your name where you were born and where
you grew up and went to school
okay my name is nils nelson i was born
in michigan a town called saginaw
michigan
lived in michigan until i was 11 years
old
when my parents moved to california we
i went to high school then in southern
california in glendale
and then on to stanford so i did all of
my work at stanford
both my undergraduate work and my ph.d
work
and what did you study as an
undergraduate as an undergraduate i
majored
in electrical engineering and
was especially on information theory so
information theory was rather new then i
think shannon's book came out in the
early 50s i started
thinking about or maybe late 40s i
started thinking about information
theory
as a first year graduate student
in 1956
and finally did a dissertation not so
much on information theory but
one that involved statistical detection
theory which is related
and that work involved looking at the
theory of games and how it could be used
to design optimum strategies
both for radar operator and someone who
wanted to jam
the radar the optimum strategies
involving
the way to spread the bandwidth around
how to spread bandwidth
so that was my thesis who was your
advisor
willis harmon he's passed away but
willis harmon as a matter of fact did a
lot of interesting work there at
stanford
some of it not related at all to
technology he was one of the very early
people who was intrigued with what the
effects of lsd might be
and he thought it could be used as a
sort of curative agent
for alcoholics and people with various
psychological problems that was before
lsd was outlawed
was he in the psychology department no
he was
willis harmon was an electrical engineer
and
he had learned about information theory
himself in florida i think where he got
his degree
and then he came to stanford and taught
courses in it
and i thought gee this is all pretty
exciting stuff i was never
very good at experimental electrical
engineering
although i had several experimental
laboratories i always had problems
getting the
laboratory results to work out the way
they were supposed to
they're always these hidden ground loops
and things like that so when i ran
across
theoretical things i thought well that's
that's a lot cleaner
and it appealed more to me
so um uh what you
uh so then you did your that was your
masters
no actually i got a funny situation at
stanford i never got a bachelor's degree
they had a special program at stanford
to go straight
for a master's in five years
and the reason for that was that without
having a bachelor's degree
the uh server the i was in the air force
rotc
and you can't really get your commission
and finally get out
until you have a degree so i didn't have
a degree until i got a master's degree
and so then i got a commission but then
the air force gave me a delay
so that i could continue on for a phd
which i did
so i ended up getting a phd in 1958
and that was also an electrical engineer
that was in electrical engineering but
it was on this business
of using game theory to
work on radar reception problems
was cybernetics or the segmentations
influential
yes well i read weiner's book
on cybernetics although i didn't
understand all the mathematics of it at
the time
and i thought control mechanisms were
were interesting
and as a matter of fact it these kinds
of ideas about control and feedback
played a role in some later work i did
um
after i was after i finished being
department chairman of stanford
maybe we'll get to that yeah um so
then you wound up at sri lanka no i
wound up first in the air force
because here i was at stanford in the
rotc
joined the rotc because the korean war
was going on at that time
and if you were in an rotc unit you
wouldn't get drafted
so i had to finally after getting my phd
i had to actually go in the air force
for three years and i was at a place
that was then called the rome air
development center
in rome new york it was part of an air
force base called griffis
air force base i think later it became
rome labs and i'm not so sure
not sure whether it even exists anymore
but here i was
as a lieutenant doing work on
monitoring the various projects that
rome air development center
was supporting with researchers around
the country
so one of the interesting things was
that among the projects i had to
monitor were projects headed by people
whose
papers i read as a student at stanford
and
so here they were big wheels of the
field and i
a green second lieutenant going to see
if their work was up to standard
what were some of those projects well
they all involved radar
work and there was a special technique
that was
being explored in radar signal
processing those days
and it was called pulse compression the
idea was this
that the amount of energy that you can
get back from a radar reflection
depended upon basically the length of
the pulse
that you could send out but the problem
with having a pulse of a certain long
length
was that then it was a little more
difficult to tell exactly where the
airplane was
you'd want a very short pulse for that
more more uh
importantly it would be difficult to
separate two objects
so if the pulses from their echoes
overlapped which they would
happen to do uh more it would be more
likely to overlap if
they were long pulses and if the objects
were close
then you wouldn't be able to distinguish
there were really two objects but if you
had very short pulses
they would tend not to overlap you'd be
able to tell that there were two objects
so the question is how do you get a lot
of energy into a pulse
and also get good resolution
in space of the distance so the thing
about the short pulse that makes it
good for resolution was that a short
pulse has very high bandwidth
so then there were people who said well
we can get high bandwidth into a long
pulse
if we just sweep the frequency the
carrier frequency of the pulse
as it as the pulse throughout the length
of the pulse
and then we can invert that process in
the receiver
and make those pulses short again and so
these things were called matched filters
excuse me and that was most of the work
that i was monitoring at that time
people were interested in very short
pulses because they wanted to be able to
detect different objects
in the re-entrant of ballistic missiles
so for example if an enemy were sending
a ballistic missile in
and it split into several different
components
you'd want to be able to pick up all of
them so you want
a very high bandwidth pulses which you
could get with these long
frequency modulated pulses doesn't any
of that work
end up being helpful for thinking about
robotics
uh i don't think so
i don't think so um and uh so
were there any other interesting
projects in the three years of the air
force
well there was very a lot of interest in
pattern recognition
in terms of being able to tell what kind
of radar
you might be receiving signals from
and that work on pattern recognition
actually used some of the ideas that i
had explored and studied during my
thesis work
on statistical detection theory and
and so one of the things that happened
in pattern recognition and
increasingly so in the later years of
artificial intelligence research
is just how important statistical
methods were
various bayesian techniques and so
that background that i had mainly
applied to radar
in my days when i was in the air force
came back to
help me later when i was doing work in
pattern recognition
so how did you wind up at sro well i was
looking around i knew i wanted to be on
the west coast
and i'd earlier accepted a job before
going to the romero development center
at the rand corporation the rand
corporation thought well we can probably
get you stationed here in santa monica
for your air force tour but in the event
as it happened they they didn't and i
was assigned to rome so
when i came back i talked to the rand
corporation again and i talked to sri
um and also to stanford
sri made me a nice offer and
it was more exciting because although
when i went to sri i thought gee it
would be interesting if we could get the
sri people to do research and radars
i talked to a very charismatic person at
sri
who turned out to be my boss charles
rosen charlie rosen
and he said radar that's kind of like
light bulbs
i mean isn't that a solved problem let
me show you something
very interesting that we're doing here
in the back room we're building a giant
brain
oh really is that right well they were
building a
very early version of a neural network
and they had done
some consulting with a professor from
cornell university frank rosenblatt who
is one of the
founders of some of the neural network
research and so i've met
frank and i ended up going to work for
sri in charlie rose's department doing
work on these neural networks
so was it a direct copy of the
perceptron that went out or was it a
very issue
well it was a variation in the following
way mainly in details
most of it in terms of its
of its design schematic design
was a direct copy but the details were
different
i think frank rosenblatt in his very
early perceptron
to make the weights adjustable these
weights are important
connection units uh in the neural
in the units that simulated so-called
simulated neuron
those weights were adjustable by having
potentiometers motor driven so he could
send a current
to the motors that would then change the
potentiometer setting and thereby adjust
the weights
we had an sri somewhat different
technique
developed by a physicist named ted brain
and that was kind of interesting also
brain
that involved magnetic multi-aperture
magnetic cores
so the amount of weight was stored as
the amount of flux
in this magnetic core and it could be
adjusted and it could be read out
and were you at all aware of ashby's
work on homies yes
knew about ashby you knew about
homeostats knew about some other people
in britain
maybe we just let that go well maybe i
ought to get it
fine because it might be a step son
hello
where were we we were oh we had these
magnetic aperture
and you were talking about some of the
british oh there are other british guys
there was um oh there was an italian guy
named gamba
g-a-m-b-a who had some kind of
perceptron-like device
um maybe some other british names have
come up
well there was um gray walters tortoise
gray walters tortoises however at that
time in the earliest days
of my work at sri we weren't too
interested in robots we were
building a pattern recognizing device
something that well the army signal core
was supporting the work
now i think it's called the us army
electronics command
something like that the signal core was
supporting it
and the rationale we didn't particularly
care what the rationale was we just
wanted to do pattern recognition work
the rationale was to recognize symbols
that were maybe hand printed on army
maps
we actually ended up doing quite a bit
of work on hand printed fortran coding
sheets
so we wanted to recognize in those days
computer programs were written out on
coding sheets
and then key punch operators read the
coding sheets and punched uh what they
saw in the
coding sheets how to punch cards and um
so we wanted to to eliminate that step
by having that done automatically so we
actually got pretty good results
and one of the reasons we were able to
get good results was that
not only did we try to recognize each
character
but before we would accept a line of
fortran code
as recognized it would have to be
something that could be compiled
so we had access to the fortran compiler
and we
had to have a legal fortran statement
before we'd claim was recognized
and to get a legal fortran statement we
might have to go back and
accept maybe the second best uh guess
on an individual character we could we
could
uh we had a ranking uh when we
recognized each character
as well it might be 95 an a or might be
an 80 percent of b and so on so
so forth and so we would go down the
line until we could
get the most probable going across the
whole
sequence of characters uh recognition
of the whole line did you come across
the work of the
hansfun forester on the biological
computer lab
yeah now he was at the university of
illinois as a matter of fact i think
when
we came across his work uh he might have
been retired at that time and living
on the west coast in half moon bay yeah
yeah
there was this whole field of
cybernetics that had had sprung up
after weiner and a lot of it was kind of
um you might say
uh there are all kinds of strange ideas
self-organizing systems there are
conferences
on self-organizing systems and on
cybernetics
some of it was extremely speculative and
not
terribly well grounded but that didn't
bother us too much because what we were
doing is pretty speculative also
a lot of people didn't believe in neural
networks at the time a lot of the early
self-organizing systems conferences were
really
talking about pattern recognition
did you go to any of those i went to
some of those conferences as did charlie
rosen
as a matter of fact at one of them i
think it was in las vegas
i met warren mccullough and
seymour pappert and
um mccullough uh
had all kinds of interesting ideas about
perceptrons and how they ought to be
built
a lot of those conferences frank
rosenblatt attended
and marvin minsky they used to have
fierce arguments
because i think minsky thought that
perceptrons
probably were quite suspect not very
scientific and well
he published that book at the time as
well
there's a lot of misunderstanding about
that book i think
just in case
some people think that the result of
that book was
a diminution in the uh amount of
research on perceptrons and uh
it was even felt that frank rosenblatt
quit his work on perceptrons after
minsky
and papper published that book i don't
think that was true
uh actually rosenblatt got very
interested in something quite different
from perceptrons he was very uh he got
interested
in uh transfer of memory
in flatworms and so he would train
flatworms to go to a maze where they
would turn left or turn right
and i guess they could be trained to do
that and they would more often go left
and right if that's how they were
trained
then he'd grind them up and extract some
polypeptides
that he thought were some kind of marker
of memory
feed them to naive worms and then
checked to see whether these naive worms
without any training
would go off go left more often to write
well he had
a slight tendency but most people
thought it wasn't statistically
significant
and so he was doing that work unaware
that minsky was writing this perceptron
book
what was roosevelt like to work with he
was a very smart guy
and uh very intuitive he had a lot of
he wasn't terribly although he had a lot
of mathematics in his book called
principles of neurodynamics
a lot of the mathematics was um
i don't know maybe not quite as as
rigorous as it might have been uh
very early had this scheme for how to
train a perceptron
and it was thought and he thought he had
proved
a um that if
you could train a perceptron directly to
distinguish dichotomous say
uh between some characters that this
training procedure of his which you call
the error correction procedure
would uh lead to a correct
dichotomization
uh his proof was wasn't too good and a
colleague of his h david block
was also a very smart guy at cornell
ended up with the proof as did some
colleagues of mine at sri
on on that matter of fact um
it isn't difficult to to know that there
is a procedure which would lead to a
correct
set of weights that would lead to to
correct classification if it could be
done
one could just try all possible values
of weights you know until you
got one this was a little more direct
this particular error correction
procedure
okay what was uh charlie
rosen like to work with
charlie rosen was extremely intuitive
and
quite bright he was willing to try all
kinds of things i remember charlie rosen
looking at various ways for controlling
or for adapting multiple layers of
weights the perceptrons at the time
only adjusted the weights in one of the
so-called layers
between neural elements and we thought
that as did a lot of other people
that if you had perceptrons with several
layers
that they would be more powerful and but
the
key problem was how did you how do you
adjust all the layers
and so charlie had various schemes he
was cooking up as did we all
and he would sit with quadrille paper uh
hand simulating different kinds of
pattern recognition problems
seeing if his schemes for adjusting
multiple layers of weights
might work actually later
um david romelhart and others
probably working on some work that
someone named paul werble said done
earlier
had come up with a some essentially a
steepest descent method
for changing weights and we didn't do it
because
we didn't think you could do steep
steepest descent
backward through a threshold element
and their trick was well you don't need
necessarily have to have a threshold
element just have some differentiable
uh function that looks sort of threshold
like the so-called
sigmoid or s-curve and you could back
differentiate through that and that's
what
rumble heart and colleagues did and they
came up with the
methods although they were quite slow
for being able to adjust the weights in
several layers
which did in fact lead to more powerful
machines actually
uh if the first layer doesn't properly
separate or partition the
the inputs of different categories
no work by future layers is going to be
able to partition them the future layers
all they can do
is then properly aggregate different
parts of the
partition space so they'll go into the
same category
so but the first layer has to do its job
okay
okay um what when you got to sri
where was it a large group working in
this area or was it well probably when i
got there there were probably eight or
nine
people uh in a little group called the
learning machines group
uh the laboratory that charlie rosen ran
the laboratory itself was called the
applied physics laboratory
and the other group in the laboratory
was working on
something quite different but something
that actually motivated charlie to get
interested in neural networks
the other thing was the other part of
the laboratory was headed by someone
named ken shoulders
kids shoulders had this idea that if you
could shrink a triode
down to microscopic size
the field effect would
diminish would eliminate the need for a
uh
a heater on the cathode so you could
just get such intense fields
with reasonable voltages across very
very short distances
and then electrons would just boil off
the cathode on their own
and you could stick a grid in there so
he was interested in
micro machining techniques for making
thousands and millions of triodes
uh charlie thought well it's going to be
difficult to wire all these things up
maybe we can develop some sort of
self-organizing idea he had gone to some
of these self-organizing conferences
that would allow us to uh get them to do
something useful
so that's when he started talking to
frank rosenblatt before i came to sri
and got interested in neural networks
originally
his idea was that they would be the that
would be the technique that would wire
up these triodes
of course the transistor came along
making it uh
making tiny triodes uh not the best way
to go
now we can do billions of transistors
and i
you don't have any problem wiring them
up you just use a photo mask and
lay down the wires wherever you think
they ought to go to do the logic
circuits and so on
so who are some of the other researchers
that were working
in your lab at the time there was a man
named
george forson he was primarily the
engineer involved in building
minos there was
as i mentioned uh ted brain
who was originally from england and
it was ted who told us about utley and
some of the others who had done work in
england
soon after i came we hired a couple of
very smart
additional people one was named peter
hart peter hart
uh eventually became my boss at sri
because i wasn't too interested in
management
and he went on to found a laboratory at
schlumberger
a company and also
more recently was chief
of a laboratory that rico started
in menlo park rinco the camera company
and we also hired a man named richard
duda dick duda
and uh dick dude was a
very bright mathematically inclined
person
who uh made sure the mathematics of all
the things that we were doing
that involved work even in the neural
network days
through some expert system work that we
did
was well-founded and um
and he's published a lot of papers well
their textbook and pattern recognition
who done heart yeah now uh
then the guy that peter hart hired at
rico david
stewart wrote the second edition
due to starting duda heart and stork
although stork did the writing
um so at what point uh did the idea of
making a a robot
or a show are we still in particular
yeah
uh when did that start to take shape and
how did that take shape
well charlie rosen had this idea well
let's see first of all charlie attended
a class
on programming in lisp
we knew through going to conferences
and being close to stanford that there
was this
subject called artificial intelligence
that we vaguely thought
neural networks probably were part of
and
pattern regulation was part of but
what's it all about
well charlie thought we ought to find
out so he went to a
class given by a
recent graduate from mit phd student
from mit
student of marvin minsky's name bert
raffell
verberfeld taught at course on lisp
charlie came back
all excited thinking we've got to do
something about ai we've got to write
programs and there's all this stuff
going on and we should be part of it
and later by the way we ended up hiring
verb raphael
he had a job for a while at uc berkeley
but then joined our group
and so charlie thought well
we have this pattern recognition work
that could account for
the robot's perception especially vision
and we through lisp programming and
through
various types of heuristic programming
ideas we could probably build control
programs for a robot
which charlie called an automaton
and he said we need to build this
automaton
and it would roll around and it would
recognize its environment and we could
get it to do things
and he wrote a memo and i've been able
to find a copy of that memo but
the original memo got all of us excited
and was used as kind of a fundraising
device to try to get
sponsors interested in this project that
was probably
in around 63 or 64. and
finally a lot of this is detailed in
some detail in my book on the history of
ai
we did get a project supported by the
advanced research projects agency called
arpa at the time
now it's been renamed darpa
for several hundred thousand dollars
over some years
to build this device that would roll
around
we had engineers who were reasonably
good but maybe not as good as they could
have been
and actually building the motorized
hardware in the vehicle
and because they might not have been as
good as they could have been when it
started or stopped it shook and so
we ended up calling it shaky we had a
session to talk about how we were going
to name this machine
and somebody suggested shaky then a very
interesting person uh whose name i might
reveal
said absolutely not this is too
important a project to give it such a
frivolous name
so of course we did give it that name
that was a guy named steven coles you
might want to talk to him also
he's got a movie out which i think i
have a copy of online
the robot history
little clips from jpl and a lot of other
places
stephen coles stephen calls later while
stephen coles participated in the
project he had come from
a student of herb simons at carnegie
mellon
and he joined us at sri he was
interested in natural language
processing
and so his idea was we want to be able
to talk to shaky
and actually not
through speech but by typing at it and
he wrote a little grammar and a
phrase recognizer and so on that would
be able to recognize certain simple
commands that we gave it
so he did work on that then later he
decided he wanted to go to medical
school
and he ended up going to the sorbonne
got a degree french medical degree
and i think most recently he's at an
institute of gerontology at ucla
and he's available i mean i have his
email address
so what were some of the big challenges
so let's see there's other people too
involved so anyway we built this thing
and we moved it moved around
and we made a film of it that's
online i think they had sections of it
at the computer history museum
and uh it was the first
controlled robot device although there
had it was preceded by
a similar device at johns hopkins
university but i don't think it was
quite as sophisticated
and they called the beast it was more of
an analog system
and of course all of grey walters things
um so
what it could do it could make a map
of its world it could use a map
it could up well it didn't really make
the map entirely it could use a map and
it could update its own position in that
map
and it could locate objects in the map
if there was a new object it could
create it and put it
at the right place in the world and so
we wanted it to be able to solve certain
kinds of problems
we had two
innovative ideas at the time that we
wanted
it to be able to to capitalize on
one was navigation so how do you get
from a to b
and so there were various techniques
that we
explored some of which involved
most of which all of which involve graph
searching so if you could
represent the points possible points in
the space that you're going
through as points in a graph and the
legs and how you would get from one
point to another as
branches of that graph you could search
the graph for the shortest path and
there are various
techniques for graph searching that were
around at the time
an edge yard dijkstra for example had
one of them
and so we came up with this technique of
uh well there was another craft
searching technique uh worked on by a
team at the university of edinburgh
under donald mickey
called the graph traverser and they use
heuristic estimates essentially for
deciding which
place you ought to look for first as a
as a way as
a possible path and we modified that a
bit
by saying well the you could score
the different places that you ought to
look at next
by what we call a a
value function and the value function
should consist of two parts we thought
one part
was how long was the path
from the start to the place you were
considering
that you had gone so far the next part
was
how far the goal was what's your guess
of how far the goal was from there and
well you don't know how far the goal was
until you actually complete the path
but you can make an estimate based on
what we thought the airline distance
would be
so the euclidean distance of the path
and
so we used that as the heuristic
function we called it
and used that in an algorithm that we
called a star
star was a little asterisk after the a
in electrical engineering very often
optimal
methods had a little that little
asterisk after them
optimal estimates so we said okay we'll
call it a star
and we were able to prove that if the
heuristic estimate was
not any greater than what the actual
path length would be
that you would soon discover then you
could prove that that particular method
would in fact find the shortest path as
soon as it found the goal
and it turned out that the goal was the
next place that you should
look at that would be the shortest path
and you could generalize it to at least
costly paths doesn't have to be distance
any any kind of cost function could be
put on it
it turned out that that particular
method is currently used in all kinds of
applications
and is the basis for some of the map
searching applications that google and
mapquest and some of the others use
unfortunately you couldn't patent things
in those days
and so we didn't we don't get a tenth of
a cent for every time anybody uses it
i use it to get here probably yeah um
did it work
so that was one thing the next thing was
well we were interested not only in
navigating through a space
a geometric space the laboratory that we
had
but we were also interested in
navigating through sort of a conceptual
space
if you're trying to achieve a certain
goal which could be stated in a special
language we used uh logic
um what's the and and you had certain
uh actions you would perform
that were advertised to achieve
certain subsidiary goals how could you
string these actions together
into a path which would take you from
your present position
to achieving the goal not necessarily
the goal of being at a certain place but
a goal of having a certain
combination of things satisfied and that
led to a
program that we call strips which was an
acronym for
stanford uh
[Music]
stanford research institute problem
solver
and so it was used
as the problem is the main
problem-solving device so
one of the key developments i think out
of it besides
the a-star and the strips algorithm we
had a lot of other algorithms for vision
processing scenes doing line finding
although other people working on these
problems also at stanford
and at mit but one of the things besides
strips and a-star and the vision things
was
putting together the architecture in
what we call a three-level
layer architecture which is currently
used in a lot of robotic systems today
at the bottom level were some of the
very basic motor functions
which really sort of programmed into the
robot you didn't have a lot of
control over how they were going to be
modified
uh they were the basic things the robot
could do move forward
move left tilt the camera pan the camera
and um then we had in addition to that
at one level higher
a level that we call the intermediate
level actions
the intermediate level actions were ways
of stringing
those basic motor functions together
and we used a special technique called
markov algorithms not too unlike what
rod brooks and others later developed in
their so-called subsumption architecture
for putting those things together they
were quite robust
in the sense that and involved this
whole idea of feedback that
i was struck with from knowing about
cybernetics
that a particular intermediate level
action didn't finish
until it achieved its goal even if it
had to
go backwards and start over and do some
things over
so they were very robust in the sense
that they were stubborn about getting to
the goal
and if there was some slip up well
okay uh but now now where are we
and what's the best thing to do from
here and uh
so then the highest level is where
strips operated
the highest level would then string
together these intermediate level
actions assuming that they were robust
and that they actually would work
which they often did string them
together into a plan to achieve a goal
like
in that case our our goals were mainly
things like pushing boxes
from one place to another going through
getting from one room to another going
through doorways unblocking doorways
if you couldn't get through things of
that sort so we made a movie of all of
that sort of thing happening
how long did it take you to uh to film
all those different things
days which we then were able to
um sequence into a reasonable movie
but um the
movie itself what we did is we ran the
program and got all the results but we
ran each section as kind of a sequence
and then we would start it up again
because we'd have to move the camera
position things of that sort
did you have to reprogram the robot each
time as well
well i think probably in the early days
of getting ready to make the movie
uh we were always introducing new ideas
into the program maybe making strips
more efficient
um but we didn't have to do any
reprogramming
actually during the the movie making it
was all what we had
and frozen at that time and were there
any outtakes that
did they always do yeah they're probably
uh
probably aware of um i don't recall
at the moment what they might be but uh
there might have been glitches maybe
computer crashes or things of that sort
and it has these sort of wires around
the base so those are
collision detectors well the shaky
system had several sensors it had the
television camera
it had a triangulating rangefinder
using a laser not a time-of-flight laser
but a triangulation system
and it had these so-called cat whiskers
uh that would trigger switches if it
bumped into something
it also had a bump bar for pushing
things
and when it was engaged that is to say
you were really up against the block
and the bump bar would get engaged and
you would know that you're actually in
contact
okay um and after that initial
sort of set of experiments did you
continue development on shaky
well that was probably the movie i think
was taken in about 1971
or two and that was
probably pretty much the end of the
shaky project
we had a lot of ideas about how we
wanted to expand
the work things which shaky couldn't do
some for example shaky didn't know
anything about
what other actors might be around
and couldn't coordinate with other
actors
as far as shaky was concerned there was
no dynamics in the world
the world stood still the only time
anything changed is when shaky changed
it
so there are things of that sort we
wanted to do and we went to
we wrote proposals who went to arpa
and up at that time well of course arpa
had
a succession of different managers and
the manager at that time decided that uh
arpa wasn't interested in any more
robotics work
and so for a while we tried to do the
work we wanted to do
under a different label we developed
what we call the computer-based
consultant
which was going to be a system that
would advise
a person an apprentice how to accomplish
various tasks
so in a way the apprentice was a robot
we still had a problem-solving system
above all that
which would sense the environment for
the apprentice
and make plans for holler apprentice one
of the tasks we considered for example
was
taking apart and reassembling an air
compressor
and so we would start out with the air
compressor assembled
and we would tell the apprentice each
step about what to do
and then how to put it back together
again and so the planning system would
then figure out all these steps
uh but then arfa finally decided no i
didn't want to do that either
so we had to leak off into other things
why do you think arpa
lost interest in robotics
well a lot of times i think it's still
the case with arpa there are certain
fashionable topics
that either through the through their
perception of what congress wants
their perception about what the military
demands are motivate opera to work in
certain
subject areas i think around that time
arpa got very interested in what they
call a command and control
so um given a situation on
say a battlefield where you're getting
all kinds of information from different
sorts of sources how could a commander
put all of that together
and make a decision as to what the
current status
is and what should be done
and so there were various tools for
command and control
that the tools that were thought to be
helpful
to a commander in command and control
namely tools of
one of them that sri got involved in is
is getting better access to databases
and more convenient access to databases
could you do it through a natural
language system could you ask questions
of the database
and get answers
more than that the information that you
might want
might actually reside in several
databases so could you have a system
that would
decide how to parse out the particular
question
getting this part of the answer from
database a another part of the answer
from database b
then going out to the databases getting
those answers then putting them back all
together
to give an answer to the commander
saying
so uh sri developed a rather large
project
doing work in that area what was that
project
well there was a project called team
transportable
english access
to something i think i have all those
acronyms in my ai history book
which you can look out and there's a
good description
of that work involving natural language
access to databases that i talk about in
the book
so during that time that you were
working on shaky was there much
interaction with
john mccarthy or any of the people that
they landed at stanford and all my
favorite
yes i think there was quite a bit as a
matter of fact in um
even yeah in 1968 i
spent kind of a half a year on loan from
sri to
stanford and i had an office out at the
artificial intelligence laboratory that
stanford was running
on a rash darrell road in the foothills
behind stanford
and worked with and uh there was other
people working on
mainly on machine vision and other
robotics projects
uh one particular person i remember
quite well the man named robert bowles
who was working on uh automatically
assembling a
um water pump for an automobile
and and they were doing various puzzles
could they um solve the instant insanity
problem as they called it by putting
blocks together
and so yeah there was there was quite a
bit of interaction
and he wound up eventually he did he's
there now
i think
um they're still working on vision
and jerry feldman was doing work on
robotics and vision in those days he
later became the founding
chair i think of the computer science
department at rochester
now he's at berkeley in an institute
called the international computer
science institute
and um i figured when it went out
reginae
she got her phd at stanford and
let's see where did she go did she go
immediately to penn after that
i know she spent a little time as an nsf
national science foundation director
and but she did work at the uh ai lab
and did you write under there oh yeah
and what would you say was the
kind of intellectual discourse that was
going on
the exchange of ideas well i think it
was very exciting
uh that whole period of the stanford ai
lab when they were in the foothills
was a very exciting time and they
developed a lot of things
probably the first printer that led to
the
xerox printer a guy named les ernest was
john mccarthy's chief executive officer
you might call him
and he was uh interested in a lot of
different things
the they had a lot of extra computer
cycles
out there and so they allowed
people interested in computer music to
use it john chowning
was out there at the time john johnny by
the way was the person who invented
the frequency modulation technique for a
sound synthesizer
that led to a lot of money for stanford
paid for by yamaha who included it in
their
keyboard stuff and then eventually they
uh
they got a site on the campus uh called
the
center for the study of computers and
music or something like that
uh yeah there were a lot of things going
on not all of which were
necessarily can connect to ai after all
you had this very powerful computer
time shared computer pdp six and
eventually pdb10s
and people understood you could do all
kinds of things with them
you could do text processing you could
do all kinds of
pattern recognition things ed feigenbaum
was there for a while
working on the project which became
dendral
a program for deciding
about the structure of small organic
compounds
ultimately he left the ai lab
because i guess he couldn't get enough
cycles on the machine and move to the
campus
he was upset because uh mccarthy and his
people were always changing the machine
around and
taking it down and making changes to it
um and uh did you ever work on another
robotics project after the shaky or did
you stay
in my life yes yeah well in a way maybe
we'll get to that
uh after uh
if you want to go sequentially just
briefly
i was a little bit miffed that arpa
wouldn't support robotics and i decided
i wasn't going to have anything to do
with what
arpha thought they are that i ought to
get involved in i mean i knew what i
wanted to do
and so i left office support and
together with peter hart and dick duda
worked on the so-called prospector
expert system
we were motivated by work by
ted shortlife a phd student at stanford
who had developed an expert system
called mycin
which is sort of the suffix of some
medical
drugs like oromyosin and things like
that
streptomycin and
so anyway he had some ideas for putting
the knowledge of experts together
in a way which was usable and could come
up with diagnoses
and so peter got the idea or maybe it
was peter and dick i'm not sure who
actually thought of at first
that perhaps you could use the expertise
of geologists
explorational geologists to help decide
about where mineral deposits might be
and so they talked to a lot of people at
stanford uh in the geology department
and students and faculty and put
together
an expert system that they call
prospector which underwent
some development and it
ended up accurately predicting a mineral
deposit
in the state of washington in mount
tallman a molybdenum deposit
and actually it wasn't at the time
couldn't be called an ore deposit
because in economic geology
an ore is defined to be something that's
economically
uh profitable
and at the time i think the price of
molybdenum molybdenum
was not high enough but it was a it was
a mineral deposit
and it was reported in science magazine
so anyway that and then so i was doing
work on that then pretty soon peter hart
decided to leave and start a company
and i needed a new person to be in
charge of the
ai laboratory i had up to that time
tried to resist management types of
things but i was the obvious candidate i
guess so i did it
that was i think in 1980 and so i was in
charge
of the ai lab which i think we call an
ai center
until i went to stanford as chairman of
the department in 85.
so at that time during those years i
wasn't doing a lot of my own research
because i was doing mainly management
things
okay um and how how did they
corral you into being the department
well if i didn't do it who was going to
i mean so it's a matter of uh
okay so you got to take your turn
that was that sri and how'd they grow me
how'd they crawl me into that
well you know i had taught courses at
stanford in the past and i had
students who had worked with me
at sri while they were doing their
dissertations at stanford
and so i had a pretty good connection
with stanford and i thought well
maybe i'd like to actually go over there
and be a faculty member
but they weren't at that time hiring any
new faculty members but they were
looking for a chair
and so i thought well that's one way to
get over there so i took it on
i promised i'd do it for six years and
did
okay i want to get back to your students
but first i just want to ask
so when the you stopped getting the arpa
funding what other places did you go to
look for from
we had funding from the office of naval
research i was writing some books at the
time
uh throughout the 70s and and even into
the 80s
and the office of naval research
supported some of that we had projects
from the air force
office of scientific research the work
on the prospector expert system was
largely supported by
the u.s geological survey
we had some support from nasa
so we cobbled together you know projects
here and there to
uh support the kinds of things we really
wanted to do
and um so who are some of the phd
students who supervised and many of them
working on shaky
well let's see yep uh there were quite a
few we had uh
tom garvey who's now at stanford
uh now at sri still i think
we had a young man named earl sassodi
uh we had
not on shaky but later kurt college
who was at sri for a long time got his
phd at stanford
worked with me on a system for reasoning
about knowledge and belief
and now he's actually at a place called
willow garage
or are you seeing willow garage yeah we
were
probably saw kurt we didn't have to care
actually i hiked across the sierras with
kurt
on skis kurt and a bunch of
other young guys i was probably in my
late 40s at the time but we did a trent
sierra trip
in the winter camped out in the snow for
five nights and
went from east to west over the sierra
crest
wow let's see
we had douglas appelt a p-p-e-l-t
one of the students um
we had a guy who did a lot of the lisp
programming for
shaky the robot named robert yates
bobby yates y-a-t-e-s
we had cordell green probably one of the
more
instrumental of the people involved in
robotics while i was at stanford i mean
while i was at sri
cordell had read some papers by a guy
named john
allen robinson on theorem proving
and so early in the shaky days we
thought that the
main reasoning mechanism for shaky
uh should be a system that could
manipulate
expressions in symbolic logic and
there had been other techniques for
doing so but robinson came up with a
technique that was particularly suitable
for computers and cordell
cream read the papers knew about it and
decided he wanted to program up
a system which he did i remember him
telling me nils you've got to learn
about resolution
i said resolution what's that and so
anyway i
read various papers and um
got involved in that which led some
people to claim that i was overly
interested in
logic as the main approach for dealing
with
artificial intelligence and i think of
it as as one
aspect of intelligence we humans are
sometimes logical
we put together logical arguments and
logic plays a role
in many of the things we do it's not the
main role
maybe but so anyway we put together a
system for
reasoning and symbolic logic that
used john allen robinson's resolution
principle as a matter of fact it was a
component of our strip's
problem-solving system also
what did greenland green ended up
he was also in the rotc he was in the
army for a while but i think
uh as a matter of fact the um
he served mainly uh his tour of duty at
inarpa and he was assist an assistant to
larry roberts
who was the uh head of an arpa
uh office of information processing
techniques office
and it was cordell green who was
assigned by larry roberts
to look into whether or not it would be
a good idea for opera to support
research and speech understanding turned
out
it was a good idea and they did
and that's all mentioned in my book also
so then green started a company uh
that is still in existence as far as i
know um
it's the name of the company it may come
to me
uh there's a company in palo alto that
did work on software engineering
we had lots of students come from
stanford
to sri and i learned a lot from the
students
because you know my background i didn't
know what a compiler was
i mean i didn't have any background in
computer science and so these students
would read stuff and tell me i'd
have to go learn it i did
um did you have much interaction with
the people who are working in
manipulation and grasping
like rick shineman well i knew about vic
shineman i knew about his arm that he
developed and
designed at stanford um
but i didn't personally have any
interaction with him i wasn't so
interested in arms at the time shake he
didn't have arms
although we designed a system that was
we had proposed to arba
that would have arms but they ended up
not funding it
when did you make that proposal
oh probably in the early 70s
and did you have much interaction with
bernie roth
um i knew who he was
but no um
professional interaction i mean if we
didn't work together on anything
okay um
and then so at what point did your did
your work ever come back to
well so i go over to stanford and
uh first thing is well i had to learn
about how you
become a chairman uh the atmosphere is a
good deal different than it was at
sri
in a number of ways first of all
the people in the department don't
actually work for you
i think their idea is look you're in
charge of
we're passengers on your ship uh we'll
tell you where you want to go your job
is to keep the engine running and
do all the things that are you need to
do and so that was a bit of a change but
one that i didn't mind
uh we had a lot of smart people at
stanford
there were occasional things we had to
solve problems
you know as any manager does uh
sometimes
there are ex you know things that happen
on the tail of the distribution that you
got to go deal with
the other thing is that whereas at sri
you could work at night if you wanted
but largely you'd get there in the
morning you might leave at six o'clock
at night and that was
a working day but at stanford really a
worker's never done
people are emailing you they've got
problems some of them in the middle of
the night
and they need things done plus the fact
that there's
this level above a department chair the
deanery
and deans have various demands i mean
they want a vision statement every now
and then you know where is the
department going
uh we have to have tenure
uh cases uh someone sells up for tenure
you have to
make sure we get all kinds of letters
written and you have to present the case
for this particular person's tenure
in front of a committee we need your
budget
and oh by the way we're going to have a
special committee in the university to
look into
how we can better use computer science
in civil engineering you know would you
be on that committee and
so there's all that kind of thing that
happens more at a university i think
than a place like sri
so it was a lot of work the first thing
i discovered there was
quite a bit of work but it was all very
exciting and challenging so
i liked it i did get involved right away
with
a bunch of students and i had four or
five of them phd students
one of the difficulties was that they
all came in at the same time
so in our little group and we had weekly
meetings
they didn't have anybody any kind of
mentors people who had gone on before
them
people they might work with so they're
all starting from scratch
and that made it kind of difficult
probably for them and for me
but they had had some very good students
one of whom by the way i did end up in
sri
lanka some of your phd students in one
particular yeah one went to one of them
named karen myers
went to sri and played a leading role in
a project that sri
called kalo which stands for cognitive
assistant that learns and organizes it
was a multi-million dollar project
and it actually led to a startup a
company called siri s-i-r-i siri
which has an application free app i
think you can get on your
iphone that will uh give you advice
about
where to go to dinner and make
reservations for you
um so i mean you've done some other work
on this kind of agents
that might aren't necessarily robotic
but did you do any other specifically
robotic work after
well let's see in terms of robots um i
joined when i
uh after finishing being chair
i took a sabbatical and
went to had half a year at mit
and harvard some combination and
as in rod brook's lab and i would teach
a class or two at harvard once in a
while
you know not a class but i'd lecture
for someone who was teaching a class
and uh half a year at the santa fe
institute in
santa fe new mexico and during that time
one reason i wanted to go to rod
brooks's lab was i began
to be interested again in this
intermediate these intermediate level
style programs which are very robust
and i was developing a formalism
out of a style of programming that i
called telio reactive programs
and these helio reactive programs i call
them teleo
reactive because first of all the helio
part was they had a purpose
and the reactive part was how they work
toward that purpose dependent upon
their particular reaction to the current
situation
so however they decided to achieve the
goal depended upon where they were at
the moment
and if where they were at the moment
suffered a setback because as on their
way to the goal something
untoward happened well they would react
to that and
carry on so i had some students at that
time who were developing
in software simulated robots which we
call bots
and they weren't never did yet well i
did have one student who actually
programmed a little
nomad it's called a nomad robot uh using
teleo this helio reactive formalism
uh tom willocki yeah i think his name
was tom willocki
and another student
who um named scott benson
who used this particular formalism to
program
devices simulated devices that would
learn
the effects of their actions so he was
able to develop a system that could
learn to fly
a simulated model airplane oh a
simulated airplane they have these
flight simulating
flight simulating programs and so
his program could learn by watching an
instructor
how to take off and how to land
and it would learn that by noticing
all the conditions that preceded a
particular
instructor's action all the readings and
all the dials and everything
and the conditions afterwards and
then um and
and then it would deduce her abduce
that uh that particular action that the
instructor
performed would in fact take the
situation from the before to the after
so i developed a whole set of these
operators which were very much like the
strips operators that i talked about
earlier
the strips rules so he was able to do
that we never made a movie of it
but he wrote a dissertation and uh i
think it's had some impact
there's something out there oh well we
can close that window again
come on we don't tell grace yeah okay
um and when you was uh
sabbatical 19
night
you're set you know all your wires yeah
uh that was in 1990.
uh i went off in the
[Music]
in the fall uh summer of 1990 and ended
up
in the summer of 1991.
and so then i had another student who
programmed little tiny robots that
would wander around on a screen these
are simulated robots
using teleor active programs that would
accomplish various tasks
another student who i'm in still in
touch with quite a bit
was able to program some simulated
soccer players
using uh reactive
programs so a soccer player
uh although might make a plan for doing
something the plans
uh aren't don't always develop the way
you think they're going to because there
are other players other things happen
and so but you always are confronted
with a certain situation
and there's always some best thing to do
in that situation and so these
particular teleo reactor programs would
try to do that
do you know if that strategy has ever
been used for the robocup competition
now that i know of however you know i
think that the idea of these kinds of
uh reactive agents i mean i certainly
wasn't the only one pursuing them as i
mentioned rod brooks
was doing similar things there was a man
at uh
jpl aaron gatt gat
who had some programming ideas that were
quite similar
there was a man named furby f-i-r-b-y at
university of illinois i think
was he and i think he had something
called reactive action packages
um would you say there's more similarity
or more difference between
say you're the subsumption architecture
of brooks or some of these other
approaches to your own
right well you know the subsumption
architecture i think was
quite a moving target i wasn't too sure
exactly what it was it was anything rod
brooks was thinking up at the time
i think the main thing that motivated
rod rooks was that
you could use the real world itself
instead of a model
and one didn't have to make models and
work
plans out in models that let the world
store itself
and so most of his ideas on subsumption
capitalized on that notion to a certain
extent we did also with the uh
teleo-reactive programs although
you could also make plans stringing
together these helio reactor programs
and
and then um proceed from there so i
think
my idea was that yes the world can store
itself
but you do people do use models
you know you have to remember where you
parked your car
if you're going to let the world store
itself yeah you'll eventually find it
but you've got to look at a lot of
places so you're somebody who's used
both statistical and logical methods
models and representations as well as
reactive systems
but these have often been very
contentious debates within the ai
community how have you
seen that well i think it's kind of
working itself out
now i think that if one knows
something for sure i mean these
statistical techniques uh well one way
in which they're used
is in the so-called belief networks in
which
more or less you have propositions
and propositions sometimes can be the
cause of other propositions
so and and each one might not be known
with certainty
you have various probabilities and there
are techniques for propagating
probabilities throughout the network so
if you learn something new
that affects the probabilities not only
of the new thing you've learned but
all kinds of other things that might be
related
however it is the case that sometimes
you absolutely know
pretty much the certainty or at least if
it's 95 certainty you may as well take
it a certain
and then in that case probably ordinary
logical reasoning might be
more appropriate say than trying to do
things with probabilities
after all when you have the
probabilities
if you're going to do everything exactly
with probabilities there's a good deal
of computational burden then not having
certainty ads
but if you do have certainty you ought
to be able to capitalize on that extra
knowledge
so i think that there's a role you know
for for both techniques
and looking back what would you say were
the most
challenging problems in pattern
recognition and
what were some of the biggest
breakthroughs that you had
well in pattern recognition
one of the problems that was recognized
from their very earliest days was
separating the figure from ground
so if you're looking at something well
even today in face recognition
you have to be able to isolate what the
face is first and where it is
before you can decide to apply the
various techniques that you might
be able to apply to decide whose face it
is so picking out a face from a crowd i
think that's largely been pretty well
solved
i mean uh everybody's personal computer
if it has picasa
uh has a way to recognize various faces
and isolate them cameras have
face isolating mechanisms in them but i
think it was thought to be a big problem
in the past
otherwise in pattern recognition i think
it's still the case that
we're not very good at dealing with
cursive script
connected speech well
there are various statistical techniques
that do a pretty good job with it now
you can buy commercial products
but being able to deal with with
connected speech and connected writing
uh was a
an intermediate term problem that has
largely been solved
um i you talk to people nowadays
who are prone to use the various
statistical methods
and i think they say most of these
problems that we thought were really
difficult
can be solved just by having lots and
lots of data
that you the more data you have the
easier it is to
dissolve it in so many of the natural
language understanding systems
and the for example the um
translation programs basically use just
lots and lots of examples
to help them the um
and what do you see as the big problems
uh in the near future
for for ai for ai in general
well dealing with common sense i think
is still the big problem
uh the expert systems are pretty good in
their special domains
um you might say well let's take a look
at one of the
milestone achievements i mean then
several milestone achievements in ai
chess playing was one the autonomous
cars another
more recently i think the jeopardy
watson the watson program of ibm that
competed on jeopardy
is pretty much a milestone achievement
however i think even its
designers will admit that when it came
to dealing with the common sense
there were difficulties it could get
around that for certain classes of
questions that it was asked
the more circumscribed the question and
the more likely it was that that
question was
explicitly in its huge database
the better off it would be however
it made several mistakes and even though
one
and i think that a lot of the mistakes
would have been solved if it had
a better more encyclopedic common sense
knowledge
there have been various attempts to try
to solve that problem
doug lenott at psyc is one
who thought that well the way to deal
with it is to hand code
all the different elements of common
sense you might ever need you know the
hundreds of millions of little bits of
common sense information uh well
good luck uh that's a never-ending task
because
common sense changes and uh
culture changes and so you're gonna have
to have something that
uh can keep track of it and how to keep
track of it well the internet's a good
source there's just an awful lot of
stuff on the internet it's all in
english
so you have a sort of a chicken and egg
problem to recognize some of the english
you have to have common sense but in
order to
have common sense you have to be able to
recognize all the things that are there
but i think gradually we're making
progress on that
and what about more specifically in the
area of planning so just historically
what have been the big problems that
have been solved and looking to the
future what are the big problems ahead
i think planning is pretty well under
control now and i haven't been keeping
track of that literature
frankly but um there are various
programs that
win so-called arpa planning contests
and they put together plans that seem to
be quite beyond what humans would
ordinarily do
planning movement of supplies
doing things like pert charts and
for a long time i think the problem of
dealing with plans which had a
a um a big aspect of time
involved in them like certain things
would take a certain amount of time
these things could be done in parallel
these other things couldn't be done
how do you uh how do certain time
dependent processes
interact and i think that's largely i
think they've made a lot of progress in
that
although i haven't been keeping up with
it
um and in terms of um so this project
you did right after
shaky which was the sort of uh coaching
coaching the computer-based consultant
as we called it um this is
sort of a form of human robot
interaction and that's where computer
human interaction
do you see areas in which ai will
advance the ability of robots to
interact
directly with people well there are a
lot of people focusing
on interaction between humans and robots
particularly
the um social interaction
amongst them i think some people at
cynthia brazil
at mit and some others uh haven't kept
up with that either
but yeah i mean humans are going to have
to
well we we hope we don't put too much of
the burden on humans
that they have to be specially trained
to be able to
interact with robots but robots are
going to have to understand humans
and so there is the need for
i believe robots to have models of what
humans believe what human
goals are what it is people are trying
to do
and so for a long time there was work on
uh what might the psychologist might
call theories of mind
how is it that one agent can understand
what another agent believes and what
another agent might
um might conclude and there are special
logics designed for dealing with that
i'm not sure where that's going nowadays
but i think it's an important area for
uh for work and robots will have to have
some idea of what it is that the people
around them are
wanting to do what their goals are how
the robots can either get out of the way
or
help achieve those goals
and you mentioned before that a lot of
the methods that are successful are the
ones that need lots of lots of examples
whether it's natural language or image
processing
do you think that's going to be a
problem for robots that have to interact
with the world and
some of their examples might be you know
crashing into things
and things like that well there's no
reason why you can't give them lots of
examples
i mean they don't have to in their life
have experienced all those examples
you can just put them in which you can't
do with humans
um so with humans you actually have to
learn by
by living both robots you can put the
the life of
previous robots right into them
and uh so
um and in the case of watson
were there specific things you noticed
and the kinds of errors that it made
that made you sort of think about how
the structuring common sense
you know i recorded all of that and i
looked at it i've forgotten some of the
questions that were asked
i know it it um well
for example it on this toronto thing
remember the
example in which it was asked uh well
the clue was
world war ii aviators or
naval battles for which airports were
named
and the answer would be midway
and chicago and o'hare
and it mentioned something in toronto i
think
even though it's supposed to be a u.s
city and i think the
people who designed it explained that
well
often times the category u.s city is
doesn't really mean exactly u.s city
it's sort of general
and so it didn't count that as heavily
watson didn't count that as heavily as
it should have but
uh i don't know perhaps some common
sense about how those
maybe maybe it had some uh inaccurate
common sense
uh that allowed it to answer toronto
but anyway it was a failure of some sort
of common sense reasoning
and do you think the increased power of
computing is going to
really advance artificial intelligence
and robotics at a much faster rate
well if you had asked if you would ask
me that a few years ago i might have
said
no because i think that whatever the
power of computing was
at the time it was fully able to handle
any of the ideas that we had at the time
i mean
we were idea short we weren't hardware
sharp
mainly now i'm not so sure
because the new idea that's come up the
use of lots and lots of data
well lots and lots of data require lots
and lots requires
lots and lots of computing and uh
so the more computing the faster it can
go
the better i don't know that that's the
bottleneck at the moment
after all if you talk to the watson
people which i haven't
but if you did talk to them and you
asked them gee
how could watson have been better
like if you had a computer this ibm
7000 series or whatever it was if it
were 10 times as fast
100 times as fast 100 times as much
memory would you have done better
i think they'd still answer well not
necessarily we would need more ideas
about how to program all that so it's
also about the structuring of the
knowledge
do you think there's specific problems
in ai that aren't going to be
susceptible to
large corpuses of data or that require
well large corpuses
of data um are going to be useful
in lots of them i don't know that it'll
solve all the
problems in ai i mean um
right now we can't do all the things
that humans can do
uh look around you and you can see i
mean we have office buildings full of
people
and many of them aren't using their
hand-eye coordination
it isn't mechanical engineering which is
a problem
you might ask why are they there what
are they doing
well they're having meetings they're
filling out
paperwork they're doing studies they're
communicating with other humans they're
making plans
those are all things why can't computers
do all that
why is there anybody in those buildings
except the janitors
and maybe a few top bosses uh well
every and and computers would be cheap
if we could do it
cheaper than those people uh so
why are they there well because
computers can't do it yet
and uh well lots and lots of data solve
that problem
i don't think so might help might be
part of the solution
but the reason we're not we don't have
what you might call
human level intelligence yet is that
we just don't have the ideas needed in
order to write the programs that would
allow us to
achieve human level ai
but you know we have a lot of smart
people
and i think we're making some progress
it will be a problem in the end i think
for
society what happens what do we do with
all the people
that computers replace
and eventually i mean right now uh
you need more and more skills in order
to have jobs
uh but the uh there's this guy robin
hansen you know about robin hansen
robin hansen is an economist and uh
he has got this interesting metaphor
of sea level rising sea level is what
computers can do
and land and the land that's inhabited
the jobs that require humans to do
and sea level's been rising and on the
shore
a lot of people are displaced well
they've had to move to higher levels
but to move to higher levels they have
to have more training
now the fact that sea level's rising
itself makes some higher levels
it's a funny thing it's sea level build
some
inlet even some mountains so people can
climb those mountains
but city level will keep rising and the
question is will it rise above even
those mountains
and uh so what do what what what do we
end up having people do
well there are certain jobs that only
people can do you can't have a machine
make
sweaters made by hand and there's all
kinds of things which involve
social interaction which only people can
do
some of the social interaction maybe
machines can do but if you really want a
human you got to have a human
and so i'm not saying that all jobs will
be replaced
but we're already seeing a trend of many
and i think that trend will continue and
you read people who talk about the
current
slow recovery of the economic situation
and and many people say well
we've laid off a lot of people because
of the recession
but in the meantime we found out that we
could do some of those jobs that those
people did with machines
and by the way we're not going to hire
those people back
and so i think that's going to be a
continuing difficulty
what's your advice for young people who
are interested in a career in robotics
or yeah
right now i i don't know i think i'd uh
try to get involved in the bridge
between ai
and neurophysiology neuroscience let's
take neural science
in particular because i think there's
still a lot of secrets about how the
brain works
that we don't understand that'll be
helpful in engineering
and the reason we don't understand them
i think is we haven't
invented the concepts needed to
understand them
i have this analogy with computers
if you had a martian coming down looking
at computers
measuring all the currents flowing back
and forth in the transistors
you know no amount of all that measuring
no amount of understanding how
transistor works
is going to tell you how say an online
banking system works
or how an airline reservation system
works you have to have
concepts that got invented in computer
science
do you even understand them you have
concepts like lists
programs data structures compilers
and they're probably a whole set of
analogous
concepts in helping us understand the
brain so
yes it's important to understand how a
neuron works and a lot of
neurophysiologists might complain
that models of the neurons that the ai
people are cooking up
aren't accurate enough well it might be
but no amount of detailed understanding
of an individual neuron
or even how neurons get interconnected i
think you're going to
be sufficient to have a good explanation
of
how it is that we do what we do we have
to have some higher level things and
there are people working on that
but some of it i think will get
developed by those very people who have
one foot in
artificial intelligence and one foot in
neuroscience
who say ah you know the uh
the analogy to
these high-level programs is such and
such the way the brain does this they
will invent
concepts that will then help us
understand the brain better
okay is there anything you'd like to add
on stores
i think you've got i think you've got
everything i well
i'll think of things after you leave oh
well um on the wiki page it said that
charlie rosen
started a winery yes he did
ridge wines you know about ridge wines
yeah i bought a model on my trip
good lines i've been up there helping
him pick grapes
uh well in santa cruz mountains it's
actually in the foothills behind
cupertino okay
you can get to it ridge winery it
probably has a website
it was bought i think by a japanese
company but i think they kept the same
winemaker
and he and three other people at sri
started that winery i think they've all
passed away now well everybody has
charlie did too