The HLF Portraits: Ronald L. Rivest
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The HLF Portraits: Ronald L. Rivest
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No Open Access License:
German copyright law applies. This film may be used for your own use but it may not be distributed via the internet or passed on to external parties. |
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2020
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English
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| Abstract |
The Heidelberg Laureate Forum Foundation presents the HLF Portraits: Ronald L. Rivest; ACM A.M. Turing Award, 2002 Recipients of the the Abel Prize, the ACM A.M. Turing Award, the ACM Prize in Computing, the Fields Medal and the Nevanlinna Prize in discussion with Marc Pachter, Director Emeritus National Portrait Gallery, Smithsonian Institute, about their lives, their research, their careers and the circumstances that led to the awards. Video interviews produced for the Heidelberg Laureate Forum Foundation by the Berlin photographer Peter Badge. The opinions expressed in this video do not necessarily reflect the views of the Heidelberg Laureate Forum Foundation or any other person or associated institution involved in the making and distribution of the video.
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00:00
Axiom of choice
Statistical hypothesis testing
Complex (psychology)
Group action
Confidence interval
Water vapor
Inverse element
Open set
Perspective (visual)
Expected value
Sign (mathematics)
Lattice (group)
Different (Kate Ryan album)
Feasibility study
Hill differential equation
Physical system
Social class
Constraint (mathematics)
Gradient
Median
Complete metric space
Wave
Arithmetic mean
Figurate number
Mathematician
Spacetime
Point (geometry)
Momentum
Wage labour
Transformation (genetics)
Amsterdam Ordnance Datum
Routing
Student's t-test
Rule of inference
Product (business)
Frequency
Latent heat
Goodness of fit
Term (mathematics)
Modulform
Computability theory
Combinatorics
Standard deviation
Distribution (mathematics)
Numerical digit
Model theory
Content (media)
Algebraic structure
Cartesian coordinate system
Limit (category theory)
Quadratic form
Faculty (division)
Formal power series
Spring (hydrology)
Loop (music)
Universe (mathematics)
Musical ensemble
Table (information)
Family
Drag (physics)
State of matter
Direction (geometry)
Multiplication sign
Decision theory
1 (number)
Hierarchy
Set (mathematics)
Insertion loss
Parameter (computer programming)
Mereology
Prime number
Food energy
Order of magnitude
Mathematics
Coefficient of determination
Many-sorted logic
Analogy
Matrix (mathematics)
Position operator
Decision tree learning
Area
Process (computing)
Moment (mathematics)
Physicalism
Price index
Flow separation
Googol
Quantum
Right angle
Resultant
Thomas Bayes
Geometry
Ramification
Classical physics
Computer programming
Trail
Polynomial
Functional (mathematics)
Divisor
Distance
Theory
Power (physics)
Hypothesis
Theorem
Integer
Curve fitting
Time zone
Dependent and independent variables
Fields Medal
Ökonometrie
Graph (mathematics)
Calculus
Numerical analysis
Inversion (music)
Number theory
Inverse problem
Local ring
Spectrum (functional analysis)
00:00
[Music]
00:17
well I'm interested in how you became
00:21
who you are today so I want to start at
00:23
the beginning and I'm gonna ask the
00:26
question based on the lack of knowledge
00:27
are you the son of scientists are you
00:30
the son of people interested in
00:32
mathematics or did you come out of
00:33
nowhere in regard to your subject
00:36
so I grew up in Schenectady New York it
00:38
was born there the Ellis Hospital may 6
00:41
1947 and my mother was a homemaker my
00:46
dad was a flexural engineer and worked
00:48
in the Navy and radar and worked at GE
00:51
Research Lab Schenectady as you may know
00:53
was a town established by GE for a lot
00:58
of production turbines and things like
00:59
that too but my dad worked at the
01:00
research lab which was and still is
01:02
there and he was very interested in the
01:05
new things computers as well as a lot of
01:07
the the radar and other things that he
01:08
were saying you didn't spring from
01:10
nowhere intellectually there was this in
01:13
the household there was already in the
01:14
householder where there was a interest
01:16
in science and technology were you an
01:20
only child I was the oldest of four so
01:23
all this before are you the only one
01:24
whose career when somewhat in the
01:27
direction of your father's
01:29
SoDo Mike myself well I got a brother
01:32
was going down top time he oldest yeah
01:33
and my brother is a retired marine
01:37
biology professor Oh my sister working
01:40
pharmacology and my youngest sister is
01:44
now a physical therapist in Seattle so
01:47
should I imagine a household strewn with
01:51
books on engineering and science or
01:55
actually he didn't bring it home it's
01:58
his particular interest there are a lot
02:00
of toys and various things having
02:01
educational things I remember the World
02:03
Book Encyclopedia you know which we all
02:05
looked looked out there and having a
02:07
number of electronic gadgets and toys
02:09
and that's all into so was that I
02:10
remember my mother although she was not
02:12
a technologist was always asking
02:14
questions so she was very very curious
02:16
Jenny she got us in the mode of you know
02:18
always questioning we're there at
02:21
whatever age you might have felt it
02:23
ambitions specific ambitions for the
02:26
children are you pretty well allowed to
02:29
follow your interests you
02:30
we'll follow they we followed our
02:32
interest pretty much there was no
02:33
expectation that you'd be a doctor or a
02:34
lawyer or a journalist or whatever in
02:36
fact when I was an undergraduate I
02:38
didn't know what I wanted to do and I
02:39
was you know using undergraduate program
02:42
as it was intended to to explore
02:43
different options
02:44
well we already see one expectation for
02:47
you and that is that you go to college
02:48
yes so that at least was in the air yes
02:51
yes yeah and both of my parents grew up
02:53
on farms in Michigan and and my dad
02:56
almost didn't go to college but was
02:58
encouraged by his nap teacher to do so
03:00
huh and my mom went to college and well
03:03
you've you've introduced the subject of
03:05
encouragement by teachers so I want you
03:08
in school let's say at 11:00
03:12
what's the school like what's the
03:15
preparation 11:00 would have been the
03:16
sixth-grade express like that so sixth
03:19
grade we had some excellent teachers I
03:21
remember a biology professor there that
03:22
was very very good and he would come
03:23
around with my abortion so on you
03:25
remember is they no no no mr. place I
03:28
think was mr. police was his name it was
03:31
that's not under them yeah yeah but yeah
03:34
the teachers were very good miss Keon I
03:35
had an astounding school system and I'm
03:37
always meeting people from Miss Kuna
03:39
have done well in their lives as the
03:41
teachers have been let's talk about that
03:42
so it's a suburb of a suburb of
03:45
Schenectady it's near the research lab
03:46
and it had many PhDs among the faculty
03:49
which is unusual for high school in a
03:51
school system high school is now in the
03:54
50s high schools 50s yeah sorry yes
03:58
and is this a time when a lot of them
04:01
are not able to get jobs in universities
04:03
or was it just the habit of the system
04:06
yeah you weren't in that was no task I
04:08
didn't I find since I went to high
04:11
school public high school in the fifties
04:13
although in California that I was
04:15
educated by a lot of very intelligent
04:17
women yeah who as women could not get
04:21
other jobs in the fields that interested
04:24
them they have also been the case yeah I
04:25
just didn't know what their personal
04:26
Sidhu does fair enough
04:28
well let's get you to middle school
04:32
junior high I'm tracking the point of a
04:37
particular spark it may not even happen
04:39
through high school but are you are
04:42
using a needle so junior high school in
04:44
seventh grade
seven yeah that's like seven eight or
04:46
whatever we're right we're terrible I
04:47
think as they are for many kids you're
04:49
going to be really what everybody's
04:50
misbehaving so it was nothing
04:53
particularly loss period nothing else is
04:57
happening right I do remember learning a
04:59
little algebra then but that was about
05:02
it in terms of so now I'm gonna put you
05:04
in high school are you any more mature
05:06
in your intellectual development I still
05:08
was much more interesting it was a
05:09
larger community it was much more
05:12
technically oriented a good very good
05:14
high school teachers were excellent I
05:17
remember some of the teachers teaching
05:18
writing teaching mathematics teaching
05:20
science I was it was a great experience
05:23
just a good high school there are you an
05:25
extraordinary student or just a good one
05:27
I'm a good student I'm a pretty good are
05:29
you good yeah so I remember I remember
05:32
being elected as treasurer of the class
05:35
because I was so good at math right okay
05:38
so I'm glad it's practical already in
05:40
your life wasn't calculus or anything
05:42
but it was if you're in an American high
05:46
school in the fifties you're getting
05:47
counseling as to where to go for
05:50
university what is that discussion like
05:52
I don't remember much of that I didn't
05:54
apply very many places didn't I remember
05:56
being courted by Michigan State which is
05:59
where my dad had gone to because I was a
06:01
National Merit Finalist and they were
06:03
trying to attract national drag my
06:04
enlist there and I ended up going to
06:06
Yale forgive liberal education which I
06:09
think was a good choice on the end heels
06:11
not a bad choice in most cases but one
06:16
doesn't even in the 50s I mean
06:17
competition every decade has gotten more
06:19
and more crazy but even in the 50s
06:21
you didn't just waltz into Yale so your
06:24
grades must have been pretty good Fred
06:27
good grades I had like perfect scores of
06:29
the SATs and things like that
06:30
okay perfect scores in the SATs some
06:33
indicator of something so I was doing
06:35
well in a connect academically and you
06:37
were doing well verbally as well as
06:38
mathematically both both sides of the
06:40
exercise yeah I enjoyed I enjoy the
06:42
writing side of things as well ah yeah
06:43
so that wasn't so usual yeah okay Yale
06:47
bids for you tries to interest you or
06:50
you just do I think I just went through
06:52
the standard application process and it
06:54
was except that they they have a
06:55
interview process where you know there's
06:56
some alum
06:57
local tech community interviewed you and
06:59
sees if you would be a good fit okay um
07:02
the nice thing about undergraduate life
07:05
in America is that the first year you
07:07
don't have to choose a major yet you're
07:09
now you're you're tasting the waters up
07:11
tasting the water you're traveling the
07:14
waters of tell me how you were beginning
07:17
to make decisions about your future
07:20
so well the nice thing about a school
07:21
like Yale this is a liberal school broad
07:23
spectrum of possible career paths and
07:26
interest you could take and and so I was
07:28
trying to decide I didn't know whether I
07:29
wanted to be a technologist or maybe a
07:32
lawyer
or maybe a psychologist or something
07:35
else but you're supposed to be wondering
07:37
it yeah and I enjoy taking the
07:39
professor's on film
07:40
you know and other things so it was a
07:42
good liberal education right I drifted
07:45
in the end towards mathematics and part
07:47
because the mathematics curriculum was
07:48
the least demanding and I could explore
07:50
all these other interests at the same
07:51
time huh so you know I took some classes
07:54
I didn't least demanding to the talented
07:57
obviously it was but you mean just in
08:00
terms of the course requirements course
08:02
requiring with you yeah so the course
08:03
requirements were I'm searching for the
08:07
first sign of a mentor or a particularly
08:10
inspiring figure him or her in the
08:13
mathematics department
08:15
no actually economics there were there
08:18
was a professor there by the name of
08:19
Richard Ruggles who hired me to work on
08:22
computer programming for the
08:24
econometrics Society and so I did that
08:27
several summers working on price indices
08:31
for Latin America and submitting decks
08:34
of punch cards to be run through the IBM
08:36
7090 or whatever was we had then to
08:38
compute price indices and so it was
08:41
technical programming work which I
08:43
enjoyed and got me more familiar with
08:45
computers um many of the people watching
08:49
this won't even be able to imagine the
08:54
state of computer life at the time that
08:56
you were an undergraduate Yale in terms
08:58
of what was available what was being
09:01
thought about it can you everybody
09:03
didn't exist as a major then so I didn't
09:06
have that as a choice if there had been
09:07
a computer science major than I thought
09:09
would have matrix would have done it
09:10
wasn't engineering I took some of the CS
09:13
courses that were in the engineering
09:14
program computers were just starting to
09:18
become part of the curriculum there was
09:20
there's a couple of courses in
09:21
programming that I took mostly they were
09:24
punch card based write punch cards are
09:27
hard to find these days my wife who does
09:28
a teaching these days was looking for a
09:31
deck of punch cards to show her kids
09:33
fourth graders today what a punch card
09:34
was just can't find that you can't find
09:36
them that's hard to find so they're
09:38
around but they're a great computer a
09:40
Museum in Boston there's like there is a
09:43
museum and they probably have some there
09:45
but she was looking for something to
09:46
bring into yes that they could actually
09:47
I would but there's also gonna food
09:51
machine with paper tape which you don't
09:52
see anymore at all so so should I not be
09:56
romantic and imagine you you begin to
10:00
have access to computers in this form it
10:04
interests you but there's not yet a
10:07
Eureka or something about you're sensing
10:10
the future it was something I was
10:11
drifting into I think because it was fun
10:14
it was interesting computers certainly
10:16
had a power that you didn't see and
10:18
other technologies is directly and
10:21
immediately even submit something and
10:22
get results back and it related to the
10:24
mathematics I was doing but there wasn't
10:27
any kinda remember any particular moment
10:29
and that's an interesting question but
10:31
of any particular moment said yes I want
10:32
to be a computer scientist although I
10:34
did drift into applying for computer
10:38
science graduate school which I must
10:39
have made a decision something exactly
10:41
if you ask me out what when did I decide
10:43
that that's what I wanted to do I
10:46
actually don't remember crossing that
10:50
threshold or just sort of drifted yeah
10:52
I'm doing more and more computer science
10:54
this looks like an interesting
10:55
discipline why don't I go try it you
10:58
wind up going to Stanford and we'll talk
10:59
about that obviously but I'm wondering
11:03
roughly how long Stanford has even had a
11:07
ph.d program in computer Stanford had
11:10
just started the PhD for that started so
11:12
I finished Yale in 69 yeah I think
11:15
Stanford started its program in 65 or
11:17
something though so it just have been a
11:19
few years have been a few PhDs out
11:21
you just sound to me less clueless than
11:24
your about what your future is going to
11:27
be because it's a fairly bold decision I
11:30
mean the other would have been to go to
11:32
Matt in mathematics yes well it was
11:34
partly tech field it was partly things
11:37
like let's go live in California for a
11:38
while - okay
11:41
Stanford should be interested so
11:44
Stanford at that point as you say just
11:47
started the the ph.d program in 65
11:50
what other do remember programs are
11:53
there around because you may have
11:54
applied to other programs or maybe
11:57
Stanford was one of the only ones to
11:59
exist so at that time I think I applied
12:01
to MIT as well it did not get in but
12:05
that's a good lesson for people to know
12:09
but and I think I applied a couple other
12:11
places I don't remember MIT
12:13
unfortunately has winter if you had
12:15
gotten into both MIT and Stanford where
12:17
would you have gone it's probably
12:20
Stanford anyway yeah probably yeah so
12:24
you're in Sanford what is the guidance
12:28
such as a PhD student gets to you as you
12:32
arrive in terms of this new field at
12:36
least field so they're establishing the
12:38
curriculum there they're putting
12:40
together courses I got to meet the
12:41
faculty you know Bob Floyd was my thesis
12:44
advisor he taught a marvelous lucky yes
12:46
last on algorithms Don Knuth was there
12:49
so Herman on many other people were just
12:52
just a fantastic group of faculty and so
12:55
part of it was the coursework and taking
12:57
the the wonderful courses they were
12:58
teaching and part of it was trying to
13:00
integrate into research and figure out
13:01
what kind of a research program you
13:03
wanted to do and and there was also at
13:07
the time just to set the context there
13:09
was also the Vietnam War and so I was
13:11
worried about the draft I'm trying to
13:14
figure out what I my life might be like
13:16
should that become a concern I did end
13:19
up working at the artificial
13:20
intelligence laboratory up in the hills
13:22
behind Stanford it's no longer part of
13:25
Stanford campus at least not part of the
13:27
teaching campus anyway and we had I
13:30
remember there was a cart there I think
13:32
it's a computer museum now where they
13:34
were
to talk about autonomous vehicles this
13:36
was one of the very first the Stanford
13:38
card trying to drive around the parking
13:40
lot and not hit any of the cars so I was
13:43
working on some of the coding for that
13:45
which allowed me to get a deferment for
13:47
a while anyway on the right the Vietnam
13:49
War situation I'm very interested in the
13:53
and the past others this the formation
13:57
of a community interested in questions
14:00
obviously Berkeley is across the bay are
14:04
you sensing as a graduate student a
14:06
larger discussion going on some of that
14:09
yeah there was for example one of the
14:12
early papers that I worked on was a
14:13
medium fast median finding algorithms
14:16
are given a set of numbers how do you
14:17
find the median of that yeah that was a
14:19
sparked by some insights that Manny Blum
14:23
had and Bailey's at Berkeley uh-huh and
14:25
connected with Bob Floyd and then some
14:27
of the graduate students and bond Pratt
14:28
Bob target and myself got involved as
14:30
well and so that that there is a larger
14:33
intellectual big in the community there
14:35
yeah as another example Don Knuth I used
14:39
to run I was it was a weekly or monthly
14:41
it's fairly often sessions at his house
14:45
where he would might speakers in and so
14:48
I remember dick carp coming down from
14:50
Berkeley talking about NP completeness
14:52
and so on to week so there was a
14:54
community was starting to it was a bit
14:56
of a distance but people would come back
14:57
and forth and I remember going up to
14:59
Berkeley wants to talk with some of the
15:00
researchers there so so there was some
15:02
back and forth between Stanford and
15:04
Berkeley but computer science was still
15:06
very small there's no industry you look
15:08
like you have now all right which had
15:10
its advantages yes because you could
15:12
talk to everyone yeah at that point yes
15:14
yes yeah um at the again you're doing
15:19
graduate work you're man who likes to
15:20
talk about ideas give me some insight
15:25
into how you're beginning to think in
15:27
terms of his direction to go and maybe
15:31
the opportunities are not vast or maybe
15:33
they are but how do we get you well
15:36
let's just say through to your
15:38
dissertation so my dissertation about
15:41
the work the research I did ended up
15:42
focusing on algorithms and I was
15:44
interested in sort of the combinatorics
15:46
the algorithmic content of how they got
15:50
to get computers to do complicated
15:51
things there are certain search
15:54
algorithms remember also to bring up a
15:58
side directly part of my thesis was on
16:00
search algorithms and I remember being
16:03
concerned at the time about the ethical
16:05
implications of this you know that if we
16:06
can make search so much more efficient
16:08
you know what what about surveillance
16:10
and what could the government do with
16:11
this and so yes so we see all this
16:13
resonating today with both the
16:15
government and Google and other
16:16
companies and actually throughout your
16:18
career yeah because and we will talk
16:21
more about the ethical issue because I
16:22
think you have clearly early
16:24
demonstrated and interests in the
16:26
ethical implications
16:28
interesting to me that it happened so
16:30
yeah so early in the process
16:32
um are you speculating at all this may
16:36
be a classic retrospective question
16:39
where we now know what's happening at
16:42
that time but at the time you may not
16:44
have known about the the future of this
16:47
that field only but effect on society
16:50
these perhaps it's hard to tell them you
16:53
would hindsight you can say anything but
16:55
yeah yeah you don't particularly
16:57
remember feeling you were now rushing
17:03
toward the future in a way perhaps one
17:07
way of setting context is to make it
17:08
just to say that I'm a big fan of
17:10
science fiction as well and so imagining
17:12
what the future could be like including
17:14
the future of computers if you know the
17:16
azimoff at Heinlein were big there and
17:17
other writers now but you know trying to
17:20
speculate as to where the field might be
17:22
going as a society or technologically
17:25
certainly always been a part of what I
17:27
do and so I think that's a part of also
17:31
part of the if you will the culture in
17:33
which you're operating professionally
17:36
you have any US I think I think that
17:38
that's for two I mean I think the AI
17:40
theme which has always been a part of
17:42
what I you - well not as strong as
17:44
perhaps some of the other things but
17:46
it's you know Frank can you build an
17:48
intelligent computer yes what was an
17:50
issue that arose certainly in graduate
17:52
school people were thinking about those
17:53
things and throughout my career I've
17:55
bounced back and forth thinking about
17:56
these things off and on but
17:58
the larger implications of what computer
18:01
science might have an impact on society
18:02
what can you do with computers is one of
18:06
the big questions still of the day right
18:08
right
18:09
with attendant fears and hopes yes like
18:13
are you are you at this point I'm also
18:18
interested in the relationship between
18:21
mathematics and computer theory but not
18:27
in its formal aspects although I'm
18:29
interested if you want to talk about
18:31
that as in the position somebody who had
18:35
elapsed mathematician although of course
18:37
mathematics is in computer era choosing
18:41
this field whether this was considered
18:45
odd or you had gone bad or you were in
18:50
worthy of mathematical theory I mean no
18:53
I think I think it's not like that at
18:54
all I think I think the the there was a
18:56
blending of these fields when I was at
18:59
Stanford Don Knuth was growing his group
19:02
of researchers there and to the people
19:04
in particular that I ended up spending a
19:05
lot of time with where David Connor was
19:07
a professor of combinatorics and washing
19:09
Shabbat both was also similarly working
19:11
in combinatorics and they taught courses
19:13
which related to the combinatorics of
19:15
algorithms and graphs and so on but also
19:18
talked to the algorithmic side of things
19:19
as well so I worked with it so I think
19:21
he saw a blending of these keys fields
19:23
more than any kind of in a way that that
19:27
leads me to the opposite question why
19:29
did computer theory break off into its
19:32
own program why didn't it stay within
19:34
the mathematics so computer theory is in
19:37
fact I mean it different places it's
19:41
different things I mean when I came to
19:42
MIT here there was an effort at one
19:45
point by the part of them on the part of
19:47
the mathematics department to take the
19:49
theory group out of computer science and
19:50
that's what I would move it in there we
19:52
declined that invitation but it was an
19:54
interesting one and nonetheless even so
19:56
right now and at MIT in computer science
19:59
theory we live within the computer csail
20:02
laboratory which is interdepartmental
20:04
and has mathematicians and computer
20:07
scientists both in it and we were side
20:09
by side
20:10
all the time so it's it's a it's clearly
20:12
a computer science theory as a feel that
20:15
spans both computer science and
20:17
mathematics right and people are happy
20:18
with that back and forth in the blending
20:20
your you've already gotten yourself to
20:26
MIT but that's fair enough the the the
20:29
the the PhD is well-received yeah I
20:34
think a PhD MPH sees a demonstration did
20:37
you've done some research and right so
20:39
that was but it wasn't a breakthrough
20:42
and it wasn't a breakthrough
20:43
okay it was any waves really good to
20:46
know because the stages of a career are
20:48
very interesting yeah all the time it
20:50
was probably badly written too I think
20:52
I've learned to write better since then
20:53
so even though you were not a bad writer
20:56
as an undergraduate well it did I think
20:59
it's the technical writing is hard it's
21:01
challenging okay oh why you have to put
21:06
your head in the mind of the reader
21:07
first of all and you have to be very
21:09
clear about the terms are using and that
21:11
they're all well defined and the things
21:12
that those do building the structure
21:13
right the usual challenge of writing is
21:15
trying to communicate the complicated
21:17
structure to an audience that right
21:18
doesn't know any of that the begin with
21:20
the goal is that lyricism the goal is
21:23
explanation that's right although you
21:25
know lyricism in the sense of
21:26
conciseness and elegance play a role in
21:28
computer science but not not poetry I'm
21:31
very struck this is really just aside a
21:34
question we don't have to belabor it but
21:36
just the use of words that are aesthetic
21:39
words that mathematicians and computer
21:42
theories which are a version of the same
21:44
use elegance of course and there is a an
21:48
act abuse of that language absolutely
21:50
yeah I know that the simplicity and then
21:52
trying to come up with theorems that are
21:55
cleanly stated and simple to understand
21:57
really matters in the field you can come
21:59
up with very complicated structures and
22:00
things that are true but interesting
22:02
because they're just too complicated and
22:04
your head around and so people look for
22:05
the simplicity right well you haven't
22:08
yet set the world on fire but you're not
22:10
doing badly you get your PhD what do you
22:14
do next
so after the PhD it was interesting the
22:16
ph.d program at Stanford had people from
22:19
all over the world including a number of
22:21
Frenchmen
22:22
and so geo Khan and Shanti malware two
22:24
of the colleagues I had there and they
22:26
said Ron why don't you come to a postdoc
22:28
in France afterwards I said that sounds
22:30
marvelous I love to travel I had taken
22:32
French in high school and I knew some
22:34
French I figured I could get along and
22:37
so I'd accepted we went there for a year
22:39
lived in Paris this is that inria
22:42
Institute Nationale de facie informatica
22:44
dominique right look at the accent my
22:47
French is mostly guys and so that was a
22:52
postdoc working with John female mostly
22:55
on algorithms and so on - it was an
22:57
interesting challenge one of the
22:58
interesting aspects of it was that they
23:00
hadn't told me when I was accepted but
23:02
the working language would be French so
23:06
I were there all day I'm talking French
23:07
all day and exhausted it but your French
23:11
was up to it got to got to be up to
23:13
Augusta and got to be after three mum we
23:16
know the language we know that you're
23:18
having a marvelous time in Paris
23:20
but what is the quality of the inquiry
23:23
there at that point so I think it was
23:26
good I was afraid the French Research
23:28
Lab was a first-rate research lab people
23:31
they were doing interesting work largely
23:32
theoretical and the work I was doing was
23:36
Zhang Jian I was or combinatorial Naga
23:38
rhythmic but it was it was good stuff I
23:42
was pleased with what we did there it
23:45
was gonna be a limited time there
23:46
because you you know you had to get on
23:49
with your career how do you make that
23:50
decision so it was a clearly a one-year
23:53
postdoc and so towards the end of that
23:56
year I have to go around the u.s. dude
23:58
it's like took time off in the spring to
24:00
travel all around Seattle and under San
24:03
Diego and Carnegie Mellon and MIT and
24:05
everywhere big loop around the country
24:07
try your what the opportunities were
24:09
right and I became persuaded that MIT
24:11
was the place education kind of offers
24:14
but MIT is its own argument for itself
24:17
but what about what they were doing at
24:20
that point that might have so there was
24:22
a good theory group here at MIT at the
24:24
time Albert Meier was was the the person
24:26
hide the main contact with Mike Fischer
24:28
was here at the time number of other
24:29
faculty were we're here then so it was a
24:32
clearly a place where a theoretician
24:34
could come in
24:36
work happily and and I think things
24:39
we're exciting at the time we the P
24:41
equals NP question had just started
24:42
bubbling up with further ways and people
24:45
were wondering could we've resolved that
24:46
and there was questions of circuit
24:48
complexity and just algorithmic
24:49
questions in general were we're of
24:50
interest of course in the course of your
24:53
career which will will will will get you
24:56
as it develops you wind up coming up
24:59
with insights with colleagues and so
25:02
forth which we'll talk about that have
25:05
very profound practical application but
25:09
the fellow who's just shown up at MIT is
25:13
a theorist who is or isn't interested in
25:17
the application of his ideas so mit has
25:20
a culture which is very much practice
25:24
oriented as well so they've played way
25:25
and when mi MIT likes theoreticians but
25:28
they also would like to see people spam
25:31
the bridge so there wasn't the the
25:33
practice theory gap and so there was
25:36
encouragement to to do that all
25:38
particularly the teaching that some of
25:40
the introductory and of course is I help
25:42
teach we're systems classes and things
25:44
like this so computer systems not but
25:46
not just the theory classes so there
25:48
were there was encouragement in those
25:50
directions I wasn't personally in terms
25:53
of the problems I was picking at that
25:55
time White's oriented towards the
25:57
practical stuff as I became later yes
25:59
but but it's in the end there was there
26:01
was encouragement motivation to think
26:03
broadly dinner in part just interact
26:05
with your colleagues better write in
26:07
part because you know you want to have
26:08
impact on society you've already made a
26:11
decision and maybe also the time the era
26:14
but you'll you'll tell me one way or the
26:16
other
not even to consider going into industry
26:18
I mean you you sound like you are on so
26:22
what an academic to reject yeah pretty
26:23
much one of the I mean there were some
26:26
research labs and I looked at Sandia
26:28
laboratories for example when I my big
26:30
tour of the country and there wasn't the
26:34
kind of industrial research labs there
26:37
are now so really the kind of questions
26:42
were interested in asking yeah you would
26:46
not have gone to uh there was no place
26:49
you know the industry didn't really
26:50
exist at the time I mean PC hadn't been
26:52
invented yeah it's the year that you
26:55
come to a mic so I come to MIT in 74
26:57
okay right the PC wasn't gonna happen
26:59
for another six years anyway or
27:00
something so is this whether we talk
27:04
about going to industry there really
27:05
wasn't an industry in any sense like
27:07
there is now it is just you know many
27:09
orders of magnitude different so know
27:11
tortured decision-making this was this
27:13
was a clear path yeah you know and I had
27:15
done teaching at Stanford I thought
27:18
summer courses I've done been a teaching
27:21
assistant for a number of terms I
27:22
enjoyed the teaching as well as the
27:24
research so I think the academic path
27:26
was pretty much clear we're very close
27:28
to a Eureka moment at this point that's
27:33
because the work year you're doing with
27:35
colleagues and you talked about the the
27:38
coming to the the insight in the end
27:42
that will mark your career really so so
27:46
the the work at MIT here was was
27:48
primarily algorithmic and characters are
27:50
looking for efficient ways of doing
27:51
things and a lot of the work of a
27:53
theoretician in computer science is
27:55
precisely this trying to determine which
27:57
problems you can solve it efficiently
27:58
and find a good algorithm for them if
28:01
you can and which problems are hard
28:03
intrinsically so it said that's
28:05
separating that we were there's just
28:06
hundreds of problems that you might want
28:08
to look at it turns out that you could
28:10
say some of them were easy some of them
28:12
are hard and some of them you're not
28:13
sure but they're clearly the same and
28:16
something that just recoding zuv the
28:17
same problem so that sifting out of
28:19
these various problems was was the bread
28:21
and butter of what was going on in
28:23
theoretical computer science at the time
28:24
yeah trying to figure out which problems
28:26
are easy on a computer which problems
28:27
are hard and so that was part of what I
28:29
was involved in looking for good
28:31
algorithms I was also thinking about
28:33
things like P equals I think at the time
28:34
to try to prove the certain problems
28:36
were hard well I didn't have the tools
28:38
of the time in fact that's still an open
28:39
problem uh very much so
28:42
so so that drifted into working with a
28:46
number of students on a variety of
28:48
things some of which involve things like
28:50
one-way functions and some of the
28:52
cryptographic things I was working with
28:53
a student by the name of Steven boy AK
28:55
who was was now
28:57
the NSA on you no inverse making matrice
29:01
industries and when when our matrix
29:02
inverse is easier to work with the
29:05
matrix themselves and things like this
29:06
so there was a lots of stuff in the air
29:11
about complexity applied to computation
29:14
which problems are easy which are hard
29:15
and then the Eureka moment is as you
29:18
said arose when Steven boy actually just
29:21
mentioned gave me a paper from Diffie
29:23
and Hellman which said you know New
29:26
Directions in cryptography and that
29:29
paper was really what changed my life in
29:31
many ways it said here's a set of
29:33
problems that we don't know how to solve
29:35
but which looked like they could have
29:37
theoretical interest in practical impact
29:39
and they're absolutely right they said
29:41
this this is a beautiful paper nicely
29:43
written and said here's the idea the
29:46
vision of a public-key crypto graphic
29:48
system and some ideas as to the kinds of
29:52
things that relate to that and what how
29:53
that might be achieved but they didn't
29:54
have a working solution and so that why
29:57
did we need a key system why-why-why
30:02
public key system yeah yeah yeah so the
30:06
vision was that everybody could make up
30:08
their own public keys and distribute
30:10
them publicly without the need for a
30:14
centralized approach it's a little bit
30:17
like the appeal that Bitcoin has today
30:19
where Bitcoin is a decentralized
30:20
cryptocurrency yes without having a
30:22
centralized issuing rights all right
30:25
well the public key vision is a bit like
30:27
that although in fact you need to have
30:28
some support for authenticating public
30:31
keys if you give me your public key how
30:33
do I know it's really you that's giving
30:34
it to me right so there's some of that
30:36
it aspect to it but it was a
30:38
decentralized flavor and it really fit
30:40
very well with the about to be born
30:44
ecommerce market rather than the
30:47
hierarchical sort of military situations
30:50
that exists very closely so it was a
30:52
different thing and one of the things
30:54
that I found most inspiring about the
30:57
diffie-hellman paper was their
30:59
discussion of digital signatures so you
31:02
can take a message and you can append
31:03
something that comes from you as clearly
31:06
from you can be verified as coming from
31:07
you right verifies that it's from you if
31:09
your
fyz that it's the content that that
31:11
message was was signed by you so it's
31:14
the electronic analog of a handwritten
31:16
signature and that was really novel I
31:18
wasn't just confidentiality it was a
31:20
sort of authentication that was achieved
31:22
there and that I found to be a exciting
31:24
notion as well and ahead as it turns out
31:26
many more ramifications down the road as
31:29
well so how do you dive into this I mean
31:32
how do you is there a particular problem
31:34
you embrace so they said basically you
31:39
know you need to have inverse problems
31:41
you need to have something that's easy
31:42
to do but hard to undo right so it's
31:44
sort of a one-way function the kind of
31:46
thing I've been talking with c-boy yak a
31:48
little bit on there and they gave some
31:50
ideas for some ideas but the world is
31:52
open you can take any kind of problem
31:54
you like and in fact we're still looking
31:55
at problems trying to see which one's
31:57
fit this this model right so you can
31:59
take a problem which is hard to hard to
32:03
invert basically turn it into a public
32:05
key cryptosystem with quantum and
32:08
computing and things like that nowadays
32:09
the question is what's hard to compute
32:11
make it may have changed the ground
32:12
rules may have changed over those days
32:14
we had conventional computers classical
32:16
conveyors at the time I was co-teaching
32:18
a class on discrete mathematics and we
32:20
were talking about number theory at the
32:22
time so that was very much in my mind at
32:24
the time and so we were looking at the
32:27
approaches that a number theory can
32:29
carry they say we so I got ID Jameer and
32:32
my Needleman involved uh sooner on this
32:34
product they said I'll you know I'd love
32:35
to talk with you guys about this suite
32:38
of problem you bring the question to
32:41
them I bring the question to them
32:42
I had this paper I said this is an
32:43
exciting paper we should we should think
32:45
about these questions and I started with
32:47
with Adi and he odd he's always
32:51
enthusiastic about new directions and
32:53
new problems so we started start with
32:55
him and then we brought let into it
32:57
audience and given the context of the
33:00
discussion we just had about math and
33:02
computer science yes Adi and LAN we're
33:03
in the mathematics department here at
33:05
MIT of time we had offices adjacent to
33:08
each other here in the lab for computer
33:10
sciences that was called but you know so
33:12
I was a computer scientist they were
33:14
mathematicians the laboratory was set up
33:16
to be interdisciplinary and really
33:18
achieved that purpose of it at this
33:19
point
33:20
mathematicians working together with
33:21
computer scientists and some since we
33:23
would all say were theoretical computer
33:25
scientists right right technically we're
33:26
different departments now there's no of
33:30
course simple answer to this but I'm
33:32
gonna ask the question too because I'm
33:35
interested also in just the process of
33:38
collaboration yeah how does it work in
33:43
this context I mean it's the three of
33:44
you that in the end came up with the
33:48
direction that Dell yeah no
33:50
collaboration as usual I mean
33:52
collaboration actually it's interesting
33:53
to talk about collaboration over the
33:55
decades because just a step back up yes
33:58
it's always about it one of the works I
34:00
did at Stanford as I mentioned earlier
34:02
was this fast median finding algorithm
34:04
there were five of us on the paper and I
34:06
remember the program committees saying
34:08
what is this you can't all be co-authors
34:10
you know you're just trying to get
34:12
travel money for the graduate students
34:14
involved or something like this it
34:15
really was caught you know co-authored
34:17
in a collaborative way but that was
34:18
unusual then collaboration is nowadays
34:21
much more a thing and much more common
34:24
and routine than it was back then at the
34:26
time Lenin Adi and I started working the
34:28
other three of us working together
34:29
that wasn't so uncommon but it wasn't I
34:31
mean single authored papers were perhaps
34:33
much more common then than they are now
34:36
so how do you how do you work on a
34:38
problem together you sit down you talk
34:39
about it what what are the constraints
34:41
what do we know what are the approaches
34:43
we generated lots of ideas about could
34:46
we use number theory could we use some
34:48
lattice based kinds of things can we use
34:51
some other kinds of thing other
34:52
constructions that we come up with there
34:53
are lots of ideas about how you might
34:54
try to do this so we just sort of like
34:56
generating ideas as to what might work
34:59
and mostly they didn't work this is a
35:03
silly question but it it hits on a more
35:06
important one and that is obviously
35:10
there's trust in the collaboration I
35:12
don't mean in terms of glory-seeking
35:14
although that's a factor it's in human
35:17
nature but separate from that does
35:20
somebody come up with an idea and I'm
35:22
simplifying it of course and the others
35:24
say that's the stupidest thing I've ever
35:25
heard but nobody disparages ideas you
35:27
know that's probably the wrong way to
35:30
put it but they're excited by that and
35:33
we'll
say you may have something cryptography
35:36
is interesting is you don't know whether
35:38
an idea is gonna work or not users say
35:39
here's a construct it looks you know
35:41
here's the way you would encrypt
35:43
something here's the way you would
35:44
decrypt something and then the key
35:46
question is can the adversary also
35:48
decrypt without the knowledge of the
35:50
private key right and so that's a
35:53
computational question which you may not
35:55
know the answer to and you may not so
35:57
even when we publish to the RSA paper W
35:59
you know we didn't know whether it would
36:01
ultimately be secure or not because you
36:04
know the key problems involved were open
36:07
problems and a cryptography is very much
36:10
like that the difficulty for the
36:12
adversary in breaking these schemes are
36:14
generally open problems they may be
36:15
problems that have been studied for a
36:17
while and look like they're harvest
36:18
Hoos are hard we don't know how to prove
36:20
problems are hard very well yet so
36:23
nobody says it's stupid or somebody says
36:25
now here's a way that you can probably
36:27
break it now you do this inadequately
36:29
it's breakable and when Adi and LAN and
36:31
I had this you've probably heard this
36:33
story before but you know we had sort of
36:36
a role different roles to play audio I
36:38
would come up with ideas more often and
36:41
Ladd would be the one to sing you know
36:42
that's not gonna work guys here's a way
36:44
to break it so he was expert at breaking
36:46
schemes that we had so we went through a
36:48
whole number of different ideas but not
36:50
even I did not to break the month so how
36:52
to take a part that's so it helps me
36:54
understand yeah how you would work
36:56
together also I guess implicit in what
36:59
you're saying is maybe one day with
37:02
different stage of computational
37:04
capacity and Sun and on what is now
37:07
unbreakable may turn out to be breakable
37:10
one yes absolutely the things change so
37:12
back back to the collaborating think for
37:13
a second I mean that's one of the
37:15
lessons that was learned during World
37:17
War two apparently was in Germany the
37:19
cryptographers who were making codes in
37:21
the Crypt analysts who were assessing
37:22
their security and daring to break them
37:24
were in different buildings in different
37:25
camps and they didn't communicate enough
37:27
and had they done so they might have
37:29
realized that their Enigma was was was
37:31
breakable Wow so I think we learned
37:33
today that it's very helpful to have
37:35
people working both sides of the fence
37:37
working together to build codes so
37:40
anyway that that was and then we're
37:42
talking about
37:44
I gotta track it with that well we're
37:46
really just developing the idea that
37:49
will make the difference yes we had to
37:52
we had a number of different ideas that
37:54
we looked at and the thing that turned
37:57
out to be RSA was not the first it was
37:59
you know the 40th or something right and
38:03
eventually you know I think it was me
38:06
that put the with the pieces together in
38:08
the particular way that ragaar say but
38:10
there were pieces that we'd all studied
38:11
carefully different framework something
38:13
so put it together so yeah this might
38:16
work so again it was another proposal
38:18
that one of you know 40 wanders right at
38:20
that right and it could have fallen down
38:22
like the others it was nice and clean
38:25
and simple so it seemed like it had a
38:27
nice structure to it that you know could
38:29
have turned out to be another something
38:32
for the Dustin if I had seen a way to
38:35
break that but you're developing
38:37
confidence as a group that this this is
38:40
something worth testing worth writing up
38:44
so that was writing yeah the way these
38:46
things work is you have an idea in the
38:48
field and cryptography these days you
38:50
publish it and you say you know we don't
38:54
see how to break this it seems like it's
38:56
here's what we can figure out about its
38:57
strengths it relates to these other
38:59
computational problems like factoring or
39:01
something like that and you say these
39:03
are this is what we know but you know
39:06
it's an open problem to assess the
39:09
security yeah it can you as a community
39:11
now figure out the imitation
39:14
Invitational to tell you the limitations
39:17
yeah yeah yeah yeah and in fact the
39:20
process for coming up with the new
39:21
crypto systems has changed over the
39:23
years to be one that's very much focused
39:25
on standardization and a community
39:28
effort so at the National Institute of
39:30
Standards technology has done a
39:31
marvelous job of running competitions
39:33
for the submission of it cryptographic
39:35
algorithms and and having community
39:38
conferences and community efforts to try
39:40
to break things and so that's what's
39:41
what's needed to really assess the
39:43
strength of it because we don't know how
39:45
to prove things are hard that that's the
39:47
problem with cryptography is we don't
39:49
know we don't have the technology it's
39:51
one of the big open problems in computer
39:52
science how do you show that a problem
39:54
is really
39:55
hard yeah let's go ideas on how to kind
39:59
of maybe do it fast but it's it's a
40:01
can't show that it's gonna you know
40:03
how's it hard why is it hard my word
40:05
case where skates how do you there Holly
40:08
it's hard on the average how do you this
40:10
paper works omitted it actually is an
40:17
answer that people have been looking for
40:20
it seems to seems to have worked well
40:21
still stood the test of time so the
40:24
paper came out it was it was a proposal
40:28
everything yes you know we said this is
40:31
well we know this is what we think we
40:33
can do with this it doesn't seem to
40:34
answer the question the Diffie and
40:36
Hellman raise and they're wonderful
40:37
paper New Directions cryptography it
40:39
interested in the framework that they
40:41
gave and it really said now there are
40:44
new things you can do in cryptography so
40:46
here's one that's an idea that's based
40:48
on factoring that allows you to achieve
40:51
a public key cryptosystem not only the
40:53
encryption side but also the digital
40:54
signature side would write which was
40:56
really relies on particular did you hear
40:59
from them by the way we talk we talk to
41:01
them and then they they were you know
41:03
interested in appreciative right
41:05
supportive things I need to get you now
41:08
from this point well to what we've been
41:12
talking about the realization that this
41:14
might actually be a solution to the
41:17
implementation and the effect of the
41:20
field yeah so it was the the
41:22
implementation issues were interesting
41:24
because at the time computers were slow
41:26
they were very much slower than we had
41:29
today I mean other 10,000 times slower
41:31
so you know and so the process of
41:33
finding prime numbers even which is an
41:36
essential part of the IRS I think when
41:38
you multiply two large prime numbers
41:40
together yeah finding those prime
41:41
numbers could take a half an hour on a
41:44
computer or something like this yes it
41:45
was Norman that wasn't the numbers that
41:47
are considered short by today's standard
41:49
so you know those have changed over time
41:53
with Moore's law and computers getting
41:54
faster but at the time the
41:56
implementation was actually a serious
41:59
issue this was a proposal that in some
42:03
sense it was feasible because of the
42:05
algorithms involved were all polynomial
42:07
time
42:08
if you look at how much time was
42:09
actually required by the polynomials
42:12
you know it took too much time to be a
42:14
really a practical interest yet that
42:17
that would soon change with as computers
42:19
get faster well one of the things we did
42:21
early on was to get interested in chip
42:25
design so MIT at the time was in the
42:28
process of building up a capability in
42:33
VLSI fabrication so designing and
42:36
implementing large-scale chips to do
42:38
computations of various sorts so we
42:40
actually put time in about 1980 to
42:43
design and implement a prototype or a
42:46
chip that would do the RSA computation
42:48
huh would find a large prime numbers
42:49
that would do the encryptions onto with
42:52
special-purpose circuitry like that you
42:54
can actually make it very feasible in
42:56
the end of snow most special-purpose
42:58
chips aren't needed so much because the
43:00
chips are pretty fast now but it was it
43:03
did the job
43:04
I mean it demonstrated feasibility the
43:05
time difference it was also an
43:07
interesting just intellectual experience
43:08
for us - how do you design a circuit
43:10
chip Justin I'm gonna get real
43:14
practically practical here and ask about
43:18
the economic implications of this
43:21
because they feel and the have proven
43:24
profound in terms of your even patently
43:30
this this concept and perhaps presiding
43:36
over in the private as well as the
43:37
public world its implementation how do
43:40
you think about that so at the time we
43:43
didn't know much about startups I think
43:46
the culture of computer science
43:47
departments has changed hugely since
43:50
then at the time there were a few
43:51
startups but not many if you try to find
43:53
colleagues who had done startups or
43:55
things like that right it's not like
43:56
Stanford where everybody's out of their
43:57
office golf during there's your star
43:59
today or something so but we said yeah
44:03
this looks interesting it looks usable
44:04
and MIT was supportive of efforts like
44:07
this so they said well this sure will
44:08
pad it and then we said well the set up
44:11
a little startup company - yeah there
44:13
might be some applications I never
44:15
application sketched in the new
44:16
directions paper we were thinking of
44:18
some - but mind you the
44:20
worldwide web and not yet been invented
44:22
and so all of the applications that flow
44:26
out of that
were still in the future and during the
44:29
80s when when the company was trying to
44:33
get going it was all very difficult
44:35
because there were no large-scale
44:39
applications like you would get by
44:40
having Amazon sales or something like
44:43
that or ecommerce in general so from the
44:47
time that we invented the system through
44:49
the 80s up until the invention of the
44:53
World Wide Web it was pretty much a
44:55
barren market markets had to be created
44:57
when the web happened things changed
45:00
like that it was it was an amazing
45:01
transformation all of a sudden everybody
45:04
was using computers in the web to do
45:06
commerce transactions had to be verified
45:09
they wanted to be encrypted often you
45:12
wanted to authenticate things you needed
45:14
digital signatures so things changed
45:15
remarkably with the invention of the web
45:18
if you read David Kahn's book about the
45:21
history of cryptography he talks about
45:23
one of the large impetuses to
45:25
cryptography earlier which was the
45:26
invention of radio and during World War
45:29
1 radio was used for commanders to talk
45:31
to their jerseys - but everything was
45:34
broadcast so people could listen in you
45:36
had to have cryptography so the
45:38
invention of radio was was a first
45:39
impetus for cryptography the world wide
45:43
web is really the second that sort of
45:44
the same kind of area nice parallel so a
45:49
short layman's version of this is you
45:53
didn't own the rights at a point where
45:55
it was paying off in a big way right
45:59
right and you lose the rights and the
46:03
natural course of a patent right that
46:06
expired right housing or something like
46:07
that so it's it's gonna allow me to
46:09
introduce a very important thing in your
46:12
life which is even the ethics of this
46:14
much of your approach in general anyway
46:17
has been little fasten flowers bloom you
46:21
know let as many people into the use of
46:23
this as I can that I prepared to say
46:25
yeah I think so I mean I think for this
46:28
particular scheme we founded a company
46:30
and there was a patent because I think
46:33
that's the only way to
46:34
get the energy in the investments and
46:36
the food right to get that out there but
46:38
then again the goal was to market that
46:39
did you say a lot of thousand
46:41
applications being yeah with the support
46:43
of this company and the software
46:44
provided by this company you can have
46:46
lots of applications using it it wasn't
46:48
trying to be restricted was trying to
46:49
get things out there I really want to
46:52
belabor because we don't have a lot of
46:54
time this Labor's not the right word the
46:56
the ethical questions that come into a
46:59
researchers life your kind of research I
47:03
guess part of that is the whole question
47:06
of who has access to who can use it but
47:09
also in the course of your life you come
47:13
up with ideas that you've deliberately
47:15
not taken possession of so to speak that
47:20
you've wanted used and make decisions
47:24
that allow it to happen widest use can
47:27
you talk about that and because it is
47:29
ethical look the question is well so I
47:30
think the ethics of cryptography are
47:32
very interesting mostly the community
47:36
runs in an open manner like you're
47:39
talking about where people publish
47:40
designs and so on - occasionally they
47:43
will patent and so on - but I think that
47:45
the effort in cryptography these days
47:47
has been pretty much through the
47:49
standardization route people won't use
47:50
cryptography unless they're standardized
47:52
and and so that that's the route that
47:54
people take now that's the recommended
47:56
path you want a cryptographic algorithm
47:58
you look for the US national standards
47:59
you say what what are the algorithms are
48:00
or published well that said back to the
48:03
ethical issues of cryptography one of
48:06
the big issues then and even now is
48:09
government access to encrypted data and
48:13
so I've been involved with that debate
48:16
since the early days in the 90s the
48:20
government tried to encourage everybody
48:23
to use a certain encryption chip which
48:26
wouldn't not only encrypt the data but
48:28
also allow them access to the plaintext
48:30
right right and that didn't go well and
48:33
I was happy the other side to encourage
48:36
that it not be adopted and today we see
48:39
the US government pushing for access to
48:42
iPhones where Apple and Facebook are
48:46
saying you know this if you try to put
48:47
backdoors
48:48
- these phones you will weaken the
48:50
overall infrastructure be worse for
48:51
security over all the benefits you get
48:54
aren't equal to the losses that you will
48:57
see you will suffer and so the ethics of
49:00
cryptography these are important policy
49:01
issues they deserve debate their
49:03
profound but but you know I think the my
49:07
taster the answers are pretty clear that
49:09
you we need good technology we need good
49:10
strength particularly with foreign state
49:12
actors attacking us left and right not
49:15
only an efficient space but election
49:17
space and so on - having good technology
49:19
to protect us without the backdoors
49:21
built-in this is what we want to be the
49:25
last question I'll ask because there's
49:28
so much in your career we could talk
49:30
about but you've tried to contribute to
49:33
voting questions as well in the in
49:37
ethical yeah perspective can you just
49:40
end the conversation with a little bit
49:43
so I think I drifted in the direction of
49:46
looking for questions where technology
49:49
comes to play and policy is relevant so
49:51
this encryption debate is one of the
49:52
areas encryption sorry elections are
49:55
another one where technology is coming
49:58
out we can see how to protect elections
49:59
better than we used to know how to be
50:00
able to do things like risk limiting
50:02
audits the use of cryptography turn out
50:04
to be powerful tools for securing
50:06
elections yes and we'd like to see more
50:08
of that so and that involves not only
50:11
technology but also policy because
50:13
people have to understand adopt and
50:15
implement these kinds of things and and
50:20
as you see on the books here at the
50:22
table I guess that you know I get I
50:24
guess I I'm attracted to areas where
50:25
there's interesting policy questions
50:27
climate change being the latest area
50:29
that I've been looking at and it's not
50:31
you are you getting a response I guess
50:34
with the distinction of what you've
50:36
achieved people do tend to listen a bit
50:38
but you have to say no are you
50:41
encouraged by I think it helps there's a
50:44
lot of pushback on the part of the
50:45
fossil fuel industry and so on to saying
50:47
you know this is a hoax or whatever but
50:49
I think that by and large the science is
50:51
winning this debate and
50:53
I'm happy to participate in the sciences
50:56
winning this debate is a great way to
50:58
have thank you very much
51:00
I did it dog with you
51:18
you
