The HLF Portraits: Joseph Sifakis
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The HLF Portraits12 / 66
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Video gameComputer scienceMathematicsComputer programmingSemiconductor memoryDecision theoryMereologyPhysicalismTerm (mathematics)Goodness of fitFamilyPerfect groupInstance (computer science)State observerPoint (geometry)Inheritance (object-oriented programming)Observational studyStudent's t-testNeuroinformatikDegree (graph theory)Multiplication signMeeting/Interview
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SoftwareComputer programmingFormal verificationState of matterAxiom of choiceLimit (category theory)Complex (psychology)Moment (mathematics)TheoryPhysical systemHypothesisNumberProcess (computing)Field (computer science)Parallel portCartesian coordinate systemStudent's t-testModel checkingEndliche ModelltheorieCalculationNeuroinformatikContext awarenessMultiplication signTuring testPosition operatorSoftware developerMeeting/Interview
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Computer scienceOrder (biology)SoftwareControl systemFormal verificationState of matterConnected spaceUniverse (mathematics)MereologyMoment (mathematics)TheoryPhysical systemMachine visionGoodness of fitPredictabilityUltraviolet photoelectron spectroscopyFood energyField (computer science)Bridging (networking)Data conversionView (database)Endliche ModelltheorieDifferent (Kate Ryan album)NeuroinformatikMultiplication signPublic key certificateRight angleDesign by contractMeeting/Interview
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Dynamical systemComputer hardwareMathematicsOrder (biology)SoftwareTelecommunicationComputer programmingIntegrated development environmentFormal verificationTheoryPhysical systemAreaMachine visionDivisorDependent and independent variablesInternetworkingResponse time (technology)Process (computing)Perfect groupPredictabilityInstance (computer science)Information securityParallel portOpen setInteractive televisionPoint cloudDifferent (Kate Ryan album)NeuroinformatikMultiplication signService (economics)Internet der DingeMeeting/Interview
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CodeType theoryOffenes KommunikationssystemPhysical systemInformation securityService (economics)Operating systemMeeting/Interview
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Data structureInformationNatural numberOrder (biology)Theory of relativityCombinational logicTelecommunicationLevel (video gaming)Electric generatorCategory of beingDimensional analysisPhysical lawArithmetic meanExtension (kinesiology)TheoryPhysicalismPythagorean theoremTerm (mathematics)MassWeightFood energyField (computer science)Point (geometry)Variety (linguistics)Observational studyDifferent (Kate Ryan album)Domain nameNeuroinformatikSymbol tableTuring testSpacetimeRight angleMeeting/Interview
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Internet forumRight angleComputer animation
Transcript: English(auto-generated)
00:17
So yours has been a life in research, that's obvious, but I saw in an
00:23
interview with you that you said the interest in research went back to your childhood. So I'm going to go back to your childhood and describe the context, the family, the young boy. Who were you? What was the intellectual atmosphere in your home?
00:41
Well, I was born in Heraklion, in Crete. I'm 46, 1946, and my family was a family of merchants. The origin of my grandfather was from Turkey. My mother was born in Smyrna.
01:01
I have a very happy childhood. My father was of Cretan origin and my mother was from Smyrna, from Turkey. All my memories of my childhood are very, very happy. I had a lot of love from my parents, my grandparents, my family.
01:28
A lot of books too? Not so many books, not so many books. I'd like to say that I was looking for books when I was a young boy.
01:40
Also, by nature, I was very curious, so a lot of curiosity. This was sometimes very embarrassing for my parents. I was keeping asking questions about how the radio works, how the car works.
02:03
I was insisting on that. I remember sometimes we were walking with my father downtown and asking how the electric bulb works. My father gave an explanation. I was not happy with that.
02:23
So, later on, we encountered a friend of him and I asked him, Hello, you see my father asked about the electric bulb and he told me the story. I don't believe it. So, do you have any better explanation? You said this? Yes, yes. You were at home?
02:40
Yes, five years old. Five years old? Yes. So, I had this observation, trying to understand in depth everything, and still I have this observation. Perfect. So, is there a school that is going to support you in your curiosity or will this take a long time to find?
03:01
Well, I was independent in the school. I was a very good student, but I did not want to be the first for some reason, because I had some kind of modesty and I did not like the spirit of competition. I don't like competition.
03:22
So, I was a very good student and then when I went to Athens to study electrical engineering at the Technical University of Athens, I was a good student, but nothing more. I graduated in 1969, so I had regularly five years of studies.
03:48
I got my electrical engineering degree. I want a feeling, if you would agree, to the spirit of what student life was like at such a place.
04:02
It was very, very different from student life today, I can imagine, because it was the 60s, the end of the 60s, and at that time students had a very relaxed life, although we had very serious duties.
04:24
Technical University of Athens was the best school in Greece, so the best students were there. We had to study very hard, but at the same time we had a lot of interests. Most of us were very strongly involved in politics at that time, and we had plenty of interests.
04:46
I was passionate about poetry, for instance. I spent many years reading poets and writing poems at that time. Tempted by a career in poetry, or this was always a secondary interest?
05:01
Sorry? Were you interested maybe in being a professional poet? I was writing poems, but I was reading also a lot of poetry, Greek and foreign poetry. We had a lot of discussions about the big questions, philosophical questions, political questions at that time.
05:26
So we had a very relaxed and very enjoyable life. Until the moment, and I should talk perhaps about that, we had a dictatorship, this pooch, that happened in April 1967.
05:43
This dramatically has changed our life. Where were you in your education at that point? At that point I was in the middle of my education, so I graduated in July 1969.
06:01
Then I wanted to leave the country because I was an active opponent to the regime at that time and involved in some... So it was a political, not a career decision to leave? Well, it was mixed, I would say. I wanted to continue my studies because I did not want to get involved in professional life, so to continue as a researcher, this was my dream from the beginning.
06:25
Of course the political situation stimulated me and I tried to leave the country and I needed a passport and this was not easy to obtain at that time. So I had to wait for one year and finally I got permission to leave the country for only a few months.
06:47
I went to Europe and by some chance I arrived in Grenoble, France and I stayed there. By chance? By some chance, yes. Because I wanted to study physics and I had a visa for only a few European countries, a tourist visa at that time.
07:10
So I went to Grenoble and then I got stuck because as an opponent to the regime, I did not have also my passport renewed and I stayed there for four years without passport.
07:25
Why was, again, the professional part of your life, why was physics going to be your choice? Well, because I loved physics, I loved mathematics and I wanted to study theoretical physics. So when I arrived in Grenoble, I applied and was admitted for a master in
07:45
theoretical physics and, okay, now I should talk about the encounter with informatics and computer science. Yes, indeed. Which you hadn't really thought about before. No, no, no, absolutely not because at the time even the term informatics existed, in French it was informatique.
08:08
So I had my first encounter, in fact, with a computer, I had to write some programs and I got very much interested in the underlying mathematics. I met a professor who was a very visionary guy, Professor John Kunzmann, who had created the Institute for Applied Mathematics and Informatics.
08:30
And, okay, so he influenced a lot my career because I was convinced to change and I got enrolled as a bachelor student in computer science.
08:44
I'm going to ask another question. What did he see in you? Did he ever say, I mean, did he pick you out? I was his, I became a Ph.D. student, I was the last Ph.D. student of him. Really? Yes, because he was retiring, wasn't the point, retiring.
09:04
Okay, we had very, very interesting discussions because I was passionate about systems, systems theory and he liked systems theory. So he gave me a first topic to study when I was a student already and I progressed a lot, so he liked my work.
09:27
I would like to say that I moved very fast in my career, I published very, very fast. At what age? So I was, when I arrived in France, I was at the age of 22.
09:41
And then I passed my first thesis in the age of, I said 70, I was so 22, and I passed my first thesis, engineering thesis at the age of 24. In a way, we're doing a history of your curiosity. So at this point, what are
10:04
you curious about? What are you beginning to wonder about as a kind of topic to involve? Yes, so what I found really amazing with computers is that they can perform calculations very quickly, very precisely, and they can be instructed to solve problems.
10:27
So since my childhood, I was very passionate with problem solving, okay. Not so much theory, of course understanding the theory, but being able to solve problems. And I liked in particular Euclidean geometry, I was passionate about that, just solving problems.
10:47
So this is what I liked in computer science, the ability to program computers, specify the solution of problems and program it, and have the computers calculate the solution for you.
11:04
I'm aware of your mentor, but is there a young community of people interested in informatics that you are seeing in Guenob, or are you pretty much alone in your passion? I'm in a quite large community of researchers, and I started in a team where we studied
11:27
hardware, and then I was more interested, much more interested in software, and the theory for building software. So I moved to another team, and I got interested in theories for building software, and finally theories about how to verify software.
11:46
And this led me to the development of the theory for model checking, for which I got the Turing Award. You are much celebrated. What was your doctoral topic?
12:01
So my doctoral topic was about verification. It was? Yes, it was about verification, and I have taken an approach that when I passed, so this was the topic for my, it was a thesis that is equivalent of diploma of habilitation.
12:21
We'll understand what it means. And when I passed this, my thesis, I had already, the theory for model checking was in 1979, and at that time the jury criticized my work because they were saying that this is too complex to be implemented, that this is not realistic enough.
12:44
Really? Because I have taken an approach that I was seeking fully automation of verification of programs, and, you know, there are some theoretical limitations that say that this verification process cannot be automated by using computers.
13:04
So I have simplified the problem by assuming that I have only a finite number of states, but very large number of states. So the criticism was that computers have very large number of states, so this will never be applicable.
13:27
And so the theory has remained on paper until we have sufficiently powerful computers that have overcome all these complexity problems and have passed a real application.
13:42
So you anticipated? We have anticipated. So this model checking is an idea that we have developed in parallel with other people in the US. You know, it's quite amazing, I think, for those who grew up intellectually with the computer revolution, that it's one of those moments where the young know more than their teachers.
14:07
Yes. Because they are more, they can imagine. Yes, you are not constrained by ideas that you have of the past.
14:22
Exactly. You are young, you see the world in a very different manner, and yes, you are innocent also, and you dare doing things that others do not tell. Would you say that it was- So it's a kind of innocence or- It was innocence? Was there a little arrogance? It's an innocence and I am arrogant. Okay, I'm arrogant. Not really, because I'm modest guy, but when I believe
14:48
I have the intimate conviction that my ideas are right, then I pursue them, no matter how much it costs. In your professional career, so you've chosen your interests, you've gotten your PhD, you've fought those who are saying no, it's not the right path.
15:12
How do you manage to get a position at Grenoble? Who is seeing that position? At the time it was very easy to get a position, because I mean life, you did not have all the problems with my employment, you have today.
15:28
So I got, I have a good record, I have good publications, so I got a position of researcher in 74 already, and I like this job of a researcher because you have a lot of freedom.
15:48
In France, the system used to be very liberal, you have to produce a report per year saying I have published so many papers, and it's very lightweight, so you are free.
16:02
Of course the problem is to be able to manage this freedom, and that's the problem, because you can be lost, if you don't know which way to go, you can be lost.
16:24
I think I was lost for some time, or I have taken some non-beaten paths, and I was lucky because these finally have proven to be very good choices, but from the beginning it was not obvious that I had made a good choice.
16:46
Here is a young man, launched, the right mind for the right moment, and how were you thinking about the future of this field at this point, because we will come to what you now think of it.
17:03
But as a young man, late twenties, launched, what was the future you thought of this choice? You are talking about the future of my choice, or the future of the field? In a way both. For my choice I was quite confident that this was not a bad choice, so I persisted in that
17:31
direction, and I should say that we had problems with publishing initially, because the ideas were not admitted, until the moment we decided to create our own community and our own conferences, because we were excluded from all other conferences.
17:48
That's funny to say it now. We created our own conference that is now a big conference, a huge community around it, it's called Computer Edit Verification.
18:04
Now regarding the question of what was our vision about computer science at that time, computer science at that time was not perceived as we perceive it today, it was much more focused,
18:21
and also we wrongly believed at the time that we could develop theories that were close to the theories we have in physics, we had a very positivistic view of computing,
18:42
which turned out to be not the right view, and of course this is a matter of discussion, still people do not agree about the focus, about the perimeter, about what computing is, there is a lot of debate even today.
19:02
But at the time we thought, and I have actively worked at that, we thought that we could develop theories for programs, for software, that would allow us to build software as we build bridges for instance, to have predictability as we say.
19:22
So you know that when you build bridges, you write down equations, you rely on a very robust theory, that is classical physics, and this allows you to guarantee that a bridge will not collapse with very high probability for centuries say.
19:42
For software we don't have, and probably we will not have, this kind of theory. I don't want to be too technical, but if we have a theory of predictability, we will have a different kind of theory. I'm back to your career, because soon I think you begin to develop a center where you can pursue some of these important ideas.
20:12
I'm very interested in the France of your circumstance, because we spent so much time imagining what's happening in California, and you know these other significant centers of computer thinking.
20:28
Are you feeling isolated in France? What is your community of intellectual? In France I did not feel really as French. I was very open, I had
20:45
a lot of connections with the US, with other countries, with Germany, Sweden, Israel a lot. I had a very good friend from Israel, who also was a professor in the US, so I did not feel too much connected to...
21:08
Yes, I was traveling a lot, so I was really connected with a broader community, and I have created a community and was one of the leaders of the community with Americans and other people.
21:24
The center, was it in part an idea to bring this conversation to Grenoble? Yes, so I created this laboratory, it's called Verimag, beginning of the 90s, and the idea is to find some industrial partner to apply our ideas, and we found at that time two partners.
21:46
One was Elbas, at that time it was not even called Elbas, it was a small company called Aerospatiale, and they had only a very small portion of the market at that time. And another company was a company that disappeared today and were building control systems for nuclear plants.
22:08
So the challenge for me at that time was to apply my theory about how to verify a system to build what people call now safety critical systems, so the problem is how to guarantee the safety of an aircraft.
22:25
And at that time, so you see, I was lucky again, at that time, Elbas, they have designed the A320, which is a very successful aircraft, and the A320 is the first fly-by-wire aircraft.
22:46
So what fly-by-wire means? You have for the first time a computer interpreting the commands of the pilot. So the commands of the pilot are not translated through some electromechanical system, but they are
23:07
interpreted by the computer, and the computer gives orders to the electromechanical parts of the aircraft. And this was a great idea that has been first applied by Elbas, but in order to apply this idea, you need to prove that it will work.
23:26
So you need to convince certification authorities, and our technological results, scientific results, helped Elbas with passing successfully the certification of A320, and this was a big success.
23:41
Who in Elbas was intelligent enough to look to your laboratory for solutions? Well, it's very, very complicated, so I cannot name people here, but they made the decision, they had a small company, a small startup, developing software technology for them, and we have collaborated with this company.
24:02
We had a joint lab, so my lab started as a joint industrial academic lab. We had a contract for four years to prepare, to develop the technology, and finally we've been successful at that. It's almost an ideal model for the cooperation of the commercial world, of the intellectual academic world.
24:24
Yes, and at that time for France, this was quite unusual. And yet you proposed yourself as a modest man, you must have taken a lot of energy, found a lot of energy to make this happen. Yes, but this was really an exciting task, and I had a very good team, I had to find good researchers, and I was lucky enough.
24:46
You see, when you have a strong desire for something, a strong willingness for something, then you have a kind of conspiracy of the universe that helps somehow.
25:01
At this time, are you writing many papers as well? I was writing papers, but also I like very much practice, so I don't have as many publications as others, but I have done a lot of things in coordinating people, setting up projects, applying my ideas.
25:27
This is what I like. I like to be a leader. Yes, and happen, in fact. Yes, yes, I like this. Again, the state of the present and the future in your field, let us now, we're now in the late 90s, I just picked this as a time.
25:46
At this time, this is happening, but also you are, are you beginning to speculate on the possibilities of your world? Are you beginning to feel very positive or perhaps disappointed with where your field is going in its questions?
26:08
One of them, of course, would be artificial intelligence, but broadly, how are you thinking about your field in the late? Okay, I had ups and downs, of course, and I have to say here that I have completely broken with this topic, research topic of verification.
26:28
At the big surprise of everybody in my community, I said, well, I don't want to work anymore on that. It's boring at this point? It's boring. I don't expect any new spectacular results, so end of the 90s I changed, and I selected another topic on which I am working.
26:49
In fact, I changed two times. Tell me about the change. So one, that I was interested in what people call now embedded systems.
27:02
So embedded systems are systems that are continuously interacting with an external environment. So from my experience with Airbus, I understood that there are serious engineering problems with these systems that control aircraft, trains, cars, and today we have 95% of the computers are embedded, so are not accessible directly to people.
27:28
They are interacting, they are controlling external environments. So I felt at the time that this could be the next revolution, and I actively worked also to set up a research community in Europe.
27:48
I was for almost 10 years the scientific director of a network of excellence, a European network of excellence called Artist in Europe, and I started this work in parallel with some colleagues in the US.
28:05
So we started in parallel research programs that I believe have been very successful on both sides. What was the question? There are many questions. So the question is how to program computers so that they interact with an environment, a physical environment,
28:29
with adequate response times, and how to guarantee these response times. So the problem here is a bit different from just simply verification of software programs,
28:42
because you have to consider the software in interaction with the hardware. This is a very hard problem, how to understand the interaction between software that executes at a certain time scale,
29:01
and hardware that is more than 100 million times faster than software. So still there are some open problems about how to understand this interaction, and how to predict how the system, the dynamic system, hardware software will behave.
29:25
So this is still very much of an open question. This is still very much of an open question, although today we have theories about that, about how to do that. But there are still some open questions. And then I got interested in systems where you have not a single embedded system,
29:49
so a single computer interacting with the environment, but many of them, and many of them moving, for instance, like now you have in cars. And the problem I'm studying currently is in particular mobile systems.
30:09
So if you have many computers that are mobile, how they communicate, how they interact, you organize the communication.
30:20
And these are very hard problems, because the environment is changing constantly, and you have not only a physical environment, but you have an environment with computers and humans. When is the payoff, if I might use that phrase, when we get it right, when we can figure out embedded systems in that sense, where will we have advanced?
30:46
Ok, so for single embedded systems, I think now we master the technology, and we have very safe aircraft, we have relatively safe cars, but not autonomous,
31:03
because autonomous cars means, or autonomous aircraft is the second problem, the other problem, that they interact with other computers and with other cars, and you have also the human factor. And taking into account the human factor, this is very very hard,
31:22
because humans are not as predictable as natural phenomena. So the challenge today is this, is to reach a vision that some people call the Internet of Things. So what's the Internet of Things? The Internet of Things is, you have now a system of systems,
31:46
so you have embedded systems everywhere, they collect information, they control local environments, and you have an infrastructure, you want to collect all this information, and send them, say, to the cloud. The cloud can analyze all this information, create some knowledge,
32:04
which will allow also some predictability about the whole system, and then send orders to the systems, the embedded systems, that are geographically distributed, and are providing the services at different places for different areas.
32:21
So is this a process that universally excites, or does it seem to terrify some people this next day? Oh, okay. Personally, I'm not terrified. Oh, I know who I am. Okay, yes. But some people may think that this can be dangerous to do that.
32:42
I think that, okay, this is a vision, this is a technological vision, and that raises also scientific problems. I don't know today if this vision is reachable at all. And there are some very open questions about that. Now some people may be terrified for different reasons.
33:03
There are good reasons to be terrified, and bad reasons for being terrified. So some people today are speculating on the fact that systems would become super intelligent, and at some point, the system intelligence will exceed the human intelligence.
33:20
I don't even understand what it means. I mean, logically, it's nonsense, okay? So people are scared about that, and people are speculating for various reasons. Okay, I don't want to say anything about that.
33:41
The good reasons one may have to be afraid is that now we will have an increase in automation of systems that will be providing services everywhere. And as automation increases, you will have only a few people, a few agents,
34:07
making the critical decisions, and that's the real danger. And also this dependency on some infrastructure that is not guaranteed to work perfectly.
34:25
You know that there are a lot of problems with security, for instance. And nobody can guarantee the security of even the best protected system. And we have evidence about that every day.
34:43
So the best we can do, technically speaking, is that we have watchdogs, we have alarm systems that monitor the behavior of the system, and if there is an intrusion, I mean, if the monitors are smart enough,
35:03
we can discover the intrusion. But you cannot avoid intrusions. This is almost, I mean, it's evident, you cannot avoid intrusions. And the reason you cannot avoid intrusions is because all this infrastructure,
35:22
the Internet in particular, has been built in a very empirical manner. No theory, and in a very incremental manner. You see, there is a big difference between the technology we used for Airbus. So for Airbus, there are very strict rules, and this is a closed system,
35:40
not yet interacting a lot with humans or the pilot. And for this type of systems, at very high costs, we know how to guarantee safety and security. For very general purpose systems, the systems are developed in,
36:01
perhaps not by good engineers, without any theory, without any recurseness, and we have no guarantees at all. So the situation is very different. If the systems are open, there are plenty of holes in the Internet, the operating systems or the infrastructure,
36:21
and if you don't understand how a system is built, then you cannot guarantee that there is no hole. So intrusions are always possible. So, just to summarize, a big risk today is that we have some accidents under code with systems,
36:45
especially if they provide some critical services. And this is a big debate. You've heard probably of the discussion about having self-driving cars in big cities,
37:02
all the stories about Tesla cars or whatever. It may be dangerous to have this, because these are open systems for which they cannot be developed in the same manner as safety-critical systems. We have, say, nuclear plants or aircraft on some weapons systems.
37:26
Finally, I want to ask you about the reputation of your field, because I think you've taken some interest in having the world understanding that computing itself is, I think your phrase is, a domain of knowledge.
37:42
It's not a handmaiden. It's not a secondary field to support others. Yes, that's my point. So, first of all, it's clear that computing is not just science, because science studies physical phenomena.
38:06
It's about discovering laws, understanding how nature works. So, computing has some scientific dimension to the extent you can consider some physical phenomena as computational phenomena.
38:24
But computing has started from mathematics, so we have mathematical ideas, so this is not a posteriori knowledge, this is a priori knowledge. So you start from mathematics, and this is Turing's idea
38:43
about what computers are, and then you try to implement this idea by using some physical elements, can be switches of any kind, can be relays initially, electronic tubes, transistors, whatever. So, computers are artifacts, and in order to talk about computing,
39:09
I think the most adequate term is domain of knowledge, because knowledge also is very important for computing. So, what is knowledge?
39:20
My definition of knowledge is that it is information that either allows you to understand the situation, so in particular this is scientific knowledge, or it allows you to build artifacts or to solve problems. So, when you build computers or you are an engineer, you build artifacts.
39:41
So, knowledge is a kind of combination between scientific knowledge and design knowledge or engineering knowledge. Is it a stage beyond information, or is information a knowledge equivalent? Ok, so, no, you have information that is not knowledge.
40:02
So, information is a more general concept. Information is, ok, I don't want to get too theoretical, because there is a theoretical definition of information, but information is a structure that you can interpret.
40:21
And it's a relationship between the mind and the structure, in fact. Now, knowledge is information that is useful, you can use and reuse. It's like a packet of information. So, for instance, the Pythagorean theorem is knowledge. Why is it knowledge? Because if I can apply the Pythagorean theorem, it gives a relationship,
40:45
and I can apply it in many different circumstances to solve problems, ok? Newton's laws are knowledge, ok? So, this is knowledge. It's a relationship that you can, it's a scheme, in fact.
41:05
It's a generic relation that you can apply and apply many times, a packet of information. What about the phrase the information age? It's a tag, maybe, but... Yes, ok, so let me say in a very simple manner what information is.
41:24
So, information is, what information is with respect to other entities that our mind understands very well because of our education. So, everybody understands that we have entities like mass and energy, ok?
41:43
These are the basic entities in physics, ok? Now, information is not mass, I mean, it's not a physical entity. Information is just a meaning you give to a structure.
42:02
So, and the meaning, you can encode it with symbols. So, if I tell you something, I give you some information, you can put it on a piece of paper, you can write it in some memory, whatever, it can stay there forever. So, a property of information that it is non-physical,
42:23
so this is a big debate, but this is my point of view, ok? It is immaterial, it's an entity that's different from matter. Why? Because it's not matter and energy are subject to... You have matter and energy are involved in phenomena.
42:42
To understand them, you need time and space. Information, you don't need time, you don't need space, ok? And it's a concept with which we are not familiar. So, let me say something. I had a very interesting discussion with children. So, I go often to some countries and I give lectures.
43:04
So, someday I was in India and I was explaining what information is in a school and a child raises his hand and says, Sir, does information have weight?
43:22
Come on. So, what do you think, has weight or not? He says, no, I think it has no weight. And you see, information, if... So, in your brain, you have information. Now something happens, or in my brain.
43:40
I forgot everything about my past. I'm not anymore the same person, but I have all the same physical characteristics, the same weight, exactly, ok? So, I like this remark by the young Indian and this, just to finish, reminds me of another joke. This is a French joke. So, the father and the child are eating Gruyere.
44:03
You know this cheese with the huge holes. So, they're eating Gruyere and when they finish, the child asks the father, Dad, where are the holes now? And the information is the holes, exactly, ok? Wonderful.
44:20
Last question I'm going to ask the poet. So, we have data, we have information, we have knowledge, this category. Does wisdom have a role as this? Yes, yes, the concept of wisdom also can be formalized to some extent. Ok, so let me explain the difference between knowledge and wisdom.
44:40
So, you can have plenty of knowledge. You can know plenty of things. Wisdom is the ability to use the right piece of knowledge at the right moment, to solve problems, and this is wisdom. So, it's higher than knowledge. You have knowledge and then wisdom is the ability to use the knowledge you have acquired.
45:05
So, perhaps we're not yet in the wisdom age. I don't know, yes, ok, but I think also that people are not curious enough today, so they have, they don't try to differentiate, to reach their knowledge
45:25
and to have a variety of points of views, ok, to compare them and to think, ok. I find that my generation was much more anxious about what truth is and truthful knowledge is, yes.
45:45
Thank you.
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