A New Dark Age
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Subtitle |
| |
| Title of Series | ||
| Number of Parts | 147 | |
| Author | ||
| License | CC Attribution 4.0 International: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/43875 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
| |
| Keywords |
33c364 / 147
1
3
4
6
7
9
11
13
15
16
17
19
22
23
28
31
32
33
37
39
40
41
43
44
45
46
48
49
51
53
54
55
57
58
62
64
65
66
68
71
72
73
74
77
78
82
84
85
90
93
96
98
100
102
103
104
105
108
110
111
112
113
115
118
119
120
121
122
123
125
126
127
131
133
134
137
139
140
141
143
146
147
00:00
TurbulenceUniverse (mathematics)State observerPredictabilityDifferent (Kate Ryan album)FrequencyRoundness (object)View (database)Event horizonExploit (computer security)Presentation of a groupWritingMetropolitan area networkComputer animationLecture/Conference
01:37
Web 2.0QuicksortBitMoment (mathematics)Functional (mathematics)Computer programmingObject (grammar)Letterpress printingMultiplication signType theoryParameter (computer programming)Proper mapProjective planeNumberDegree (graph theory)ExtrapolationSingle-precision floating-point formatVirtual machineVolume (thermodynamics)Process (computing)Connected spaceStatement (computer science)MathematicsScaling (geometry)SoftwareComputer scienceBuilding1 (number)Traffic reportingEntire functionHypermediaTrailLecture/Conference
05:12
Multiplication signMedical imagingTrailHypermediaTraffic reporting1 (number)Basis <Mathematik>Ocean currentTwitterDigitizingMassVisualization (computer graphics)Digital photographyMappingService (economics)Acoustic shadowSequenceSurfaceGoodness of fitRow (database)SpacetimeEntire functionLecture/Conference
07:10
Virtual machineElectronic program guideAcoustic shadowGroup actionOrder (biology)Installation artCellular automatonMeasurement
08:12
PlanningPhysical systemMappingSatelliteMultiplication signService (economics)Google MapsPoint (geometry)Medical imagingLecture/Conference
08:57
Plane (geometry)Medical imagingPoint (geometry)Variety (linguistics)SatelliteVirtual machineSoftware testingGreen's functionCASE <Informatik>
09:51
Projective planeRight angleFilm editingUniform boundedness principleHybrid computerBitMedical imagingGodView (database)Classical physicsSpacetimeDigital photographyExtension (kinesiology)Lecture/Conference
10:56
Extension (kinesiology)Digital photographyMereologyProduct (business)CuboidMedical imagingPattern languageProjective planeMultiplication signPoint (geometry)Copyright infringementDependent and independent variablesContext awarenessLogicWeb browserLecture/Conference
12:30
Personal digital assistantSpacetimePlane (geometry)Correlation and dependenceInheritance (object-oriented programming)Thermal expansionWeb pageTelephone number mappingRepeating decimalHost Identity ProtocolSign (mathematics)FacebookDistribution (mathematics)AlgorithmMaterial requirements planningInformationComputerWebsiteSoftwareState of matterInternetworkingInformation securityTrailSeries (mathematics)Web browserExtension (kinesiology)Shared memoryWeb 2.0InternetworkingPoint cloudComputational intelligenceReal numberPoint (geometry)QuicksortBuildingRight angleProjective planeCASE <Informatik>Computer animation
13:25
SpacetimeInformationComputerInternetworkingExtension (kinesiology)AlgorithmDecision theoryReading (process)Web browserGraphical user interfaceTerm (mathematics)Solid geometryRouter (computing)ComputerDomain nameConnected spaceTime domainProjective planeInternetworkingRight angleCASE <Informatik>DeterminantLikelihood functionQuicksortSign (mathematics)Moment (mathematics)Mechanism designPhysical systemStability theoryComputer animationSource code
14:13
Chi-squared distributionSpacetimeBootingPlane (geometry)Thermal expansionInheritance (object-oriented programming)Duality (mathematics)Inclusion mapInformation privacyCASE <Informatik>Right angleTrailMultiplication signPhysical systemAlgorithmWeb 2.0Mixed realityPlanningGoodness of fitMetropolitan area networkField (computer science)Computer animationEngineering drawing
14:59
Computer-generated imageryExecution unitField (computer science)PlanningTrailPhysical systemComputer fileRadical (chemistry)Data miningClosed setSocial classOrder (biology)Endliche ModelltheorieLecture/Conference
16:04
Information securityComputer architectureClassical physicsDigital photographyPhysical lawSpacetimeForestVisualization (computer graphics)Divisor
16:57
Information securityCuboidService (economics)Identity managementDivisorView (database)Closed setFlow separationGroup actionDigital photographyRight angleMoment (mathematics)Near-ringFile archiverComputer architectureSpacetimeSoftwareWebsiteVisualization (computer graphics)Lecture/Conference
18:32
SoftwareVisualization (computer graphics)Software architecturePressureIntegrated development environmentCodePhysical systemPoint cloudProjective planeBitSubject indexingQuicksortPerfect groupLecture/Conference
19:31
Convex hullBitBookmark (World Wide Web)CalculationMultiplication signEstimatorAreaMathematicsReading (process)Division (mathematics)Data structureMassEngineering drawingLecture/Conference
20:36
PredictabilityResultantLevel (video gaming)Multiplication signComputational intelligenceBitHypothesisVector potentialScaling (geometry)Engineering drawingLecture/Conference
21:22
Factory (trading post)Scaling (geometry)Computational intelligenceMathematicianGradientPoint (geometry)InformationTelecommunicationEngineering drawingLecture/Conference
22:19
Line (geometry)Continuous functionExecution unitNumerical analysisOperations researchScale (map)FaktorenanalyseFourier seriesFunction (mathematics)Computational intelligenceComputer programmingProcess (computing)CalculationFocus (optics)19 (number)Multiplication signVirtual machineLecture/ConferenceEngineering drawingProgram flowchart
23:26
Computational intelligenceProjective planeMultiplication signMoment (mathematics)Von Neumann, JohnRichardson, Lewis FryComputer programmingPlug-in (computing)Mainframe computerPersonal computerQuicksortLecture/Conference
24:16
Von Neumann, JohnComputer architectureComputational intelligenceProcess (computing)MereologyConnected spaceGastropod shellPersonal computer1 (number)Electric generatorSatelliteDivision (mathematics)Radical (chemistry)Computer programmingVirtual machineComputerNeumann boundary conditionDesign by contractLecture/Conference
25:56
Nuclear spaceComputer programmingComputational intelligenceProjective planeSoftware developerArmComputer architectureNeumann boundary conditionLine (geometry)40 (number)19 (number)Process (computing)Artificial neural networkView (database)PredictabilityDegree (graph theory)Field (computer science)TheoryProduct (business)Observational studyLecture/Conference
27:19
Artificial neural networkConvolutionField (computer science)Electric generatorFacebookMedical imagingCompilation albumLecture/Conference
28:15
Medical imagingArtificial neural networkGoogolVirtual machineComputer animationPanel painting
29:10
GoogolAdditionDifferenz <Mathematik>Metropolitan area networkVisualization (computer graphics)Virtual machineArtificial neural networkBitComputational intelligenceMedical imagingCombinational logicDegree (graph theory)Associative propertyMachine visionLecture/Conference
30:15
Representation (politics)Artificial neural networkConvolutionFacebookMeta elementMaxima and minimaElement (mathematics)Medical imagingLevel (video gaming)Metropolitan area networkComputational intelligenceArtificial neural networkMoment (mathematics)SatelliteFlow separationSpacetimeSphereSystem callComputer programmingComputer animation
31:08
AverageMoving averageMedical imagingoutputVotingArtificial neural networkInformationBitSpacetimeProjective planeSparse matrixPersonal digital assistantDistancePattern languageCodeFile formatComputer animation
31:59
SimulationPrice indexPoint cloudFile formatVotingMetropolitan area networkPoint cloudAdditionArtificial neural networkAssociative propertyPattern languageDirection (geometry)Exception handlingExtreme programmingElectric generator1 (number)Vector potentialElectronic program guideSpacetimeBitNP-hardMereologyMIDIForm (programming)Power (physics)Computer clusterInstance (computer science)
33:25
Order (biology)Control flowRaw image formatMetropolitan area networkPoint cloudMathematicsScaling (geometry)State of matterLevel (video gaming)Service (economics)Order (biology)CausalityAverageDemosceneFrequencyProcess (computing)Computer programmingAnalytic continuationChi-squared distributionTrail
34:41
Process (computing)Open setForcing (mathematics)Event horizonIntegrated development environment
35:25
InformationPoint cloudGame controllerInsertion lossDifferent (Kate Ryan album)Materialization (paranormal)Differential (mechanical device)View (database)Lecture/Conference
36:40
Moore's lawHill differential equationProduct (business)NumberTheoryRule of inferencePower (physics)Physical lawView (database)SpacetimeInformationInverse elementGraph (mathematics)Moore's lawNumberComputer animationDiagram
37:32
RoboticsSoftware testingInformationOrder (biology)Set (mathematics)Virtual machineServer (computing)Computational intelligenceProcess (computing)Airy functionGame theoryDeep Blue (chess computer)Computer chessBeat (acoustics)Computer animation
38:26
Deep Blue (chess computer)Game theoryComputational intelligenceComputer chessGodMoment (mathematics)Process (computing)Form (programming)Vapor barrierForcing (mathematics)Endliche ModelltheorieVirtual machineRight angleSupercomputerMultiplication signSoftware developerPoint (geometry)SpacetimeSoftware testingData storage deviceFamilyAreaGoogolMassDataflowMeeting/InterviewLecture/Conference
40:54
Alpha (investment)Virtual machineBitGame theoryMassData miningArtificial neural networkState of matterInstance (computer science)Potenz <Mathematik>TheoryLevel (video gaming)Process (computing)Deep Blue (chess computer)Fundamental theorem of algebraMedical imagingStructural loadWordWave packetDigital photographyNetwork topologyWeightComputer animationLecture/Conference
42:43
Artificial neural networkNetwork topologyStructural loadDigital photographyMedical imagingMultiplication sign
43:32
Artificial neural networkMedical imagingComputer programmingVirtual machineBuildingPrisoner's dilemmaRule of inferenceGoogolComputational intelligenceLecture/Conference
44:25
Artificial neural networkTelecommunicationDecision theoryOrder (biology)Domain nameCryptosystemVirtual machineInformationScripting languageEndliche ModelltheoriePhysical systemProgram flowchartComputer animation
45:27
Cartesian coordinate systemInformationEndliche ModelltheorieFacebookPerturbation theoryNon-standard analysisDecision theoryMathematical analysisLeakHand fanMultiplication signLecture/Conference
46:42
DeadlockMultiplication signInformationVirtual machineMoment (mathematics)Hand fanChannel capacityDialectPhysical systemProgram flowchartLecture/Conference
47:33
1 (number)Process (computing)Endliche ModelltheorieMetric systemMathematical modelMereologyMultiplication signFamilyHoaxComputer animationLecture/Conference
48:50
State of matterOffice suiteHoaxTraffic reportingWebsiteHypermediaContext awarenessFacebookMereologyStructural loadMultiplication signProjective planeData storage deviceComputer animationLecture/Conference
49:46
Projective planeJames Waddell Alexander IIHoaxBuildingSocial classResultantMedical imagingStreaming mediaSmith chartGoogolWater vaporDigital photographyGoodness of fitRight angleSemiconductor memoryMoment (mathematics)AlgorithmLecture/ConferenceMeeting/Interview
50:57
Open setSample (statistics)Archaeological field surveyExecution unitEntire functionMathematicsOrder (biology)Asynchronous Transfer ModeResultantView (database)AuthorizationService (economics)Information securityComputer animation
51:55
InformationDecision theoryArtificial neural networkView (database)Level (video gaming)Point (geometry)Disk read-and-write headTheoryPhysical systemInternetworkingMappingDifferent (Kate Ryan album)Dependent and independent variablesMultiplication signCognitionEntire functionEndliche ModelltheorieProjective planeFundamental theorem of algebraMatching (graph theory)Lecture/Conference
54:05
InformationMathematicsExtension (kinesiology)Computational intelligenceEndliche ModelltheorieMultiplication signSoftware testingInternetworkingAbstractionLecture/Conference
55:11
InternetworkingAbstractionSingle-precision floating-point formatComplex (psychology)Interactive televisionDifferent (Kate Ryan album)SpeciesFilm editingConformal mapPoint cloudFormal languageElectric generatorSet (mathematics)Endliche ModelltheorieSimilarity (geometry)Lecture/Conference
56:55
MedianCartesian closed categoryHypermediaIntegrated development environmentGoodness of fitLecture/ConferenceJSON
Transcript: English(auto-generated)
00:15
Our next presentation comes from a man who is from Great Britain but now lives in Greece.
00:21
Now I don't know if he just tried to escape the bad weather in Britain or the Brexit, but we're sure happy he made it all the way here to Hamburg today. Now the latest elections in the US and the referendum on the Brexit have shown
00:41
us that with all the collected data and technology that we use to predict the outcome of such elections we can do it. We cannot really know everything or predict everything. A new dark age is an exploration of what we cannot no
01:02
longer know about this world and what we can do about this. Our speaker James here is an artist, a publisher and a writer and his writings have appeared in magazines and newspapers all around the world such as Wired, The Observer and The Guardian. He's also a very experienced lecturer in conferences in
01:27
universities and different events and we're very happy he's here today so please welcome him and give him a warm round of applause. James Pridel.
01:45
Hello, thank you very much for that introduction. Thank you very much for having me. Thank you all very much for coming here. This is huge and amazing and I'm very grateful. I just put hello there rather than new dark age because I didn't want it to sound quite so frightening at the
02:02
outset. I don't know like it's a thing I'm working on at the moment and I was also asked today to sort of talk a bit broadly about some of the work I do so a lot of this is going to be like a bit me me me and that I'm going to show some things I've made as a way of explaining the way that I think through technology and society and various stuff around that and
02:22
hopefully there'll be connections through that and I'll stalk quite a lot about one particular project I did this year and then some kind of extrapolations from that to kind of where I think I and perhaps we need to be thinking next. Actually my background is as a computer scientist
02:42
but I'm a seriously lapsed one. I managed to graduate with a master's degree without actually learning to program which tells you everything you need to know about CS education in the UK and has since been trying to teach myself desperately ever since and really badly but I've also been a literary publisher and a journalist and a number of other things but it was my
03:02
kind of background in publishing and my interest in the web that sort of got me slowly moving towards the place I'm in now which is increasingly in the art world but always always crossing over. This is a good example of the kind of way I put things together which is a few years old now
03:22
but it was when I was working as a publisher but wanted to start to talk to people about how technology and the internet in particular changed the way in which we were putting knowledge together and so what I did was I took the complete history of a single Wikipedia article. I took the entire changelog of it which was the article on the Iraq war which at the
03:41
time I made this book was it was about seven years of data on a single Wikipedia article and because I was working a publisher at the time I learned how to make books properly so I typeset it all it's all in like neat little three columns quite small type but when I had that all print and bound that single Wikipedia article changelog took a 12 volume size so
04:05
that the scale of a proper full old style encyclopedia and what I was thinking through this work is the fact that the way in which we build stuff with technology doesn't really necessarily change the world so
04:21
often but the way we understand how that technology functions changes the way in which we can interact with the world and so this is still an old school encyclopedia. Wikipedia is still an old style encyclopedia but the technology we've built that underlies it allows us to put that together in a way that allows us to see all the changes we made to it. We can see the fact that encyclopedias are not final statements of fact
04:44
but processes of argument over time with many many many voices contributing to them. It's a machine for historiography rather than for history and that for me captures something quite fundamentally different about the way we're capable of building stuff today and so my projects go back and forth between like things that exist as big solid objects which are
05:04
kind of quite easy to understand and more kind of immaterial software things. This was a project that I ran for three and a half years on various social media tracking reports of drone strikes. So I was working with journalists from places like the Bureau of
05:20
investigative journalism who were tracking drone strikes in undeclared theaters of war. So these were the covert ones in Pakistan and the Yemen in Somalia. These reports were being posted and those who were kind of interested in them could find out slightly more about them. But it struck me as deeply strange that this entire war was going on without images which is deeply strange in our current times I
05:44
mean and for a long time for more than a century we've they used to send illustrators to battlefields right with pencils and papers in an age of mass media where we're saturated with images. There was this whole war going on without them and yet at the same time we spent the last 10 20 years photographing the
06:00
entire earth from space. You can pull out your phone and look into any spot on the surface and I wanted to pull those kind of infrastructures together to close those loops. So every time there was one of these covert drone strikes reported I went to a digital mapping service use various ones to find as close as I could the actual landscape in
06:21
which that occurred to kind of resupply some of these images back to this ongoing fight. And I post those to Instagram to Twitter to Tumblr as these long sequences trying to create a kind of visual record of what was occurring. One of the good things that happened is that these images started to then be used in other places.
06:41
So media when discussing drone strikes instead of just running like a photo of a predator drone supplied to them by the US military would actually show images of the places where these things were occurring the actual landscapes that we're kind of talking about here. And that attempt to make these kind of hard to see I don't want to say invisible because you can always see
07:02
thanks and good. I don't want to say that but these hard to see things kind of more visible I take that kind of out into the real world as well. So for several years I've been doing these drone shadow works which are one to one realizations and that came with me just wanting to know more about these big machines a thing which I'm very unlikely to ever see in
07:22
the real world but I have you know the tools to find out just the size of it right. Just a simple thing like that and go and put that out in the street where people can actually see and kind of measure oneself physically against the thing it really changes the way people see and understand these things and I've painted these all over the world and it's
07:41
totally open sourced it. So there's a guide online to join your own and people have been subsequently taken that and done it both as art installations and at protests in all these kind of I just get like people sending me pictures occasionally when these things have appeared around the world which is amazing it's very simple to just set out to draw a thing in order to understand it and then realize a
08:03
whole bunch of stuff by doing that physical action. What the drone shutters really emphasize is actually the intentional invisibility of these things the fact that they're designed not to be seen not to be seen physically because they fly so high they're invisible but not to be seen politically as well because because
08:22
they automate systems that would otherwise lead to body bags and all the reasons you know the military is like drones. And that got me into the habit of drawing other things in the street as well. This is a rainbow plane that I drew in Ukraine a couple of years ago. If you've spent half as much time as I have on Google
08:42
Maps you might have seen the rainbow planes. They're kind of I find them deeply beautiful. I find them if you're scouting around airports or whatever you might spot these things in Google Maps and what they are are their artifacts of satellite mapping. So the satellite is passing very fast overhead taking images of the ground. The planes are flying very fast below them.
09:02
And the thing is that satellites don't see like humans see. And this is the point of which I become fascinated because I'm interested in seeing how technology sees the world in fundamentally different ways than we do. And satellites see using a variety of sensors both visible spectrum red green blue and in a high
09:21
definition black and white in this case and deep into the infrared and the ultraviolet. So they see things that we're not capable of seeing and this these little glitches are very interesting because they allow us to glimpse inside that system and see the way in which the machine is seeing the world and attempt to kind of reflect that back by by making
09:42
them visible and addressable and kind of pointing these things out in this way. It makes it possible to to think about them for me in slightly different ways. A couple of years ago I did this project in South London. This was called the right to flight and this
10:02
is on top of a multi-story car park in South London where we built these silos one of which is a hanger for this blimp. The blimp is kind of amazing. It's a thing called a helicite which is a hybrid of a balloon and a kite invented by a slightly mad British guy who I had to spend several days in the pub with.
10:21
The helicite is amazing because it's better than both balloon and kite. It has helium to lift but also a kite to fight the wind so it doesn't get blown down like a normal balloon but has much more lift than a regular kite. And I was flying it off this rooftop to kind of investigate what it was like to be able to have that view from above that kind of
10:42
classic surveillance god's eye view of the world. How would it feel for me to be able to make aerial images like this to to become to to access this airspace above London that was is otherwise kind of heavily restricted and it was a beautiful project and it ran for several months but it also
11:01
weirded me out to quite a large extent because obviously the first thing I did was whack a camera on this thing because aerial photos are beautiful and I want to see all this stuff from above. Very quickly I started to become really uncomfortable with that because I was doing surveillance essentially and I was well I tried to inform people what I was doing
11:21
and I I looked at various ways doing I kind of released the footage openly so I could see the the products of what I was doing and this kind of stuff. No one actually cared. This is in part London is not really giving a shit about anything but also I thought whether it's just a lack of
11:41
awareness the thing or whether it's just the fact that London is so subject to surveillance all the time anyway this is kind of off people's minds but I realize the responsibility was entirely on me to stop doing this essentially that I suddenly got to a point where I couldn't make any more images like this and I realized that in many ways it's quite hard to make artworks about surveillance
12:00
that don't essentially do more surveillance and that's a really bad pattern to get into because I don't think it helps I think it just kind of reinforces the logics of surveillance and normalizes it for people so I stopped myself I took the cameras off I end up putting like a kind of pirate box on there so people could share that felt like a more useful kind of relationship to
12:22
have with it and so I started looking at more kind of pedagogical ways to talk about these things like this project which is called citizen X which is a browser extension very simply anyone can download it tracks your browsing but entirely privately data doesn't leave it
12:41
I don't share it with anyone including myself but as you're browsing the web you can hit the button and it will pull down it'll show you very simply stuff that's very simple for most people in this room I'm sure where the internet thinks you are and where the internet thinks the website you're visiting is so it shows you this relationship of the the physical
13:01
infrastructure of the internet and like many artists I've been kind of investigating the physical infrastructure of the internet for a while and thinking it's important to sort of point at this stuff and go you know this magic cloud place you hear about isn't some faraway strange place it's like it's a real thing it's here it's large buildings filled with computers in legal jurisdictions
13:20
subject to certain laws but but there's there's weirdest stuff going on under that as well so with this project I didn't just want to highlight that physical internet I wanted to highlight what that physical internet does to your rights which is in this case based on NSA documents showing how the NSA assigns citizenship and what
13:40
the NSA does is it when it's pulling in all that data it looks at your browsing habits and it says well based on where this person is going on the internet and the sort of behavior they have there's this percentage likelihood they're American because that's an important determinant for the NSA because they're supposed to not track Americans so they have to have some mechanism for making this
14:01
distinction so moment by moment online your citizenship which used to be this really solid thing that used to be a thing determined by your passport by your place of birth that you could prove those of us who are lucky to hold stable citizenship you could prove with a document that's no longer the case your right to privacy is being determined algorithmically all
14:21
the time by tracking of your behavior and so what system X does is it looks at your your very simple behaviors online and it builds you one of these algorithmic system chips to say this is what actually your web browsing behavior would look like if someone more nefarious was looking at
14:40
it and how that might kind of come to affect your rights and I was looking at the sky a while back because I was looking for these planes which are covert metropolitan police surveillance planes and hang out in fields quite a lot looking for weird stuff like this and what I found instead was was
15:00
these flights this is an example of why I go into fields and stare at the sky and find myself stood next to a bunch of kind of weird interesting people because you end up in the fields around airports with the plane spotters the chem trailers and activists working on various subjects and working with a bunch of activists on tracking
15:21
deportations from the UK this is the system the British government uses to deport people which is basically where they hire private planes because it got embarrassing to do it on on a general general usual aircraft and in the middle of the night they put people onto these planes and they fly
15:41
them out of the country and this is a very hard system to see because it happens in the middle of the night at private terminals airports it happens within closed courtrooms and within privatized detention centers and so working with some friends of mine who are architects and architectural modelers we used planning permissions
16:01
went to the council we got the files on these kind of places we did these kind of classic investigative journalism practices of finding eyewitness accounts finding council documents visiting and sketching places where we could where we weren't allowed to photograph in or to visualize them so this is a courtroom in the center of London in the UK it's illegal to photograph
16:21
courtrooms so there's no photographs exist of this space but there are very very shiny architectural visualizations that we made of it this is an interesting courtroom because it's a courtroom in which special laws apply the UK passed a special law for immigrants appeals commissions where there's suspicion of terrorist activity
16:41
or some kind of security risk where they can present secret evidence which is evidence that the defendant and their legal team are not allowed to know about it makes a complete mockery of the idea of a fair trial and what's interesting is the way in that the way in which that appears as architecture in spaces like this because you can actually point to it the divider on the left here is what
17:01
separates the public viewing gallery from the spooks viewing gallery and the closed witness box over there is so that members of the security personnel can security service can appear without giving away their identities and this culminated in a eight-minute film you can watch online called seamless transitions which documents these these various
17:23
spaces that the courtroom which you just saw a detention center near Heathrow Airport and the final airport through which people are deported which is Stansted if anyone goes to the UK on holiday and what what again I was trying to do in this this this film was not necessarily
17:43
paint like just the what you would traditionally do with photo journalism so this was commissioned by the photographers gallery in London and it was when it first went on show there it was alongside a huge amazing deep dive through the archive of something called the black
18:00
star archive which is an archive of human rights photography from the 20th century it's extraordinary but it's very much what you think of when you think of of 20th century photojournalism which is the kind of sharp black-and-white photo a face in a crowd this frozen moment this moment from which we can supposedly draw like this particular story this one signal
18:20
thing which will explain everything else and it's and it's a fantastic way of working but in this I kind of realized I was doing the opposite which is I was using the architecture of these spaces and also the way in which work like this is put together which is hugely based on research assemblage cooperating with other artists visualizers and also
18:41
cooperating with the software architectural software that makes this is the same software that generates a huge amount of the built environment around us and yet we still kind of assume there's one master architect at the heart of one of these things rather than several thousand people working on code AutoCAD or however it is who now have a huge stake in our built
19:01
environment so all of these different pressures come into play on trying to depict not a particular place but the entire legal and infrastructural system that goes into creating the situation that results which is horrific deportation system so that's a very brief and swift run through of some
19:21
of the sort of things I do I want to talk a little bit more about about this project the cloud index which requires a little bit of history which I hope will be interesting this is this is one of my favorite people Lewis Fye Richardson in 1912-13
19:43
Richardson was a meteorologist who was working the Eskimedir Observatory in Scotland doing a very very early kind of weather calculation mostly just recording the weather and trying to like think about what it might mean to start to have this kind of huge body of weather data and then in 1914 he
20:01
was called up for the First World War and he was a Quaker so he's a pacifist so he joined the ambulance division and spent the war running around with stretchers the other thing he spent the war doing was that he'd taken a bunch of data a bunch of readings from the observatory and from other observatories around Europe that have been gathering for the war and
20:21
over the course of two to three years during the war while in trenches while under fire he performed the very first mathematical calculation of a 24 hour weather forecast just over the area of northern Europe just over the area that you see here but he figured out what the maths was to take a bunch of historical weather data and advance that 24
20:42
hours right to push that prediction forward into the future which is an incredible achievement and I say it took him several years to do this with pencils and paper and when he analyzed the results because obviously the day he was doing was by that stage almost a decade into the past he turned out to be broadly correct some
21:00
things were a bit more exaggerated whatever but it but his kind of his thesis worked and because of course this was a time before what we now think of as computers he didn't he thought this was a kind of achievement but thought realizing its full potential was a very long way off he in the in this book the
21:22
one I showed before he goes on this kind of beautiful little fantasy where he describes what it would take for his weather forecasting to actually achieve some kind of industrial scale and be economically useful this is a painting of that fantasy by Stephen Conlon for 1986 where he Richardson described this
21:44
vast globe on the inside of which will be painted a huge grid covering the entire earth which weather forecasters could kind of point to read data off calculate it have it replaced you can see there be teams of mathematicians then still called computers work in the middle and then this kind of
22:01
amazing infrastructure of information telegraphs and all the kind of communications infrastructure of the age pouring information into this place and pouring it back out again and so this was he knew it was possible but he thought you know mankind would never actually get to the point that it would be possible mankind
22:20
did get to that point in in 1950 when the very first mechanical calculation of a 24 weather forecast was performed which was done on the ENIAC which is a computer that some of you have probably heard of which is my personally favorite computer the first stored first
22:44
mechanical stored program computer built in the 1940s the University of Pennsylvania and this is the process for doing it and they brought together a team of meteorologists in kind of from 1948 onwards and in 1950 they ran this very very first calculation all the very
23:01
first successful calculation of a single day's weather forecasting over the northern United States and when they did it it took about three weeks to run right which doesn't sound so great but you have to remember that the ENIAC broke down a lot of the time and what they realized was that
23:21
actually if they subtracted all the time when the machine hadn't been running from the process they got it down to 23 hours and 30 minutes so they had voted for the very first successfully time calculated the weather faster than the weather itself was happening right this is a incredibly significant moment and John von Neumann who worked on this project wrote in
23:41
the in the project log that we have achieved the dreams of Lewis Fry Richardson computation now advances faster than the weather itself and I like the fact that it happened on the ENIAC which as I say is my favorite computer for those who aren't familiar with it here it is it was you know an early mainframe so it
24:01
occupied two huge rooms first at the University of Pennsylvania and later at the Plymouth proving grounds Aberdeen proving grounds where it was moved after the war and it was you know it required a lot of fiddling and plugging in if you've read Chewing's Cathedral which is a great book about the kind of history of the early kind of
24:21
computational programs there's a beautiful quote in that from a guy called Harry Reid who was a engineer who worked on this computer in which he says that I always thought of the ENIAC as a very personal computer now we think of a personal computer as something that we kind of carry around with us but the ENIAC was a computer that you lived inside right it completely
24:42
surrounded you and actually for me like that that division of personal computer is obviously and I think increasingly not true because we all live inside this kind of shell of computation now we know that these are mostly terminals to other other connections whoever owns them whether we build these things outwards or not whether we're talking to satellites or
25:00
not the ENIAC hasn't contracted into these things is actually expanded out into kind of vast shell around us and the other thing that I really like about the ENIAC is you know it's one of these computers these generation of computers were the last ones that were kind of truly physically legible so computation now is legible to those of us probably not me who
25:22
are really smart enough to kind of follow logically step by step what kind of process is happening within machines with the ENIAC you could do that physically with your eyes by looking at the blinking lights so that you could watch a computation kind of unfold on you across the walls you could actually see that as it went
25:41
from different parts of the machines through the through the architecture you could kind of follow that and that kind of legibility also seems to me something quite crucial to kind of go back to and reflect on as I mentioned one of the people who worked on that was John von Neumann who I'm sure you're most familiar with is the kind of inventor the von Neumann architecture worked on a lot of these
26:00
early projects and obviously was working on both the weather project but also on the Manhattan project because this is here with with Oppenheimer who ran the Manhattan project and the same computer the ENIAC was used for both the weather forecasting programs and for the development of nuclear bombs these are the kind of two grand computational arms
26:22
races that really kind of got computing going in the 1940s and 50s and von Neumann had this line which which he wrote first in the commentary on on the atomic on the weather project but later recycled to talk about the Manhattan project which was kind of his
26:41
view of what computation could do and particularly computational prediction and he said that all stable processes we shall predict and all unstable processes we shall control and that was his view of what you could do with computation you could you could model and control the world's sufficient degree that you could predict the outcomes so
27:01
I wanted to to take some of those ideas of the weather and our computational predictive abilities and bring them somewhat up to date and I was I got particularly fascinated with them what's currently happening in neural networks because I studied you know I studied CS a while back I studied AI in the like early
27:21
noughties but basically the AI of the 90s that was dying so we were taught neural networks as a kind of historical anomaly like a nice idea some interesting theory but basically a dead end and the field kind of went quiet for 10 years and it's just starting to go kind of massive again right the last five years neural networks have become starting to kind
27:42
of become inside everything essentially in rather interesting ways and so I thought it was important to kind of reevaluate them and explore them again and this is from a paper from the beginning of this year things move fast in this world of Facebook research paper about
28:01
DC GANs deep convolution deep convolutional generational adversarial networks which is something that they've been they've been pushing and using and this is from a newer network generation from that paper where they took thousands and thousands of bedrooms pictures of bedrooms
28:20
and they asked this network to simulate new bedrooms so each of the images you see here is not a bedroom that exists in the world it's a bedroom that's been dreamed up by a newer network that's seen many many pictures of bedrooms and it's fascinating to me what we decide to train these things on and the more public
28:42
image of neural networks which kind of everyone's a lot of people have seen is the deep dream stuff from Google and so what deep dream is is an attempt to see back through these networks and try and understand how they think about and make images so it's kind of instead of asking the the
29:01
network to look at an image and tell you what it sees it's asking it basically gives it blank or fixed images and I'll sit to kind of reverse them back through that network to kind of imagine and visualize what it can see in the world and it turns out those things are terrifying and I think I think this should be
29:20
acknowledged more when the guys on the machine learning team at Google first came up with deep dream they showed it to Google PR and were like look what we made and PR was like never release that and in fact they weren't allowed to the deep dream leaked first of all and I
29:41
don't know how but it's interesting and then they had to come out and say okay this is what this thing is and everyone went whoa man computers are tripping balls and didn't and didn't you know there hasn't been a huge amount and I know I don't think you know the people behind it are quite annoyed that there hasn't actually been a bit more thinking about
30:00
what this kind of stuff entails because it's what neural networks are doing in the world is kind of interesting you can do weird stuff with neural networks and visual neural networks and DC GANs in particular this from the same paper so you can do addition and subtraction you can take a bunch of images associated with certain concepts and you can then
30:20
produce new images that combine elements of those things so this is a network that's never seen a picture of a smiling man but has seen the these other distinct stages and you can do arithmetic on that to produce new kinds of image outcomes as I thought well if a computer can dream of bedrooms and it can dream of smiling men it can dream of all things that
30:40
can possibly also dream of the weather so I took you might have noticed from the other works I'm quite obsessed with satellite imagery so I took eight years of satellite data of weather over the UK this is from the EU space programs Meteosat which sits up there
31:01
well above Null Island watching the earth every day several images a day of weather systems over the western hemisphere and chop that down into images over the UK came to about 16000 images which I stress is really not enough for doing this kind of work but I only had eight years of data on the other input which
31:21
was polling data from the UK on their voting intentions in the EU referendum I'm sorry about Brexit I'm not going to speak of that again except I am because this is what this project is about this is this is a short snapshot I actually had eight years of polling data it's a bit sparse at the end but it was enough to extrapolate eight years of information and so I built
31:42
one of these networks with the assistance of Jean Kogan he might be around here somewhere and he's brilliant and fed it eight years of weather images from space eight years of images of cloud formations and eight years of this polling data about the EU referendum and
32:00
when your network dreams new weather formations dreamed new kind of cloud patterns and once it started to make these kind of associations it was then possible to say to it well you've seen what the weather looks like on these certain occasions when the voting is trending in these particular directions so just as we did the addition with the smiling man etc let's do some
32:21
addition on the voting and say well what should the weather look like if the entire country was to vote in one particular direction or in the other direction to generate weather potential weathers that we could simulate if we wish the democratic outcomes to be different and the ones that
32:41
the extremes are what they are I actually kind of enjoyed more the outcomes that played around with undecided voters this seemed to me actually something that really required more attention in the aftermath of you know very divisive elections of the kind of various kinds that we've seen this year to try and
33:00
put a little bit more emphasis on not on hard Brexit or Trumpism or whatever it is but on the huge space within that of indecision and unknowingness that actually guides and hugely influences a lot of our elections and the reason that I thought that this would be in part a worthwhile thing to do is because we can change the
33:21
weather now if we if we want to this is Vincent Schaeffer in his laboratory General Electric in the mid 1950s Vincent Schaeffer is the man who invented cloud seeding which is the realization that you can place into into early forming clouds crystals of silver iodide that will
33:41
cause those clouds to precipitate you can you can you can raise smog levels you can burn off fog you can enact quite large scale changes to the atmosphere when Schaeffer's work started to appear in the 1950s there was a huge public debate in the US and elsewhere about what the possibilities of
34:02
weather modification might be there was a huge belief there was a huge military belief that the weather would be the weapon of the future the US used weather modification extensively in Vietnam it had a two year program of seeding with silver iodide clouds over Vietnam in order to basically obliterate
34:21
the Ho Chi Minh trail they reckon they extended the monsoon period by an average of six weeks each year making it rain more and more and more when that leaked again leaking they stopped it immediately and there's been little willingness in the West to continue cloud seeding experiments ever since then
34:42
but it doesn't stop other people these are Chinese weather making rockets the Chinese government has a weather department of some 50,000 employees whose job it is to make the weather they active very much in agricultural locations either make it rain on the crops or preventing it hailing on
35:00
crops but they also do a lot of work clearing the air for official parades for the opening of the Olympics for these kinds of events so the weather is is a tool that we could be deploying should we wish to and one that we might increasingly want to think about deploying in
35:20
the in the not too distant future when we have to ask ourselves quite serious questions about what we want to do with the environment how we're willing to use the things that we know how we wish to use those things whether we have the strength of our beliefs in our ability to act in the world and why do I do this and
35:41
it is because I'm no longer convinced actually that it's possible to understand the world just through processing kind of vast amount of information that I don't think this is any longer a proactive useful way of dealing with the world but I realized that I kind of wanted to retain the cloud not
36:02
as a kind of bad metaphor of kind of lossy engineering or corporate control and not as that kind of materialist look I found a data cable kind of like materialization of the world but but as a way of thinking the world as recognizing that the cloud is cloudy that what it reveals
36:21
to us is often actually a kind of difference and differentiation around the world and that is not a purely kind of fixed channel for information that's there to tell us how the world is but asks us to question it and think think about it more and this is increasingly necessary because this informational
36:40
view of the world this view that by gathering information we kind of make more knowledge and more sense is increasingly at risk. I'm sure again Moore's law is familiar to most of people in this room the rule that the number of transistors doubles every couple of years in certain space you get more and more power more and more processing power.
37:00
This is its inverse. This is something that's been named Arum's law by people who work in the pharmacological sciences. This is the graph of money spent on medical research against drug discovery and it's going the other way. The more money that's put into this the less and less we're
37:20
learning and there's various proposals that have been put forward to various analyses of this which is a long running and longstanding problem in pharmacology. And one of the most prevalent theories is is essentially too much information. If you know how drug discovery is done now it's not basically people in white coats in a lab.
37:40
It basically looks like a server farm it looks like large machines it looks like robots and it looks like very very large data sets of information being automatically run through these high throughput screening machines in order to test new drug reactions against one another. And this this problem seems to be
38:01
continuing and one of the ways in which science labs are dealing with it is increasingly putting people back into the process. Not as some kind of like airy-fairy you know computers are bad people are magical kind of way. But simply that there seems to be some other way of thinking the world that doesn't rely on vast vast
38:21
data sets but on plotting slightly distinct and non-machinic ways through them. The example I like of that is is the story of advanced chess. So this is the famous game in 1997 between Kasparov and Deep Blue when IBM spent a decade building
38:41
a computer to beat this one poor guy at the only game he'd ever loved. it was you know it was this it was this insane moment because we basically like our idea of intelligence is always like the one thing we're still holding out. And you'll notice in any discussion of AI that what constitutes intelligence has
39:00
kind of receded for decades as the machines have got better and we've got oh no not that list. You kind of continually move the barriers. But at this point chess was the thing right. And it was a really shocking moment when possibly the greatest chess player of all time was beaten by this machine. And you know Deep Blue was a brute
39:22
force machine. That's what it did. It basically was capable of doing such vast search through the kind of development process space of the game as it evolved that it could out think any human by a pure kind of brute force approach of thinking ahead. And that that ultimately seemed to be right.
39:40
We finally built enough processes to beat a human. And what was interesting is that while lots of people kind of like oh God that's it the machines take over. Kasparov came back just one year later with a thing he called advanced chess which was a new kind of chess which was not humans versus machines but humans and machines versus humans and
40:01
machines because it turns out that today and you know and this has become very successful. A you know not even a supercomputer today advanced computer will wipe the floor with any human alive but a human using quite a relatively weak computer assisted by it
40:21
will wipe the floor with even the biggest supercomputer. There's some kind of interesting complementariness to the way in which this thought happens. There seems to me one model of how we can think about our relationship technology not as this kind of completely oppositional thing but as a form of cooperation and kind of thinking through problems together.
40:43
But at the same time there's something also that's that's coming out of that at the same time because this is where we next put intelligence right. This is into this is the AlphaGo game from earlier this year when Google's DeepMind again just a massive sports sport machine. Played Lisa Doll one of the
41:02
greatest Go players in the world. And despite a heroic fourth game third game fourth game DeepMind won. And so Go is now again one of these things that we held up as being such a important bit of intelligence and is now kind of you know has again been taken by the machines.
41:20
And while I really look forward to advanced Go and all the kind of interesting things that will come out of new ways to play it there's something fundamentally different between DeepMind and DeepBlue. And that is we don't understand how DeepBlue AlphaGo so it does what it does. We don't understand how the
41:40
neural network actually beat these. There's theories and there's kind of ways of watching the game and picking them apart. But essentially this was a machine that was trained first by a team of humans and then trained against instances of itself and its learning ability while it played against other instances itself was kind of exponential. And it's thought processes because it's in the
42:02
neural network and the way it's been trained that neural network is unintelligible to us at some kind of deep fundamental level. We can maybe produce the kind of weird deep dream mind state images for AlphaGo but they won't tell us what the the process is that allows this this to happen.
42:21
Everyone's probably heard this is the example of of the strangeness of neural networks. I've checked this story and it doesn't appear to be true but it's the classical story of how strange neural networks are and why they do things that we can't quite understand which is that the U.S. Army commissioned a neural network to recognize
42:41
tanks. They gave a team of researchers a tank a bunch of soldiers and they said go and hide in the woods take loads of photos feed them into a neural network and we wanted a neural network that can see tanks in the trees that can see camouflaged artillery and so on so forth. So they went out they took loads of these photos they went back to lab they built in a
43:00
neural network they ran all the images through it and and it worked perfectly on this kind of X thousand images that they had the neural network every time classified yes there's a tank in this image no there's not even when the tank was hidden deep in the trees and invisible as they went back to the army they gave the neural network and were like here you go we've done it all and the army put its new pictures in
43:21
and it completely failed it didn't work at all and when they went back because they were working with a relatively small data set they looked at the images again and they realized that the tank had only been there in the morning and so all the images of the tank had been shot in the morning and all the images without tanks have been shot in the afternoon and the neural network was really really good at working out
43:40
whether it was morning or afternoon but it completely and utterly failed to see tanks and these are the kind of strangeness that occur when using technologies where we don't quite see the way the machine sees the world and we're building neural networks into all of our things now this is Nvidia's
44:01
program to to train cars to drive where they basically just put the neural network in the car connected to all the computers and driven it around for a while so it learns to recognize the world around it without any external cues it doesn't know the rules of the world rules of the road it doesn't know what anything else looks like it's just watched humans drive for so long it figures it
44:20
out for itself these things are becoming inside the things around us all the time and we're actively building the tools of our own mystification this I find particularly strange this is from a Google brain experiment just a couple of months old where they trained two neural networks to hide their communications from
44:41
a third party so they Alice and Bob in this arrangement evolved their own cryptographic system to protect their communications from Eve and I myself think it's a really great idea that we should train the machines to be able to talk in secret behind our backs I think this is a
45:03
struggling to see see why we want to do this this this idea though of course of being able to gather all the more data about the world making it to collect everything in order to make better decisions is of course one that we see across
45:24
across across domains this is the this is the approach of surveillance agencies this belief that by gathering all of the information they will be able to make better decisions they will be able to pull out and they will build a model of the world that's so complete that they'll have a full understanding of it on which
45:40
they will be able to act and it strikes me that this this is not actually all too different from the way in which we also oppose this belief that actually the belief in information itself being somehow sufficient to change opinions to to sort of enact
46:03
some greater good naturally on its own behalf is also the approach of of of transparency of kind of full information release that the you know NSA and WikiLeaks essentially believe the same thing which there is some
46:21
smoking gun information at the heart of the world that if we only bring it to light everything will kind of magically be made better and that that that seems to me to be an increasingly insufficient way of viewing the world this is as a small example Facebook's analysis of hot topics for November 2016 with WikiLeaks right up there in the old men quadrant of
46:42
most discussed topics from the time of the election I'm a big fan of Julian Assange's original network analysis secrecy that posed the kind of leaking as this kind of like grit in authoritarian regimes that will kind of make them grind to hold but it does appear to me at the moment that right now it's kind of
47:01
merely oiling a machine that none of us want to keep running that we're locked in a kind of deadlock of an information war that we're both playing against each other in ways that are not going to break us out of this this opacity transparency dialectic
47:21
anytime soon and all around us this it seems to me that it's becoming harder for these predictive mathematical information based systems to work and we need to ask very specific questions about what that's doing to us and to our societies there's a huge fuss around polling and the poor
47:46
performance of polls both in the referendum and Trump and everything else but most of that seemed to focus on the fact that like the mathematical models were wrong and they'd have to be revised and we kind of build better ones rather than seeing these things the things that
48:00
actually poison the discourse as part of the process that when you are running polls running metrics running 40 models like this all of the time alongside everything you're actually shaping the outcomes themselves so when polls are done during a campaign they don't just say what people are going to do at the end they change people's behavior during the campaign
48:20
and they also change what is politically possible because people run polls and they say oh people don't like that we're not even going to put this out into public discourse modeling and predicting in this way restricts the kind of actual possibility of discourse around us the debate around fake news is a kind of startlingly
48:40
interesting one but one that is not also historically new or novel or should not be and also needs to be I think very specifically historically situated as something that as an idea of so it's suddenly that we can't know this stuff or that it doesn't emerge from quite specific ways of seeing the world I was particularly struck by some of the
49:02
reporting in recent weeks that went on and on about the fake a lot of this fake news stuff emerging from the former Yugoslav Republic of Macedonia that cited it particularly in this place without any particular context around the political situation in that place is possibly being related so the story if you don't know
49:21
it is that lots of kids in Macedonia and particularly apparently in this one town of Valesh set up loads of these websites which were getting more hits on Facebook than New York Times only kind of traditional media by completely making up stories and so getting tons and tons of ad revenue and it strikes me that this is a place in which they have a government in Macedonia which for the last
49:40
10 years has pursued a policy of fake news as government policy this is part of Skopje 2014 part of the Macedonians government project to build an entirely fake history for Macedonia by erecting statues of Alexander the Great and Philip of Macedon and classicising
50:01
buildings with kind of Hellenic columns and this kind of stuff in a completely kind of ahistorical move that's been opposed by lots of people in the country but as a result of a kind of huge resurgent nationalism and nationalism that actually depends on feeding a kind of fake stories and we seem to be optimising a lot of other stuff for this as well and we seem to kind of desire this
50:21
inability to fully to see and understand the world these are images that I research of Robert Elliott Smith discovered in his own photo stream when he he posted originally the two pictures on the left and Google's I think it's called like automagical algorithm or
50:41
something produce the image on the right which is an image that combines them both to have both people smiling but is an image of a moment that didn't happen right now ever it's a it's a memory of something that never actually occurred and likewise Adobe is pushing a thing you might see called vocal which is a way
51:02
of editing sound as you would edit text so basically a tool for a kind of public tool a kind of Photoshop for audio essentially that allows anyone to kind of create these kind of things our entire a lot of our technological advances seem to be in order to kind of create and change
51:21
and mold the fabric of reality in ways that are not always visible and transparent to to the visitor and the outcome of that is a world in which people don't believe anything when when complete authority of any kind of trust in institutions no I'm no one for blind trust in institutions either but the
51:40
fact that when in the UK you had a referendum and something like more than 25 percent of views of participants believe that was going to be rigged by the security services is a very worrying situation to be in and that is a result of a kind of a crisis of literacy and
52:01
inability to deal with these increasingly complex narratives that are supplied by our technologies more information as we saw in the pharmacological research we've seen the neural networks doesn't necessarily lead to better decisions and in fact often kind of hardens people against them the sociologists Frederick Jameson
52:20
in his work cognitive mapping describe this is describe conspiracy theory in particular as this attempt cognitively to map landscapes of information which is simply too vast for people to grasp that no one person can hold this entire system of information inside their head it's the same problem as big data it's also the same problem we encounter every day
52:42
on the Internet because we are continually faced with stuff that doesn't map into our model of the world and that is existentially terrifying because we're quite weak you know lizard brained creatures and that we have not equipped ourselves with literacy for dealing with complicated and often paradoxical information and
53:00
as a child of the kind of hippie Internet right this is deeply worrying because for me the Internet has always been a place of incredible emancipatory possibility a place in which you have access to all of this information and it makes the world more comprehensible to you it gives you grant greater agency and that allows you to make more social or
53:22
better ordered whatever your decision very naive view it turns out right but a really important one to get across because we're living in a world where information has been increasingly available for a long time now and is increasingly riven by fundamental isms and deeply entrenched differing points of view so we need to think very
53:40
carefully about what our what our responses are what are the new literacies that we need to build not to understand raw information but to understand these these differences between information there was a I took the title of this talk from a piece in the New York Times from a few
54:01
weeks ago by the director of global weather for a global weather foundation a big weather forecasting agency huge computational weather forecasting data the full deal and what he was saying was that alongside these growth of information available
54:21
to us is also huge and cataclysmic change climate change is now progressing to such an extent the atmosphere is warming to such an extent that the models that we've built based on the hundreds plus years of weather data we've been gathering no longer apply the weather models are actually getting
54:40
worse because the underlying models that generate them no longer work so well in a in a rapidly changing climate and we're actually moving towards a place where we're going to know less about the world than we did previously we're going to be able to predict less and that's going to affect our ability to prepare for it.
55:05
This, no not that, this, I'm running out of time and I want to take a couple of questions so anyone's got them. Here's what I think I don't think the internet and so much of the stuff that we've built
55:20
on top of it is a tool for understanding the world as some kind of abstract entity that we can regard as static and stable rather it's a tool for understanding our own ability to understand and interact with the world and it's the most extraordinary complex and advanced such tool we've built
55:41
as a species for me it's a cultural tool it's something that emerges like language and writing emerged and what's extraordinary about it is that no person or no single set of intentions created this thing or imagined what we might do with it for so its uses it's an unconsciously generated tool for
56:02
unconscious generation and it's this model of complexity it's kind of cloudy cloud and if we are to take our kind of technological understanding of the world seriously I feel that we must recognize that it's teaching us to expect and understand differences in the world to understand cloudiness
56:20
and complexity and not conformity and similarity. Thank you very much.
56:40
Thank you so much James. We now have a couple more minutes to take questions. Please use the microphone to show up at the microphone so I can see you.
57:04
It's right if not. Or beer later is also good. OK. Are you sure you don't have any questions? This is your chance.
57:22
I think we're good. All right then. Thank you very much. Thank you very much.