every­day, smaller-scale mun­dane tasks, once we start to be able to visu­al­ize this kind of ener­gy flow.

Pieters: Maybe shift­ing gears a bit. When you were talk­ing ear­li­er about con­sul­tan­cy for social good, in a sense, or for mean­ing­ful prob­lems to solve. You tend to hear more and more this kind of X for social good.” So what is your expe­ri­ence on run­ning prof­itable social enterprise?

Klein: Yeah, it’s a real­ly inter­est­ing time right now. There’s so many for-good and for-profit com­pa­nies start­ing up. And then so there’s a very healthy debate going on about whether or not that’s a good thing. The rea­son it’s hap­pen­ing is par­tial­ly because folks try­ing to do the right thing, try­ing to imple­ment social good and non­prof­it orga­ni­za­tions, have been frus­trat­ed. I mean, they’ve been real­ly frus­trat­ed by the slow pace of progress and the fact that they don’t have access to the top-quality tal­ent and they spend a lot of their time try­ing to raise mon­ey. And so they see things hap­pen­ing in the tech world that are much more rapid­ly inno­vat­ing through this ethos of com­pe­ti­tion and dis­rup­tion. And I think we have ben­e­fit­ed from that but there’s a lot of prob­lems to solve there still. I mean I think it’s a great thing to try to flesh out. 

The thing is we want some­thing that’s more flex­i­ble. We’re try­ing to find an inter­est­ing way to struc­ture things so that the act of solv­ing the prob­lem gen­er­ates enough prof­it to sup­port a vibrant tech­nol­o­gy inno­va­tion scene, more like a tra­di­tion­al tech start­up. One of the things you have to guard against is mis­sion drift. Time will tell what’s the best strat­e­gy in doing that. I think that it is a dan­ger­ous thing to have mon­ey dri­ving deci­sions now. Because of peo­ple that care the least about the mis­sion and the most about mon­ey will tend to get the most pow­er with­in orga­ni­za­tions unless we can have ways of dili­gent­ly pro­tect­ing against that kind of thing. 

Winiger: Could you envi­sion let’s say the year 2025 where an enti­ty like Google is a major play­er in renew­ables or con­ser­va­tion? Could you envi­sion such a future?

Klein:could envi­sion such a future, yes. Newsflash: ener­gy is big busi­ness. And right now it’s being dom­i­nat­ed by the fos­sil fuel indus­try. But that’s going to change. It’s going to be some­thing much clean­er, much more effi­cient. And that tran­si­tion will cre­ate a lot of wealth, a whole new glob­al lead­er­ship that has the plan­et’s health much more in their minds I hope I can see in sev­en­ty years or so what that looks like. I think we’re going to be beyond what we cur­rent­ly see in solar pow­er and wind pow­er. We’re going to have much more inter­est­ing cyborg inter­faces with the nat­ur­al world.

Winiger: In Germany they have this amaz­ing trans­for­ma­tion unfold­ing in real time with 30% peak time ener­gy pro­duc­tion now on renew­ables. But the thing that real­ly gives me hope is that half of that renew­able ener­gy is in citizen-controlled hands—

Klein: Good point.

Winiger: —coop­er­a­tive hands. And there’s this kind of silent rev­o­lu­tion of dis­trib­uted pow­er unfold­ing, and dis­trib­uted struc­tures that con­trolled the tech, basi­cal­ly. Which is very uplift­ing, from my perspective.

Klein: That’s some­thing that I’ve just recent­ly become inter­est­ed in and aware of, these kind of dis­trib­uted val­ue chain sys­tems. There’s lot talk about Bitcoin, blockchain. And you know, that’s some­thing that I did­n’t ful­ly under­stand the poten­tial of, these kinds of mar­kets until recent­ly. So I’m real­ly look­ing for­ward to dig­ging more into that and under­stand­ing how they can be lever­aged. You can imag­ine sys­tems where this plan­e­tary net­work of sen­sors is being put into a glob­al, dis­trib­uted CDN. And solv­ing the most crit­i­cal prob­lems with that data will set the price of the data and the val­ue of col­lect­ing data, and the val­ue of solv­ing prob­lems with that data.

I do think that machine intel­li­gence is going to be a big part of this. I think it’s debat­able how much that will add to a human under­stand­ing of these sys­tems. I tend to be on the side of we as humans use machine intel­li­gence ulti­mate­ly to derive insight. These large-scale machine intel­li­gence sys­tems will add an incred­i­ble amount of under­stand­ing of how the nat­ur­al world works. 

Pieters: For instance here in Stockholm, we’re hav­ing a project where we’re actu­al­ly look­ing at wind­mills. Windmills are high­ly inef­fi­cient, in the sense that they pro­duce ener­gy but you don’t real­ly know how much ener­gy it pro­duces where or when—

Klein: Right.

Pieters: —because you need a good weath­er model—

Klein: Yes.

Pieters: —and that’s super dif­fi­cult to do. So a lot of things to win there, by using things like deep learn­ing, mak­ing mod­els much bet­ter, and there­by also mak­ing things much more efficient. 

Klein: Yes. My under­stand­ing is that that’s one of the pri­ma­ry dri­vers right now of the increase… The per­cent­age of total ener­gy in the United States com­ing from wind is being large­ly dri­ven by more accu­rate weath­er pre­dic­tion mod­els. That did­n’t hap­pen by acci­dent. That actu­al­ly hap­pened through pol­i­cy. And that pol­i­cy was multi-dimensional. There was poli­cies to put fund­ing into large-scale com­put­ing sys­tems that facil­i­tate this kind of work. Put mon­ey into fund­ing algo­rith­mic research that can lead to improve­ments in weath­er pre­dic­tions. And the hope was that those would lead to increased uptake of wind pow­er, and that’s hap­pen­ing. So that’s a great exam­ple of this multi-pronged tech­nol­o­gy, sci­ence, and pol­i­tics that can be suc­cess­ful. But there’s a lot more that can be done. We can’t claim any kind of vic­to­ry right now.

Pieters: You men­tioned a few things which kind of give hope for the future. One is cul­tur­al change, that peo­ple will become more aware of these things but then also devel­op more tech­nol­o­gy to address these issues. Better met­rics. More accu­rate inter­ven­tions. And things like restoration.

Klein: Yes. Geoengineering is a scary but poten­tial­ly inevitable outcome.

Pieters: So which oth­er things kind of excite you? Let’s say at NIPS, what are you inter­est­ed in at NIPS?

Klein: I think the area that I am per­son­al­ly the most excit­ed about is actu­al­ly one of the fur­thest away from my domains of expe­ri­ence, which is genet­ics. The tech­nol­o­gy evo­lu­tion in genet­ic tran­scrip­tion tech­nol­o­gy is on a dou­ble expo­nen­tial. You know, so Moore’s Law is this expo­nen­tial rela­tion­ship. Genetic tech­nol­o­gy’s on this dou­ble expo­nen­tial. And it’s now afford­able. Ten years [ago] it was impos­si­ble and now it’s afford­able to ful­ly sequence the human genome and any­thing else we get our hands on. And that’s going to continue. 

So this tech­nol­o­gy is going to be every­where. And deep learn­ing is going to be a big part of that. So there’ve been a cou­ple star­tups recent, includ­ing Deep Genomics and Atomwise that’ve start­ed up to tack­le this prob­lem. Existing play­ers such as Illumina are very excit­ed about the poten­tial. And you know, these star­tups are look­ing at every­thing from drug dis­cov­ery to can­cer diag­no­sis based on very small blood samples. 

We have it being used in pre­ci­sion agri­cul­ture, that we can take envi­ron­men­tal sam­ples like very small air and soil sam­ples and detect dis­ease and prob­lems, and have infor­ma­tion we can use to opti­mize agriculture. 

And of course now there’s not just the analy­sis but now we have the gen­er­a­tive part of that with CRISPR and the relat­ed tech­nolo­gies. We’re just start­ing. I mean, if you look at the tech­nol­o­gy that’s being used today for genet­ic analy­sis with say, deep learn­ing mod­els, they’re much much more sim­plis­tic than what we see in these large-scale image recog­ni­tion sys­tems. They’re bor­row­ing from image pro­cess­ing and speech recog­ni­tion and they’re show­ing that like so many oth­er things, right off the bat we’re see­ing large gains in recog­ni­tion accu­ra­cy for detect­ing how cer­tain drugs bind to dif­fer­ent sites on the sequence. 

You know, it’s been pro­ject­ed that the genomics indus­try is going to increase by ten-fold in the com­ing few years. I think that is true. It’s going to become a huge huge indus­try, and machine learn­ing and spe­cial­ized deep learn­ing archi­tec­tures for genet­ic analy­sis and for genet­ic edit­ing are going to become a thing in the next few years.

Winiger: I mean, the cost decrease is obvi­ous­ly the most promi­nent sign of this unfold­ing rev­o­lu­tion, real­ly. But as well it opens up some broad­er, ter­ri­fy­ing sce­nar­ios where you can start the gene dri­ve from the com­fort of your bed­room and prob­a­bly try to mea­sure the impact on a large-scale biosys­tem on your deep learn­ing mod­el at home rather than some­where on AWS. But you only can get that far. These mod­els prob­a­bly will, on the dark side, become a real­i­ty as well, then.

Klein: Yes, yeah. There’s a dark side to all of these things. These pow­er­ful tech­nolo­gies, they have such destruc­tive pow­er if used in the wrong way, inten­tion­al­ly or unin­ten­tion­al­ly. And so how do we address that? I mean, we address it by under­stand­ing, by a hands-on approach, by dis­cus­sion and decid­ing togeth­er as species where we should be apply­ing our ener­gies and how we should be using things. And to have as much trans­paren­cy as pos­si­ble across the board.

Winiger: So are you a real­ist or are you an opti­mist, or… What would you call yourself.

Klein: I’m def­i­nite­ly an opti­mist. Yeah. I am an opti­mist. I’ve changed over time. When I was younger, when I would go into kind of a med­i­ta­tive state I would envi­sion things com­plete­ly falling apart, and poten­tial­ly quick­ly. But as I’ve advanced in my career and I have the abil­i­ty now to talk to pol­i­cy­mak­ers and talk to peo­ple in tech­nol­o­gy talk to peo­ple on the ground doing the work, I’m a lot more opti­mistic. I see that at least in the peo­ple that I have come in con­tact with, and admit­ted­ly that’s com­pared to glob­al pow­er struc­tures it’s a very small slice. But I’ve become opti­mistic. I can see that devel­op­ers in the future will how so much pow­er to imple­ment change. You know, we tend to be a lot that strives for sci­en­tif­ic truth and opti­miza­tion. And I think we will col­lec­tive­ly decide on opti­miza­tion for good. And the thing is you know, good is not an objec­tive thing. It’s some­thing that we all have to con­tin­u­al­ly revis­it as a species.

Winiger: If you made it this far, thanks for listening.

Pieters: And also we would real­ly love to hear your com­ments and any kind of feed­back. So drop us a line at info@​ethicalmachines.​com.

Winiger: See you next time.

Pieters: Adios.

Help Support Open Transcripts

If you found this useful or interesting, please consider supporting the project monthly at Patreon or once via Cash App, or even just sharing the link. Thanks.