Hacking collective as a laboratory
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00:00
HackerWurm <Informatik>Minkowski-MetrikHackerUnrundheitRandverteilungComputeranimationVorlesung/KonferenzBesprechung/Interview
00:41
SoftwareWeb logMereologieDatenverarbeitungssystemNeuroinformatikGrundraumOrdnung <Mathematik>HardwareBildschirmsymbolForcingProgrammfehlerHackerStrömungsrichtungVorlesung/Konferenz
01:35
Regulärer GraphAusnahmebehandlungFaktorenanalyseGruppenkeimPhysikalischer EffektMinimalgradServerQuaderMathematikSoftwarePartikelsystemDifferenteGruppenoperationVirtuelle MaschineComputeranimation
02:10
SchnittmengeProgrammfehlerDifferenteMultiplikationsoperatorNebenbedingungPartikelsystemKategorie <Mathematik>GruppenoperationÄhnlichkeitsgeometrieVorlesung/Konferenz
03:05
ZehnÄhnlichkeitsgeometrieDifferenteProgrammfehlerKategorie <Mathematik>Gruppenoperationp-BlockSchnittmengeMathematische LogikMetropolitan area networkOffice-PaketVorlesung/Konferenz
03:56
Einfach zusammenhängender RaumProzess <Informatik>SoftwaretestNichtlinearer OperatorGruppenkeimProgrammfehlerDifferenteTermWeb logGruppenoperationExpertensystemQuaderOrdnung <Mathematik>NeuroinformatikComputeranimation
04:46
ExpertensystemArithmetisches MittelOrdnung <Mathematik>ProgrammfehlerSchnittmengeGruppenoperationQuaderVorlesung/Konferenz
05:32
Einfach zusammenhängender RaumProzess <Informatik>SoftwaretestNichtlinearer OperatorGruppenkeimMonster-GruppeQuellcodeÄhnlichkeitsgeometrieQuellcodeTypentheorieSchnittmengeProgrammfehlerGruppenoperationOrdnung <Mathematik>PartikelsystemComputeranimation
06:07
ZehnEntscheidungstheorieSoftwareDatenmissbrauchVorlesung/Konferenz
06:49
Monster-GruppeQuellcodeGruppenkeimÄhnlichkeitsgeometrieDatenmissbrauchEntscheidungstheorieSoftwareKreisflächeGüte der AnpassungResultanteTermTypentheorieGruppenoperationMusterspracheZählenVorzeichen <Mathematik>MereologieArithmetische FolgeComputeranimationVorlesung/Konferenz
08:33
Offene MengeSoftwareProdukt <Mathematik>FokalpunktCOMQuellcodeComputerarchitekturDifferenteSoftwaretestKognitionswissenschaftOpen SourceVorlesung/Konferenz
09:47
AutorisierungMailing-ListeHackerMAPMinkowski-MetrikMereologieMusterspracheHackerCodierungDifferenteComputerspielLie-GruppeComputeranimation
10:18
MultiplikationsoperatorKlassische Physiksinc-FunktionGruppenoperationEndliche ModelltheorieRoutingVorlesung/Konferenz
10:52
ComputerRelation <Informatik>DatenreplikationStabilitätstheorie <Logik>Mechanismus-Design-TheorieProdukt <Mathematik>Minkowski-MetrikHackerEntscheidungstheorieShape <Informatik>BeweistheorieComputeranimation
11:26
Minkowski-MetrikInverser LimesSystemaufrufSchlussregelOffene MengeProgrammierungOrdnung <Mathematik>MultiplikationsoperatorZentrische StreckungQuick-SortGrenzschichtablösungEntscheidungstheoriePhysikalischer EffektEndliche ModelltheorieThumbnailDifferenteHausdorff-RaumSoftwaretestMakrobefehlVorlesung/Konferenz
12:26
Relation <Informatik>ComputerStabilitätstheorie <Logik>DatenreplikationMechanismus-Design-TheorieHackerProdukt <Mathematik>Minkowski-MetrikRelativitätstheorieForcingOrdnung <Mathematik>NeuroinformatikCASE <Informatik>SystemaufrufSoftwareHackerMinkowski-MetrikComputeranimationVorlesung/Konferenz
13:01
Physikalische TheorieInformationKartesische KoordinatenComputersimulationGeradeGruppenoperationCASE <Informatik>NeuroinformatikArithmetisches MittelEndliche ModelltheorieSupercomputerSystemaufrufÄhnlichkeitsgeometrieMinkowski-MetrikComputersicherheitAusnahmebehandlungMultiplikationVorlesung/Konferenz
14:21
CASE <Informatik>HackerProgrammiergerätDirektes ProduktLuenberger-BeobachterProgrammierungQuaderNatürliche ZahlLie-GruppeSchlussregelBeobachtungsstudieDifferenteVorlesung/Konferenz
15:09
VorhersagbarkeitKartesische KoordinatenHackerDifferenteNatürliche ZahlEndliche ModelltheorieStabilitätstheorie <Logik>Produkt <Mathematik>MomentenproblemMinkowski-MetrikVorlesung/Konferenz
15:59
PunktNatürliche ZahlTelekommunikationMinkowski-MetrikÄhnlichkeitsgeometrieSichtenkonzeptQuellcodeProdukt <Mathematik>HackerPaarvergleichFrequenzVorlesung/Konferenz
16:29
HackerPlancksches WirkungsquantumTheoretische PhysikObjektverfolgungMustererkennungSimulationHardwareMinkowski-MetrikÄhnlichkeitsgeometriePhysikalismusGrenzschichtablösungWurzel <Mathematik>Open SourceHackerOffene MengeFrequenzParametersystemSchlussregelKreisflächeComputeranimation
17:05
ParametersystemPhysikalismusTelekommunikationMinkowski-MetrikHackerGüte der AnpassungFastringRichtungKlassische PhysikVirtuelle MaschinePlotterPlancksches WirkungsquantumEndliche ModelltheorieVorlesung/Konferenz
18:11
SchnittmengeKlassische PhysikFehlermeldungMustererkennungEndliche ModelltheorieDreiecksfreier GraphHackerFormale SpracheResultanteRechenbuchPhysikerComputersimulationEinfügungsdämpfungVorlesung/Konferenz
18:59
ComputersimulationKlassische PhysikPhysikerDifferenteÄhnlichkeitsgeometrieVorlesung/Konferenz
19:22
HackerPhysikalisches SystemReverse EngineeringQuick-SortMultiplikationsoperatorHackerMetropolitan area networkComputeranimation
19:49
ProgrammfehlerAnnulatorHackerZentrische StreckungSchnittmengeQuaderMaßerweiterungVorlesung/Konferenz
20:33
HackerÄhnlichkeitsgeometrieSchnittmengeZentrische StreckungPhysikerPunktPhysikalisches SystemSystemaufrufVorlesung/Konferenz
21:05
Schnitt <Mathematik>Klassische PhysikHackerAsymmetrieMaßerweiterungQuellcodeNatürliche SpracheVorlesung/Konferenz
21:41
Natürliche SpracheNotepad-ComputerMechanismus-Design-TheorieVirtuelle MaschinePhysikalisches SystemTLSProzessautomationPatch <Software>ExploitZeitabhängigkeitAnalysisProgrammfehlerNatürliche SpracheGenerator <Informatik>ComputersicherheitExploitDifferenteEvoluteMinkowski-MetrikFokalpunktStützpunkt <Mathematik>Basis <Mathematik>QuaderNatürliche ZahlGoogolWeb logPhysikalisches SystemAlgorithmische LerntheorieVirtuelle MaschineMomentenproblemCoxeter-GruppeUltimatumspielTLSComputeranimationVorlesung/Konferenz
22:44
Physikalisches SystemFormale GrammatikMereologieRechter WinkelMinkowski-MetrikHook <Programmierung>Stabilitätstheorie <Logik>PhysikalismusProdukt <Mathematik>VerkehrsinformationVirtuelle MaschineHackerVorlesung/Konferenz
23:09
MereologieTranslation <Mathematik>StandardabweichungFormale GrammatikSchnittmengeVirtuelle MaschineNeuroinformatikPhysikalisches SystemWeb logPhysikalismusGüte der AnpassungVerkehrsinformationArithmetisches MittelComputersicherheitVorlesung/Konferenz
24:11
HackerAutorisierungElement <Gruppentheorie>Mechanismus-Design-TheorieATMProdukt <Mathematik>TypentheorieHackerBestimmtheitsmaßComputersicherheitEinfacher RingVorlesung/KonferenzComputeranimation
25:01
BeobachtungsstudieHackerPunktVorlesung/Konferenz
25:40
SimulationHackerSchreib-Lese-KopfSoundverarbeitungKlasse <Mathematik>Vorlesung/Konferenz
26:10
HackerMereologieZeitzoneDatenfeldSoundverarbeitungSimulationOrtsoperatorTranslation <Mathematik>ExistenzsatzGrenzschichtablösungMultiplikationsoperatorGruppenoperationSystemaufrufVorlesung/KonferenzBesprechung/Interview
27:07
Physikalischer EffektGruppenoperationHackerDifferenteNegative ZahlOrdnung <Mathematik>MaßerweiterungQuellcodeMultiplikationsoperatorCASE <Informatik>MomentenproblemVorlesung/Konferenz
28:28
Wurm <Informatik>UnrundheitRoutingVorlesung/Konferenz
28:52
MedianwertKonvexe HülleKartesische AbgeschlossenheitHypermediaJSON
Transkript: Englisch(automatisch erzeugt)
00:15
Welcome, everybody, to the next talk, Hacking Collective as a Laboratory. Our speaker is Martin Zarod, he's from Poland.
00:23
This talk is about sociology, and the talk will offer you an insight on the sociological research that has been done on hackerspaces and hacking collectives, and now I ask you for a warm round of applause to greet Martin. Have a lot of fun.
00:46
Hi. My name is Martin Zarod. I'm a member of the Warsaw Hackerspace. I'm also a Ph.D. candidate at the Institute of Sociology at the University of Warsaw. Before I start, I ask you, please imagine a computer bug.
01:03
It could be a software bug, it could be an exploit, it could be a hardware bug, it could be the bug that started your hacking career, it could be the bug that you are struggling currently. Please imagine any kind of computer bug. You do not have to be a hacker if you are just interested. It could be a bug that you just read about in a popular press.
01:23
I do not care. Please imagine. Let's, ten seconds, any kind of computer bug. So, I guess that some of you, when imagining a computer bug, also thought about how it
01:40
works. Some of you thought how to stop it working. Some of you thought how to exploit it, how to make money of it, or how to make this good, this bug, for a good cause, or any kind of cause. Some of you also, perhaps, thought how to make it visible, or how to make it invisible,
02:03
or what machines, instruments, would it take to make it, to change the degrees of visibility. Would it need an oscilloscope? Would it need a server, or any kind of software? Perhaps some of you thought about systematic bugs, bugs that are inherent to particular
02:23
designs. Some of you might have thought about situational bugs that are exceptional, connected with particular sets of constraints that do not happen every time. Obviously, some of those questions are technological questions. Some of those questions might be connected with science or medicine, because the question
02:45
about knowledge is inherently connected with different kinds of scientific, technological, medical reasoning. But there are kind of questions that we, when we start asking them, we come closer to economical issues, or sociological issues, or philosophical issues.
03:03
And sociology of science is about what is common for the people who are shared by the same thinking, or similar thinking, about particular categories of knowledge. So when we start asking questions, who may see that particular kind of bug, or when
03:24
we start the questions, what does it take to see that particular kind of bug in a sense of situational setting, or sociological setting, then we are starting to ask philosophical questions, or sociological questions. The difference is, when a philosopher asks about knowledge, he or she thinks about
03:47
the individual. And when sociologists start asking similar questions, he or she thinks about the group. So, some of you might have thought about the particular kind of man-in-the-middle attacks,
04:03
for example, as described at Claude Fleur's blog. It's okay. Some of you might have thought about syphilis, or maybe not, because this is a different kind of bug, and it doesn't, and it's not that closely connected with computers. Or is it? So, when we think about the bugs, or knowledge, in terms of sociology of knowledge, we start
04:29
to understand that learning to see a bug is also about belonging to a particular group. When we start to see different kind of bugs, when we learn how to manipulate them, how
04:43
to exploit them in a different kind of way, we are also being closer and closer to particular knowledge or particular expert sub-societies, or sub-social groups. And obviously, the bug is connected with a particular setting. It might be situational, but more often than not, it also needs an extra work to
05:05
make it visible. It doesn't mean that this bug is artificial or natural. It just means that it's not obvious, or it requires a special knowledge or a special social setting in order to make it visible.
05:20
This is one thing that is shared between syphilis and men in the middle attack. They are not visible at the first glance. They need some kind of expert setting in order to become visible. And the sociology of science is about that kind of expert settings. So to see or not to see a particular bug is also to belong or not to belong to a particular
05:43
knowledge group. And sociology of science has different strains. One strain is proposed by Merton, and it worked that science was a source of certified knowledge for the rest of society.
06:02
The science was the only source of knowledge. The other types of knowledge was not certified. The Merton would go crazy if he thought about hackers. But, fortunately, he didn't have to. Other kinds of sociological thoughts was proposed, for example, by Bruno Latour, and
06:23
he proposed that technology is society made durable. And it means that particular design decisions that were made, or particular design decisions that were, that seemed to be random, accidental, eventually became more and more stable, more
06:40
and more constrained, and more and more embedded into fabric of society. For example, if we decide where do we put our bridges, if we decide what measures of our networks are enough to ensure privacy or security, we are making long-term decisions
07:00
that will shape the societies that will come after us. My, those approaches are good, and many people are working within them with a good, with rather interesting results, but I wish to talk about approach proposed by Ludwig Fleck, Polish sociologist, Polish philosopher, Polish microbiologist, and he proposed the notion of thought collective.
07:26
It was 1930s, he published in German, so perhaps not all of you might have read about him, so he proposed that thought collective is a group of people who share the similar epistemological approaches.
07:42
What do I mean if I say epistemological approaches? For example, if certain group of people agree, more or less, that something counts as a good evidence, or something counts as truth, or some methods of achieving truths
08:00
are better or more reliable than other, then perhaps this group of people might be a thought collective. What is more advanced sign of thought collective? It would be the ability to recognize some patterns that would be invisible to other. That kind of pattern might be a syphilis bacteria at the microscope.
08:22
That type of pattern might be men in the middle attack in the cold, but it's about seeing the pattern that other group of people would not see. And learning to see that pattern is also starting to belonging to that particular group. And we, the sociologists of science, we are somewhat different than our friends from
08:45
psychology department or our friends in cognitive science department, because we think that thinking is a collective endeavor. We do not really believe that people think on their own. We believe that when particular individual starts thinking, he or she is thinking
09:05
with his or hers books, experiences, histories, biographies, and so on, so on. And many more advanced project, for example, open source software or architecture or Vassarman test to detect syphilis was collective endeavor.
09:24
And we also think that thinking involves minds, but also involves hands and eyes. Sometimes we do not have the idea of solution in our mind. Sometimes we just come upon it, you know, with tinkering, with seeing things in
09:43
microscopes or in cold. So how does it work in practice? I spent some years watching and talking and observing my friends from different hacker spaces in Poland and in some neighboring countries. I gathered the data.
10:01
I got privilege and access to many parts of their private or hacking lives. They talked with me. Then I gathered this data, I analyzed it. The first stage of analyze is called first stage coding. And this is looking for basic patterns. Basic biographical patterns, basic situational patterns, basic, for example,
10:23
group rituals, rather simple stuff. Then, basing on that, I devised some kind of models. I test them and eventually I publish papers. So this is the classical sociological route of work, but some of you might not know it.
10:42
So what about the laboratory? So sociologists of science, since Merton, spent an awful lot of time in laboratories because laboratory for a modern society is a special place. This is the place where not only science is made, but this is also a place where future society
11:05
is kind of shaped up, not because in the laboratory we could devise any kind of knowledge or not because any kind of truth could be socially constructed no matter whatever, but because many of the decisions made in laboratories have significant political consequences.
11:26
I'm not only talking about decisions made in programming communities, but it also dates back, for example, to program Manhattan or the Wasserman test for syphilis. So what is the laboratory or what makes the hackerspace a special kind of laboratory?
11:47
As a rule of thumb, laboratory is a kind of separated space when you could make mistakes freely. The mistakes made in the open society cost more than mistakes made in the laboratory
12:03
and laboratory enables you to kind of limit the space or time or causes in order to manipulate them. Things in laboratory might happen faster, might happen slower, might happen on different scales, for example, in the scale of microscope or in the scope of macro sociological models,
12:23
and you could, for example, limit some causes. And in the sociology of science, sometimes we say that in the laboratory, the first relations are reverted. It means that in the outside world, the bacteria are stronger because they are harder to spot, they are harder to catch,
12:42
but in the laboratory, we could limit them. We could catch them in our networks, in our instruments. But what is particular for the hackerspace or any other kind of informatical technology laboratory that in the case of IT laboratory and hackerspace, in silico is the same as in vivo.
13:05
It means that there is very thin line between theoretical model and practical application. For example, in physics, in chemistry, in biology, computer model is very much separated than the wet work, the bench work, the real work, whatever you call it.
13:23
But in the information security, theoretical model could be more or less tested or in many cases could be tested as fast as you may. There are exceptions, I know about the supercomputers, I know that not all theoretical informatics could be tested on the spot,
13:44
but as a general principle, it works more or less. The particular laboratory, as we know from FLEC, is often linked with a particular thought collective. Thought collective is a group of people that share, as I said, similar epistemological patterns
14:04
and in the case of hackerspace, that kind of hacking thought collective could be recognized, for example, with accepting this principle is to tinker with something, is to understand to something. It doesn't mean we would go with that after, so don't worry,
14:23
but it comes with kind of cost because in the observed hacking communities, they are often brilliant engineers, they are often brilliant programmers or tinkerers, I couldn't pay more respect to them, but more often than not,
14:40
they have some problems with stabilizing the knowledge. Often the manuals or tutorials or documentation provided by hackers is limited or in the best case scenario is often linked to the direct product. Hackers do not have much patience or do not put much attention to general principles.
15:10
This is the difference from many natural scientists. Many natural scientists are looking for general rules, generalizations, whereas hackers would go for application.
15:24
For scientists, something is true if we could make a general predictable model and for hackers, something is true if it works. Obviously, some engineers would do differently, some natural scientists also would do differently,
15:41
but we would go it in a moment. But if the hackers have problems with stability of the products of the hackerspace, what is the most stable product of the hackerspace? I think that the most stable product of the hackerspace are the hackers themselves. Not many products of the hackerspace have the similar amount of documentation
16:04
or similar amount of autonomy as the hackers. You do not need much to operate a hacker, but you need much to operate the hacking products. From the sociological point of view, hackers are much easier to handle than hacking products.
16:26
To more comparison with natural scientists, I compared the data on the hacker spaces collected by me in several hacker spaces in a period of three years and so on and so on, with the data on CERN and Max Planck Institute collected by Karak No Sathina,
16:43
and we have some similarities. For example, compared with physics, hackers more often than not would agree that the knowledge belongs to the whole community. The knowledge would be more distributed one. And this is not very surprising because we know that open source models or open hardware
17:03
has some similar roots with physics community in CERN. So the historical argument stands. But also what is less obvious is that hackers, as well as particular physicists, also like to gossip, to talk about machines.
17:22
We know that many physical instruments operate more or less or are calibrated more or less, and they have good days and bad days. We do not always write about it in scientific papers, but if you remember about the issues of detection of Higgs boson
17:41
and many false claims or problems with CERN announcement and their own limitations, you would see the trace of it. But comparing hackers with physics is rather obvious. We know that there is a hacker space near CERN, and many of the electronic people or physics people from CERN are members of that hacker space.
18:02
It's really nothing new. But what is more interesting is comparing hackers with biologists from Max Planck Institute. For example, some biologists agree with the classical hacking stance that to understand something is to produce that.
18:22
You do not have to devise a general model. You just need to devise the tools. If you are able to manipulate it, then you understand it. Other thing would be the recognition of authorship in important chunks. And the third thing would be the more attention to quick trial and error cycle.
18:43
In this particular biological setting in the observed period, it was, I'm open to discussion. Many of the biologists doing the wet work, doing the bench work, doing the laboratory work, relied on quick trial and error cycle without doing much formal calculations,
19:03
without resolving much to formal models, just unlike the physicists. Physicists, you need a lot of simulations before you put on your atlas. But this is rather interesting classical sociological stuff,
19:21
but let's do something crazy. Let's reverse the thinking. We were looking at the hackers as they were scientists. We have some differences, we have some similarities. But let's look at the hackers as some sort of social syphilis. I'm really sorry.
19:44
But we know that syphilis in the time of Fleck shown something about the society. For example, shown that many of respected middle-aged men were not as faithful as they claimed to be.
20:03
It's shown something about the need of public healthcare. It also shows something about who really knows something about small tiny bugs invisible to other people. And up to some extent, and this is a metaphor,
20:21
I do not claim that hackers are bacteria. I also think that hackers is a kind of... might act as a bacteria in a certain settings in a scale of society
20:40
because they reveal some things that otherwise would be hidden. And what is also a similarity is that hackers do not need a knowledge about the whole system. They do not need to understand the whole setting. Physicists need all the data, biologists need all the data, but syphilis and hackers need only one access point, one entry point.
21:06
This is obviously a classical knowledge or asymmetry between attacker and defender. And here I'm treating some hackers as attackers, but for the sake of metaphor, let it be. What if, if some kind of hacking history or history of hackers
21:27
or sociology of hackers resemble up to some extent history of syphilis and biologists, maybe we could also think, or maybe we would see that some kind of genetics,
21:41
genetics of exploits or genetics of hackerspaces. And this is made because people working on security are working on different generations of bugs and evolutions of bugs. And people working on hackerspaces talk about generation of hackerspaces, for example, maxigas.
22:01
But also we know that many of the natural sciences, for example, chemistry or medical data moved to automation discovery systems based on machine learning. I would be very much amazed if some parties weren't breeding machine learning systems on exploit and patches.
22:22
We have automatic discovery systems on biology, we have automatic discovery systems in chemistry, in medical data. I'm almost sure that there is a beast in the Google or in the NSA that is feeding on exploits, that is feeding on blog posts,
22:41
and that is feeding on this presentation at this moment. But here comes the interesting part. I said that hackers sometimes have problems with stability. Reports produced by hackerspaces are less standardized
23:01
than inscriptions or reports or scientific papers presented by the formal laboratories. It means that knowledge from the formal physics laboratory is much more easier to chunk into machine learning system. It means that it's easier to standardize knowledge,
23:24
but on the other hand, as my friend put to me, we're still feeding the computers knowledge about the computers, so the translation part is easier, so it goes both ways. But nevertheless, the issue of standardization in this part,
23:44
it's no longer a bug, it's a feature, or it's an issue. Less standardized knowledge, the whole set of blog posts, tutorials, YouTubes, gossips, CTF reports, whatever.
24:02
They do not resemble scientific papers, but maybe this is good. Maybe this is what we need. So I know that people have found some cures for syphilis. It's good. But I really hope that, and when they found that cure for syphilis,
24:25
many of the thought collectives collaborated. They merged, they changed, they were forced to collaborate. They need to understand and translate from one mode of thinking, one mode of knowledge production to another. They learned to talk to each other and they created something bigger.
24:42
I really hope that some hackers will start, or will continue translating to other types of knowledge production. For example, knowledge production by journalists, we know that it's occurring. The knowledge production by the academic people,
25:01
we know that it's occurring, and so on and so on. And the last thing, I really hope that unlike the syphilis and unlike the history of flak, I really hope that hacking would not be cured. Because we do not need the syphilis, but we need the hackers. And I really wish on this point,
25:23
I really wish to express my sincere gratitude to all the participants of my study. For the three years I observed you, and you granted me the privilege to be observed. You agreed to give interviews, and I'm really grateful for that. Thank you very much. Thank you very much, Marcin, for your talk and all your insights.
25:53
We luckily have five minutes left for questions and answers. So if you have any questions, please move to the microphones that we have throughout the room.
26:00
Please, your first question. Please move close to the microphone. Is this working? Yeah, it is. Okay, so in your experience doing this research, did you ever find hackers in a research lab? And if you found any positive effect of this, and if not, and you ran a simulation in your head, what do you think would be a positive outcome
26:22
of having hackers in a research lab? I've run into several accounts. For example, during this Congress, when people have two affiliations, they were affiliated in particular hacking collective, and at the same time, they were active in the academic field. We know that people exist.
26:41
I haven't met many of them, but I met several scientists turned hackers or hackers learning some things from science. And I think this is beneficial. If you are more interested in that, please look into my papers about hacking collective as a trading zone. I have a special part of research done
27:03
on the translation intergroup issues. Thank you very much. Second question. You said that hackers are like syphilis to the society. And I'm sorry, but I didn't understand in which way they are the syphilis. Can you repeat it?
27:21
Yeah, of course. This is a metaphor. The hackers sometime, in some sense, act as a syphilis because, first of all, they reveal some things about society that otherwise would be hidden.
27:43
Second, in order to see the actions of the hackers, you need a different thought collective. And what is peculiar, I haven't talked much about it, but this is the moment, in some cases, the same people that are acting
28:01
would be the same thought collective that would be the cause. And this is characteristic for hackers because some actions done by the hackers could only be understood within the hacking community. So that is the extent of metaphor. This is not about causing harm
28:21
or any kind of negative stereotypes connected with hacker. Thank you. Do we have any other questions? We still have some time left. So if you have a question or a comment, feel free to move to one of the microphones. It seems to me that this is not the case,
28:41
so please give another warm round of applause to Marcin. Thank you very much for your talk.
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