Geordie Rose - Will AGI need to be embodied?

hey everyone welcome back to the podcast
i'm especially excited about today's
episode because we're talking to jordy
rose
who is the founder of one of the world's
first quantum computing companies
is a company called d-wave that many of
you may already have heard of now jordy
really is a hard tech pioneer at the
time he founded d-wave back in the 90s
next to no one was paying attention to
quantum computing at all
and he's also been ahead of the game in
a completely different area and that's
reinforcement learning
in fact at the time i first met jordy
back in the mid-2010s
he was already very focused on the
potential that reinforcement learning
would have
for augmenting the capabilities of
existing ai systems which is why he then
went on to run another company called
kindred ai which developed one of the
first concrete applications of
reinforcement learning in industry
majority's ultimate focus is now on agi
something that he's working on at his
new startup
sanctuary.ai now sanctuary's approach is
based on a really exciting and unusual
thesis which is that one of the easiest
paths to true agi
is going to be to build embodied systems
so ai's that actually have a physical
form and structure and they can interact
with a real physical environment
so we're going to be talking about that
thesis as well as other questions around
what happens when agi hits
how humanity can best prepare itself for
that event and the broader ai
alignment and ai safety problem so i'm
really looking forward to diving into
this conversation i hope you enjoy it as
well
hi jordy thanks so much for joining me
for the podcast
uh thanks for having me should be fun
yeah i'm really excited to do this
uh obviously we've spoken before uh
quite a few times about agi i'm looking
forward to this conversation to have
like kind of a
a broader chat not only about your your
background but also what you're working
on right now which is very agi relevant
for context you were one of the pioneers
of applied quantum computing which
is or was your main area of focus at a
time when almost no one was paying
attention to it
i'd love to start there so can you can
you tell the story of how you became
interested in quantum computing and how
that led you to start working on what
would eventually become d-wave
uh yeah you bet so i um i was
at ubc in grad school doing physics
and the uh the area in which i was
doing research was called condensed
matter theory
which is the study of materials at at
low temperatures
and in my case the uh the particular
stuff that i studied was a material
called the molecular magnet
and the if you can think of a molecular
magnet as being kind of a lattice of
tiny little magnets
and those tiny little magnets um flip
around a lot
and how they do that uh it gives these
materials their properties and it turns
out that
that as you cool a material like this
there's a very abrupt transition
between these little magnets acting like
quantum mechanical objects
uh somewhere around 300 millikelvin so
that's 0.3 degrees above absolute zero
so very very cold temperatures
but the the behavior of these things is
remarkably different below this
temperature
and uh it occurred to me back then so
this would have been like
late 1990s that you might be able to
build a computer
out of something like that
and above at that time there was some
academic research about
uh using materials like this is
potentially
new kinds of probes of crossovers to
things like compute computational
complexity problems and things so things
that were not
traditionally viewed as physics problems
and
again around that time there was some
work at mit
to take the uh these basic ideas that
arose from the study of
materials and and uh and write them in
the language of uh
this thing called quantum computation
which back in the late 90s
was nowhere near as commonly understood
or talked about as it is today back then
it was uh
it was an academic thing almost entirely
um so
i i i got obsessed by this idea of could
you actually build a computer out of
something like this
and uh um that ended up being the
inciting
motivation for starting d-wave so when i
when i finished my graduate work at ubc
i i founded d-wave uh together with a
few other people
and we started thinking about uh how
would you go about
actually building a a quantum computer
and that was in 1999 when we founded the
company
well yeah and since then d-wave
obviously has become known as like a
leader in the quantum computing space
you've also gone on to focus more and
more on machine learning which now is
basically your entire focus and
specifically this
question of how to build agis i think in
our conversation
i think one of the most important or
interesting through lines has been
you have this interesting embedded
systems thesis about
sort of what it would take to get to an
agi would you mind exploring that thesis
a little bit kind of unpacking it and
why you're thinking about agi in that
way
sure yeah i would i would characterize
my my technical interest is not really
about machine learning because i think
uh we haven't said anything
controversial yet so i'll start
cognition of the sort that all animals
have including humans
uh learning is a part of but it's a
small part
uh most of what we do is not learned
and the the the vast bulk of the uh the
uh the equipment that we bring to bear
on the problem of getting around the
world
is learned in a sense it's learned over
evolutionary scales by
evolution trying different things and
the
ones that are you know able to succeed
in
making copies of themselves through time
uh those
uh trials if you will the the the
experiments and
hyper parameter settings uh that are
successful
um make it through and get passed on so
my my view and i say like
my is kind of the royal my because
there's kind of a group of us who've
been pursuing these types of ideas now
for more than a decade
is that the uh the correct lens through
which to look at general intelligence
is uh through the lens of the the
biological lens
so we have this example of what we think
of as general intelligence which is
human-like
but we have lots of other general
intelligences around us
virtually every biological organism is
general in some sense they have to deal
with the
peculiarities and weirdnesses of the
real physical world we all do
everything from bacteria to flatworms to
oak trees to us all
share the same physical universe and we
have to find our way through it
so there is a sense in which uh all
evolved creatures have a type of general
intelligence and the
the thing that binds them together the
reason why i
view that way is that they all have a
situation in the physical world so all
of these things have what you could
think of as a body
a vehicle through which their actions
are taken
and a vehicle through which information
about the world comes into them through
their senses
and uh this picture of um
a a thing that's situated inside an
environment
would be common to anybody who studied
reinforcement learning because this is
kind of the central premise of
of the world view that reinforcement
learning represents is that you have
you have observations which are
partial in the sense that you can never
know exactly what's out there
and then you have uh as an action space
which
are the sorts of things that you can do
and in a physical object that means
movement
uh usually i mean there are there are
some there's some uh
things that we can argue that you could
do you could take actions that aren't
movement
um and that's fine but the basic premise
still holds is that you uh
you sense the world around you you have
partial understanding of what it is
and then you you move or you take an
action and then
uh the the fitness of the sequence of
actions you take
somehow matters and in a biological
system it's related to survival and you
know the other things that we need to do
to get
ourselves around the world without
killing ourselves or otherwise making a
mess of things
so the the premise that we've always
followed is that the
the clearest path to cutting through
this extraordinarily difficult problem
is to mimic uh biological systems and
the types of intelligence that they need
to navigate the world and so we build
robots and we build control systems for
robots and these control systems for
robots to the extent that we can
mimic the um the types of functionality
that you'd imagine you'd need
not one-to-one you know we're not
copying the brain
but we're thinking about what properties
of the brain
are required for you to for example know
how
uh to reach out and grasp an object or
to know how to walk on
uneven terrain um or to know
how to reason about the world
all of in the ways that we believe we do
or we we suspect or
you know the sorts of ways that humans
or other animals do it so that's
basically the thing i think it's
it's it's kind of obvious and i think
the reason why
uh you know we are a little bit ahead in
this game if we are
is that we committed to it uh early you
know we've been doing this now for um
for about 10 years uh we have one uh
exit from a product that is was used use
these ideas
in some way and we continue to work on
it and try to generalize these ideas
actually yeah you were just telling me
before we we started here that your last
exit kindred
actually is pretty recent and i think
it'd be useful to provide a little bit
of context on that because it is so
core to this whole story yeah sure so
the the origins traced back to my last
days of d-wave so i had a
team around me that was working with our
customers and
everything that anybody at that point
did with the d-wave hardware had to do
with uh
with a type of ai problem which is
sampling over
probability distribution so there was a
particular technical thing that
we thought we might be able to do well
and we were in contact with a bunch of
people who were thinking about that
problem
more or less all researchers uh and they
tended to be the the kind of the cream
of the crop because this was an exotic
piece of technology and um
people were thinking kind of about the
bleeding edge so one of the members of
my team uh suzanne gilbert
um hadn't had a series of ideas about
how you turn some of these advanced ai
ideas
into control systems for robots that
would be uh
biologically inspired quote-unquote uh
which
which means that they would be intended
to be control systems
that would allow a system to navigate
the real world
and uh i thought they were brilliant
ideas that needed to
flourish and dwave was not a robotics
company and
never would be so i encouraged her to to
leave
and and start another company so that
she founded kindred
in 2014 and in the
months that followed she called me up
and asked me if i would be interested in
coming and running it
with the the premise being that we would
build a new type of control system for
robots
that could dramatically extend what they
could do and potentially even in some
limit create general intelligence
so i was absolutely fascinated by this
as as a thing
i've always thought that it's kind of
obvious that uh
the uh that the sort of thing we call
intelligence is much more important than
what we call computation
computation is a tool it's a thing that
you use to achieve some objective
but the setting of the objectives or the
somehow the figuring out of what
questions to ask is clearly more
important than the tool
so i was more interested in that so i
left
and uh we ran it together for four years
up until 2018 and uh we we
we brought it through three rounds of
financing up to a 100 million dollar
valuation
in um would have been late 2017
uh we had supreme of the crop in terms
of investors and a very very good
technical team including some of the
founding members who are
who are absolutely excellent and uh
we've we turned the
the spotlight of all this technological
stuff we've been working on
on a on a problem that really resolves
down to one
technical issue which is can you build a
robot
that can manipulate and grasp real world
objects
so this sounds really easy right because
any human can do it without thinking
about it
but this idea of doing it in the real
world turns out to be
at the heart of why it's hard to build
robots that can you know make their way
around the physical world
so we focused on that and in particular
on a specific implementation of it
in the e-commerce distribution center
ecosystem there's a
series of places in these large
distribution chains where things that
are not easy to specify in advance
because they change all the time you
know what you buy
is not the same this week as it is next
week and there are millions and millions
of these things that you can buy
so building a robot that could
manipulate and grasp any of them
and we did that it was the first
robot to use reinforcement learning in
production this was one of the ideas
that suzanne
one of many ideas that she had woven
together in this kind of cognitive
architecture that she developed
uh and uh it was very successful
last i heard they'd done more than a
hundred million successful
uh grasps of objects in the wild which
is considerable you think of it as an
episode in reinforcement learning
yeah positively labeled episode um
and the uh the company was acquired for
uh 200 pounds a couple days ago
uh which in canadian dollars yeah 200
million pounds
uh which is about 350 million canadian
um
by a british uh a british company so
the the way that the just getting back
to the agi question so how does this
intersect so the underlying ideas that
were used to generate that
that product um
can be used to potentially use the same
ideas but building
up a robot that differs in the sense
that the robot is a general purpose
robot
so this is a this is a new thing that
doesn't quite exist yet although we've
been working on it pretty hard for many
years
is the idea of building a physical
system a a robot
that isn't designed to do one thing but
it's designed to do anything a human can
do
and if you can apply the same kinds of
ideas that suzanne developed in the
kindred context
in the context of a general purpose
robot
then you can deploy it in any place that
does work
so it can make clothing it can
serve you coffee at starbucks it can
deliver your food it can make you your
food
it can take care of you when you're
older so the idea of building
one robot that can do all of those
things
is the central premise that is is behind
it and related to our approach to body
cognition this idea that you want to be
able to build a physical body
for something like a mind mind being a
robot control system
that allows the physical robot to be
able to do and
be in the world in the same way that we
are and navigate it and understand it in
the same way we do
and what is the what's the nature of the
world model that gets built by i mean to
the extent you can talk about it
obviously a lot of the stuff is going to
be under wraps but
um are there are there things you can
say about that world model especially in
the context of
you know today's ai systems which are
very narrow like we look at
even a system like gpt3 which has a lot
of really impressive few shot and zero
shot behavior
getting to the point where we have like
one system that can do a whole bunch of
different things
does that require like a paradigm shift
beyond the gpt3 like
scale deep learning massively is there
is there something else going on
under the hood here well i mean i so
i've always
thought that the uh you you get what you
pay for right so if you
if your objective is to build uh
something like gpt3
where you uh you start from the premise
that there are no priors
essentially you know what you're doing
is just taking all the the data that you
can find and then
and then processing that data and
extracting the statistics of the data
so if you start from that premise that's
what you'll get you'll get a system that
if you do it right
will be uh absolutely great at that
thing which is
learning about the statistics of the
data that you send it
now that to me is a
a very interesting thing but it has
nothing to do with general intelligence
and the the and again let controversy
alert
uh there are way different ways to think
about how you
understand what intelligence is and then
how you go about building it
so i know i have utmost respect for
those guys i think that what they've
done is
technology it's it's it's just an
amazing thing you know that gpt3 thing
is like alien technology you know you
don't even know what it is
yeah uh you kind of explore it
experimentally
uh not from first principles almost
so the um uh i felt very weird actually
when i started
playing with it because it is really one
of those uh you know the blind man in
the elephant thing
where it's unclear what it is
even after you experiment experimented
with you know i understand what it is
you know i understand how it was built
and all of that but
but what the artifact that it created
was something very very strange
and this idea of it being alien
technology appeals to me because it's
kind of like
if aliens built something and we didn't
know why and they just gave it to us and
it was that i wouldn't be surprised
yeah anyway so so the my my approach
uh or our approach to how to do this is
is very different
so ours is almost on the other end of
the spectrum where if you're going to
build a robot
that actually can do things in the real
world in the way that you'd expect a
squirrel or a you know a bird to
uh the idea of learning over
data with no priors makes little sense
outside of an academic environment so
there are people like rich sutton who i
admire and respect almost more than
anyone else in this field
who take this perspective that what we
really want to do
is uh okay if he calls it the bitter
lesson or something like that
is that we want to avoid um we want to
avoid putting priors into
systems and try to learn everything from
scratches to the extent that we can
and if we do that then we're kind of
limited by how smart we are
and our computational power and if you
take
take it for granted that we're going to
be as smart as we can be the limit is
computational power and our ability to
do things in the with intelligence in
the world is ultimately limited by
our access to compute cycles so that's
one perspective i don't take that
perspective at all
my my perspective is that we have uh
as much evidence as we need about how uh
the priors need to be structured in
order to
hang learning on them where it's needed
so machine learning and
particularly things like supervised
learning have a role
in building a a cognition
for example visual systems converting
pixels into representations
clearly that's going to be something
connectionist it's going to be a neural
net of some sort
but what you do with the resultant
representations
is clearly in my view not just about
learning you know there is something
going on in our minds that we know quite
well
you know if you if you think about the
intersection with neuroscience there's
lots of clues about the ways things have
to be
uh i'll give you one clear example of
this so
we uh and it goes to your original
question about like how do we think
about the world models that we built
so the first thing is the robots have
world models so that's
that's one thing is that the the the
internal life of one of these cognitions
has a simulation of reality in it
not at all obvious it's not the way that
all
uh you know approaches to ai work of
course
but in this type of a system a control
system for a robot
the internal model of the world has to
be high fidelity representation of the
actual world
so think about the the matrix right the
movie
so the people in the matrix at the
beginning have such a high fidelity
uh immersion in this simulated world
that they can't tell it's not real and a
robot
needs to have something very much like
that an immersive
internal model of the world that is not
only about
what you can think of as representations
or in the game language the scene graph
of the environment the things that are
kind of platonically there and their
properties
but also the the the renders of those
things which visually you can think of
was what you're seeing
so step back a minute and if you've got
say an object on your desk or you're
staring at something
there's a thing philosophically speaking
how do you think about this i
this this what's going on so there's a
thing there you believe that
but what your eyes are seeing are a
render
of that thing the light bounces off of
it and we get some
picture of it on our retinas and then
something happens something happens in
our head uh
the the way that our internal models
work in these robots that we build and
also
to a certain extent the ones that
kindred although to a lesser extent it
wasn't as
necessary is that there's a an internal
model of the world you can think of as
like a video game
which is a one-to-one representation of
what the thing is actually looking at
as it looks around and it and the pixels
that hit its cam the light that hits it
count its camera
resolves into something that it might be
able to recognize like a cup
that generates a hypothesis that there
actually is a cup there in its internal
model and in the video game such
situation you could think of as a cup
popping into existence
in your video game exactly where the
robot thinks it is
and that cup is then compared to the
input from its pixels and
another important point about our
perspective is that
unlike nearly everybody else who does
this sort of thing to us
the simulation the internal model of the
world
is the primary model
it's not the senses so uh it's
very easy to fool yourself into thinking
that what your brain
imagines is happening is what you are
feeling
touching smelling seeing and hearing
that's not the way we approach the
problem at all
the way we approach the problem is that
what you think
you're seeing smelling touching and
hearing is actually
the inside model of you the model you
have in your head
touching the simulated stuff in your
head
and your senses are a secondary thing
which ensures that that model doesn't go
out of lockstep
with your surroundings so this this this
peculiar notion that
you know people have referred to
sometimes as like uh
you know we we exist entirely inside
this
piece of bone we take it literally and
the
the senses then become checks they don't
become primary they're secondary
they're they're kind of like the anomaly
detector signal that you use to
ascertain whether or not your internal
model of the world is in accordance
with what your senses are telling you so
this flipping of the
of the of the game from senses or
primary and we should be building
supervised learning systems to see if
there's a cat in front of a robot
to know the internal model is primary
i have a model that there's a cat at
that position and now my senses are
telling me whether or not i'm right
that flip is a core part
of how we think you have to think about
uh
cognition in this in the context of the
body cognition if you're going to build
a robot that can make its way around the
world it needs one of those things
and you need to look at it in that way
or else you're going to get what we have
today
like look around you where's the robots
they're nowhere unless you count cars
which you might but this premise that we
have machines that can do a bunch of
stuff is just not been
uh seen and like in quantum computing
the ev the evidence in front of your
face that you do not have it
is evidence that the state of the art
and the way that people are pursuing the
problem is
wrong so it's not that the ways that
robots are built are somehow going to
gradually get better and somehow
magically going to turn into something
else
that won't happen you know robots have
been moved around the same way as you
know barring the kindreds and the
covariance and other people who are
trying to do things right
the same way ever since there's been
machines there's something new that's
needed
and so the our what we're working on is
is the extension of the kindred
hypothesis
to the general case to try to build a
machine that has a mind
uh of a sort and um it's so it's able to
navigate the world
in the same way that a biological
creature would and be able to do all
sorts of things that are completely
beyond the scope of any machine that's
ever been built
that's that's so fascinating and it
sounds like there are basically two
different layers then to the
to the thesis here the first one being
okay we need an embodied system because
that's
one part of the evolution prior let's
say like we we know that we've evolved
to have bodies and that must be part of
this
and then another part is this almost
like i don't know whether to call it
like
metacognitive or almost buddhist or
whatever you want to call it but
this perspective on the difference
between numina like the object that
really exists
and then our perceptions of it that
seems like it's its own separate prior
is that sir how you're thinking about is
it two different uh camps or are they
are they also linked in a way that maybe
i can't think of
well they're very they're definitely
linked because the the fundamental
reason for thinking about the world in
terms of models
and not senses is connected to function
and if you can think of it like this
like how do you actually pick an object
up
and it's not as simple as you think so
if you're a sense first person
i'll just give a silly example but it
matters in robots
so let's say a sense person sends first
person and i've got this thing called a
cup
right so i'm sensing it by looking at it
now when i reach out to
to grab it my hand includes the object
so for a while while i reach out to grab
it
my senses can't tell it's a cup
yeah so how do i know it's still there
and how do i so this is a
silly example but it's an example of of
of the
of the the first crack in
a sense first uh uh perspective if
you're trying to build a robot that can
navigate the world
if i if i'm solely running say clarify's
cup detector on my robot
at some point in the grasp procedure
that cup detector is not going to fire
anymore
and the robot is not like us right the
robot doesn't know
quote unquote it's cup because you
didn't tell it
that there's something called object
persistence
which is the fact that we know in
physics
uh f equals m a and all of that
means that that thing is probably not
going to move
unless something else happens that i
probably can detect
like you know a gust of wind or
something so the
the the the that that very simple
example
which is that the sense first approach
to the world clearly is wrong
in our case for something even as simple
as reaching out and grasping an object
uh means that you have to add something
else and so
if you're a roboticist wanting to solve
that problem you might say yeah well
we'll just
we'll just put an entry in a database
that says somewhere that if i see a cup
somewhere
i'm going to leave it there but then
what you have is a cascading hack
that ends up not scaling if you start
just adding pieces like this one at a
time
you're never going to pick that
underwear up out of the bin because
there's going to be something some edge
case
your brittle system that you tried to
hand code will not work so instead
let's say we we take the the the model
first view you bake all of the priors
of the world into your model because we
know what they are there's this you know
this it's it's absolutely peculiar to me
that we've known newton's laws for
hundreds of years
we know how the world works we don't
need to learn it
we it's given to us at least at the
scale in which humans act you know maybe
quantum or general relativity there's
some questions but
the physics of cups are absolutely known
we don't need to learn anything about
the physical world we already know
everything there is to know so if you
can build a simulation
that can essentially just be an f equals
m a solver that's good enough
uh you can pick things up in simulation
you can do things like the prior is the
object stays there
until it's moved and now my senses are
just verifying to some extent that the
object is still there
so if my hand partially occludes the cup
i don't care that my senses can't tell
it's a cup it can still see some pixels
and so if i if my my model says there
should be a cup there and i can still
sort of see a little bit it's enough
so this idea of putting the model first
solves an enormous swath of problems
in the uh navigation of the world so
they're back to your question about
priors
so the cognitive system that runs in a
robot that's supposed to be
like a humans contains dozens
of things like this where you have you
specify in advance
i know all of these things it might be
an engineering challenge to run them in
real time
like for example the types of physics
engines that run
in uh like say the phys x engine that
nvidia has or others of its sort
they're not designed to solve physics
what they're designed to do
is solve the physics that you would look
at
if you're running a game so there are
engineering challenges in going from
the physics models that are the sort of
you know the right ones uh that include
things like friction
and surface stuff and all that uh but
they're engineering challenges
and so you know like everything else
i've ever done in my career
the the the question of how you peel
back
a really complicated problem that isn't
obvious how to solve
is you first divide it into pieces and
then you take each of the pieces
seriously
and then you do it right a priori you
don't use all the stuff that people have
built before
if it's wrong and uh you know in quantum
computing and in
in agi call it agi i don't even know
what to call it but this idea of
building
reasonable control systems for robots
that the dogma the state of the art has
failed
and because of that following the same
path that people have laid out before
will fail also now you'll get successes
like kindred where you can make a few
billion dollars here and there and
that's fine
but that's not that's not what i care
about what i care about is trying to
solve this problem
how do our minds work well enough to
build them i view that as being
the single most clear holy grail of
all technology you know if you can solve
this problem it's bigger than any other
problem that
humans have ever conceived of everything
we've ever done or thought
believed dreamed it all lives inside
this thing we've got to understand it
why don't we it really bothers me that
we're all
all seven billion out of us are carrying
this thing around with us
and we don't know how it works doesn't
that bother everyone else
i mean it's a travesty we need to
understand how this thing works
if we if we're gonna if we're gonna call
ourselves intelligent
as a species i think one of the tests of
whether you get to call yourself that
is the species has to understand where
that thing comes from
and right now we don't so let's let's
let's change that
i think it's time for uh for our
the our community uh machine learning
researchers ai researchers
to really take and roboticists as well
to really take for
for uh seriously the question of can we
actually solve this problem and if we if
we were
how would we do it so we have our own
angle uh it's not the only one
uh there are the data driven approaches
of you know rich sutton and elias
sutscover and these other folks
who you know are doing it a different
way i think that it's
time for us to to solve this problem and
when we do
there's going to be unlocked potential
of the sort that's never happened in
human history i mean the steam engine
is going to look like a bump in the road
compared to our ability to harness this
particular type of technology
we should do it and uh the time is now
you know people are always thinking
oh it's 20 100 years out those ideas
that something is 20 years out have no
weight in my mind
because the the the good friend of mine
eric ladazinski a guy who's
i used to work with a d-wave he said
this thing and it stuck with me said
if you can do it in 20 years just do
what you would have done
then now and that that's kind of a
really deep thing is that if you can
figure out what the right things to do
are
you're not necessarily bottlenecked by a
span of decades so you just have to be
smarter about how you attack the problem
be efficient and and solve the right
kinds of problems in the right order and
you'll get there a lot sooner than
people
thought i'm not saying that agi is easy
it isn't
it's way harder than anything else i've
ever worked on like
it's orders of magnitude more difficult
than quantum computing
like orders of magnitude it's not even
in the same ballpark
but uh i think it's also concomitantly
more important
like i said a computer is just a tool it
just answers the questions that we post
to it if it's built correctly
but this thing that we're trying to
understand which is how we understand
the world
what are we what is do we have a purpose
if so how could we find out what it is
um
is is there is there any meaning to any
of this
and if so what is it what is the
eventual outcome
of all of this if you go far enough into
the future all these questions are
things we should be able to answer
and um you know that that kind of
answering those kinds of questions
trumps computation in you know some
massive way
well and this ties into i guess some of
the bigger questions about the future of
agi
like one of the common concerns is that
humans don't yet know the answers to a
lot of those questions what gives us
meaning
what do we want what's our morality and
as we start to look at
embedding some of those moral frameworks
in machines either in the form of priors
or in the
form of a data-driven approach like the
one openai has taken as you've
highlighted
it it sort of forces us to have the the
philosophical rubber meet the road
are you are you concerned about like for
example what's been called the alignment
problem this idea of aligning machines
with human values is that something that
you think is surmountable or that we'll
have to face soon
well i mean there is this this is a
tough one because there's all sorts of
ways that you can create technology that
ends up having
uh unforeseen consequences you know we
see it all over the place
you know like social media is a prime
example
social media in and of itself is neutral
you know it's it's a technology that
allows people to
communicate in a certain way but like
everything else
in any con sufficiently complex system
it's very difficult to figure out what's
going to happen
when you mature a technology like that
and
it's going to have negative consequences
as well as positive ones
nothing is ever cut and dried you know
everything is gray
the looking at social media is a case
study
of how a powerful technology has good
and bad parts
and how to how a society should uh
deal with this sort of a thing i think
is a good way to start
having the discussion so one of the
things that occurs
is that you know take the social media
example
so social media has brought home a point
that i think
we sort of suspected but is made clear
that uh the human mind is extremely
vulnerable to certain
uh column idea pathogens
so if you yeah if you introduce
an idea in the right context
you can get people to believe it no
matter what it is
so i've often when i was when i was
younger and even to a certain extent now
you wonder about something like nazi
germany like how could that happen
how could all of those people uh
agree to some things that you know
any sane person would say how
how are we allowing this to happen this
has to end
so how does that how does that how does
that work and i think that the
the the social media experiment has
shown us
that the mind of humans
evolved in a particular type of
landscape where it was very important to
us to be part of the tribe
if you were if you were kicked out of
the tribe
for through most of our evolutionary
history you'd die
because we need as a social animal with
very little physical
ability we need to cohere and work
together
in order to attain
so social media hijacks this it creates
tribes where your belonging to the tribe
overwhelms your cognition so your
irrational thinking about something gets
overwhelmed by your need to belong
and if a million people seem to be
saying the same thing
you are almost powerless to disagree
and this is not just about uh say the
right-wing
trumpist stuff the left wing is just as
bad
there are all sorts of idea pathogens
that that every political stripe
um adheres to without
thinking through the consequences of
them and often they sound good on the
surface
but if you start thinking about them
they rip apart so
the social media thing is is both good
and bad now if you think about like this
idea about
how to treat ai as it emerges
so ai is going to be an extremely
powerful thing that i think
in our lifetime will lead to machines
that we would consider to be
sentient so we will have discussions
about whether
the systems that we build within our
lifetimes i'm not going to put a date on
it because you know you can't predict
the future but
my sense is that the problems are not
insurmountable in some period of time
that we can we can count
so uh let's say that happens we're in a
very different space
where we're no longer the the top dogs
in terms of you know some things like
you know ability to reason or something
like that
so then what and i think it's the same
thing is that we have to step back and
ask
um what is what is a salve or a bomb
that gets that helps us navigate the
good and the bad of the future
and as much as i hate to say it i think
this
really traces back to um
to people learning at a very early age
that there's a lot of value in
skepticism and not believing what you're
told
so if there was one thing that i think
would be
uh the antiviral against all idea
pathogens
it's to never believe anything you hear
so if you start from this premise that
no matter what anybody tells you whether
it's your mom
or a political figure or your advisor
when you're a phd student
or your teacher when you're in grade
school
everything they tell you question it and
don't believe it
and then figure it out yourself so that
second part is hard
but i think this is the this is the
inoculation against the
the idea of pathogens is that think that
anything anytime anyone is telling you
something they're trying to get
something from you
that isn't in your best interest even if
it's just for you to agree with
something
so don't you'll put put your put your
own brain
before the brain of the tribe and don't
believe anything you hear
even if it sounds good on the surface
and think it through
and make up your own mind and even then
you know you're not going to necessarily
get good outcomes with ai i'm not so
naive to think that
you introduce something like this into
human society that things couldn't go
sideways
but i think that the the thinking for
yourself part is the
is the key to inoculating against all uh
bad outcomes because i give you
something
you could use it for call it good or bad
those are relative terms but let's say
there's some general sense of what that
means
uh you have to be able to reason
about your situation well enough to be
able to make good decisions
about the technology its use its context
and all that
so for me you know my mental model of
the world is that we are about to share
the
planet with a a cambrian explosion
of uh new kinds of entity they're not
going to be like us
they're going to be as alien as gpt3
or as humanlike as robots that are
supposed to be like us
um there's gonna be thousands of these
things running around
and we are no longer any illusion that
we had that were kind of quote unquote
in charge which i hate as
that's the one of the worst idea
pathogens there is
this idea of control um being a good
thing
is uh is gonna go away and it will have
to
because the we won't have a choice and
it's not bad
you know the this idea of being in
control is such a vague abstract notion
that
i i'm not sure even what it means you
know i hear this all the time
as an objective for how to deal with
emerging ai technologies is that
we have to be in control that's really
not
very good as an idea on several levels
again if you peel it back it doesn't
make any sense
you're not in control now i don't
control whether i pay taxes
go down the list of everything you do
today and you tell me where you have
agency
it's an illusion and so the question
isn't whether or not you're giving up
control the question is whether or not
the future is better than the present
that's the key question
and i view the emergence of these new
kinds of technologies as being a kind of
flourishing
it enables us to do things that we
couldn't have done before
and a a human-like ai
can be put inside a body that can
survive
space if we really want to go and
populate mars and thrust
this is a key piece of that story you
know can you put
a physical human body on mars sure can
you send them to
the next star system probably not so the
the the the flourishing of the kind of
thing that we are if we want that to to
go on which i think everyone does we
don't want this
great experiment in cognition to end
so how do we do that well we we we
flourish we diversify
we we we branch and
all this branching stuff doesn't mean
that you're lesser it means you're
greater
you created all of this stuff that the
future is going to hold
you were the the progenitor of this
massive explosion
in things like us you know if you
believe that being alive is a good thing
if you believe that being conscious is a
positive and you have the power to give
dumb inert plastic and metal
that gift shouldn't you
you know i think if the if you think
about the world as being
made up of things that think and enjoy
and live
and things that don't and we could give
the things that don't the same
gift isn't that a moral imperative that
we do that
so this this business about the the
the moral and ethical considerations
about ai
i think often end up being too
provincial you know we think about
today's technology where we're talking
about is like bias and data and stuff
like that
i'm not interested in any of those
conversations to tell you the truth
because
that the the real question isn't about
the bias of data
the question is about when we get to the
point of creating something that thinks
what is that going to be like because
then then we're talking about like you
know the uh the
a nuclear bomb versus like try like a
spark you know the difference between
the two in terms of its impact on human
society
they can't even be compared they're on
different scales yeah
the notion of an intelligence explosion
i think it's really interesting
when it especially in the way it
intersects with what you're describing
here which is like yeah we have these
moral agents which are you know ais that
are advanced enough that
have cognition we do have to think of
them as morally
valid agents and i guess the the
question is like
so when it comes to an intelligence
explosion one of the concerns
is that an intelligence explosion might
actually lead to the eradication
of human consciousness or cognition that
the coexistence
of human modes of thought and super
advanced ais that might be able to
self-improve
that might be able to kind of reach
heights of intellectual capacity and
just rich cognitive consciousness that
we can't even imagine
might be impossible like are you
concerned about that about the
coexistence
uh sort of not being a possibility or an
option for whatever reasons
yes but the the again the what i think
the right way to think about this is
you're choosing amongst
options so it's it's not a simple matter
of saying
you know there could be a bad outcome if
this particular thing happens because if
you ask
is there bad outcomes in any potential
future the answer is yes
if we didn't create ai and we just
continue on a certain path
what's the chance that we're still
around in a couple hundred years i don't
think it's very good
yeah so the the the i think again the
right way to think about this is not
is there a potential bad outcomes of
potential of past that we could take
that's wrong because there are bad
outcomes in all of them and it's a
fallacy to think that just because you
can point to one bad outcome in one
then the that that's the outcome is
first you know
guaranteed and somehow the only bad
thing that could happen
there are a lot of bad things that could
happen again it come it comes down to
what do you value so i value
uh the thing that we are you know i
would rather be
alive than you know a rock
and by the way not everybody believes
that there's this great book i always
recommend to people called uh it's by
thomas legatti
it's called the conspiracy against the
human race so in it he argues very
coherently that consciousness
is actually one of the worst things that
could you could possibly possess
as a piece of matter uh and and gives
arguments for why that is but if you
take that argument aside and you don't
believe it
and you think that being conscious is a
good thing then presumably we want more
of it
now this thing about intelligence
explosion by the way i don't believe
that the conventional use of that term
will ever come to pass
i think that what we call intelligence
is situational
intelligence is the ability to achieve
goals in a specific type of environment
and it's not a number so this is super
important it's not a thing that just
grows
it's it's a tool that you use to get
what you want
and so what you want in a biological
system is ultimately driven by
you know the evolutionary pressures in a
machine it's much more complicated
because at least at the beginning
uh that's set by human engineers so if
we want to
you build a reinforcement learning
system that learns to play go
we bake in the prior that winning that
game is what we want
we don't ask the machine you know how do
you feel about playing go
you tell it and the kind of intelligence
in that example is the ability to
achieve the goal which is to play go
well
it has nothing the intelligence itself
has nothing to do at least
at some level of analysis with the goal
itself so
i think that this intelligence explosion
is not a good idea i don't think it will
actually happen
in a sense of escalating capability i
think what will
more likely happen is you'll get the
cambrian explosion type of thing
where you have thousands of different
kinds
of intelligence which are you know
algorithmic
structures for achieving different kinds
of goals and they'll all be suited to
different niches
so gpt-3 as an example
is great if you're uh if you're a
a creative person trying to come up with
names for products
so at some point in the future probably
everybody in marketing is going to go
away
and there's going to be an ai based on
the statistical properties of language
and
the something we know about persuasion
and all it will do is create ad copy
and it will be designed such that that's
what it does
and even though you could say wow this
thing is really sophisticated and boy
does it do that well
it's going to be an alien intelligence
that doesn't ever want to
turn the world into paper clips or any
other of these ridiculous scenarios
it's structured such that that is what
it does and they're going to be other
kinds
of ais built that have other goals and
they can get better at achieving those
goals
but unless something fundamental changes
about the way that we think about
machines changing the goals
is a human exercise so if you have a
human
want to change the goal of the machine
to let's go shoot everybody
that's not a failure of the technology
don't blame that on ai
or cognition blame that on the person
who thought that was a good idea
and so i think that the the the again it
comes down to this business that
the the technology is gray you know it
will create
huge opportunities and flourishing for
some people
it will create absolute bad things for
others it's always that way every
technology ever in the history of the
world has been like that
the question is where does it end up in
the balance and i think that investing
in understanding our minds
in the balance is going to be the single
best thing that has ever happened
to the human condition uh it will be
for all of the potential negatives that
could happen and i'm sure some of them
will come to pass
and many of them we won't be able to
predict in the main
we will think that we were absolute
barbarians before this transition point
happened when we finally figured out how
it works
it's interesting yeah i mean we
certainly do think that about
our our past selves in a lot an awful
lot of different contexts so
in some ways it wouldn't be it would be
such a shock to find yeah like 50 years
later
when we have this technology thinking
back oh my god we used to do
dialysis we used to put people in in
prisons for crime and things like that
before we had better solutions
um there's even more more even more uh
radical there was a time when our
descendants didn't have language
right so we don't we don't even think
about those those people as being human
but they were in the sense that they
were our descendants
you know you go back far enough in time
and are directed that you know your
great great great great great
grandmother
didn't have language and so we view
these great dividing lines
in our progression um you know
as being kind of like things that are
done with
you know we okay now we're now we're
we've learned how to speak in
in you know in python okay we're done
yeah no there's all sorts of other
dividing lines
and i think the dividing line between a
an entity that can understand
the way that it's its internal working
of the
of the way that it it processes the
world and before
is as great of a divide as an entity
that doesn't have language and it does
maybe even a greater divide
and the fascinating thing which makes me
believe in the simulation hypothesis by
the way
is why is it that we live right at the
time when that transition is happening
it seems
unlikely but here we are
yeah no i agree and i think that there's
there's an interesting through line to
what you've been talking about
um including with the reference to sort
of the concentration camp guard in nazi
germany let's say
when you tie it back to this idea you
know we say oh well our ancestors didn't
have language they weren't even human
and the moment we say that we kind of
define this out group and we feel
i don't know if disgust is the right
word but there's like it's like
three percent disgust of like oh man we
came from the simeon origin
and and how low and so on whereas you
know today
morally we will be reprehensible to our
future selves or whatever
entities end up showing up you know 50
years 100 years from now when this
technology's around and
we realize oh my god like this is how
we've been treating each other
um the moral norms of you know just 10
years ago have completely shifted
and this idea that we that we found this
like one true
moral framework that's going to be you
know this can hold true for all time
although i'm sure the concentration camp
guard in nazi germany sure felt that
they were on the right the right track
i'm sure that joseph stalin's closest
associates felt that they were on the
right track
everyone seems to have this absolute
moral confidence and yet the one thing
that seems to keep happening is that
those moral values get
flipped over every couple of years or
decades
is that something that like that you
think is is gonna
is ever going to change are we destined
to keep seeing this like this moral
shift even as we ease into ai
as ai starts to maybe even think about
moral norms
well i i don't feel uh discussed
personally to our
our predecessors if anything uh i feel
uh admiration for all living creatures
because
step back and consider that every every
creature that's ever lived has an
unbroken chain of successful
reproduction
going back to the beginnings of life on
earth that is an absolutely staggering
thing
you know you you the the the bug that
you swat
that you know that that got in your way
that
entity has an unbroken chain of
successful reproduction going back to
the beginnings of life on earth
that particular bug now if that doesn't
give you some like
feeling of reverence for your fellow
creatures i don't know what will
so if i think back to our our our
ancestors
you know those guys had a lot to deal
with you know the primates there's a lot
going for them but there's also a lot
not going for them uh it's hard to be a
primate
and uh the they somehow made it through
and then they somehow
got to the point of being able to
develop language and so i i feel like
um kind of sympathy for them in their
pre-language days
uh not discussed and and i think that
the way that
we're going to think about the who we
are today
you know when we go down a few
generations
and think back to this is again it's
going to be more of a sympathy thing is
that
there are things we don't know today
that are going to become clear
and in hindsight it's going to be you
know they didn't really know
all of these things and uh we'll give
them a pass
because they weren't they just didn't
know and
the you know the whether or not this
kind of a consistent revolution of of
of continual reveals keeps going into
the future
where every time you kind of step up
as a thing something true is revealed
about the nature of the universe that
you didn't know before
whether that continues forever i doubt
it
there's probably some limit to knowledge
that you can have
and at some point you become as
omniscient
as an entity can become uh and then
there is nothing else
you you've reached some kind of
intellectual nirvana
where you kind of comprehended
everything there is to know about
everything
uh but we're so far from that now that
uh
it's kind of just a kind of an
intellectual exercise to think that far
in the future
but in terms of like our ai transition
that we're about to go through
the the one thing that we're going to
need to have as a species to make it
through this is empathy
is that we need to we need to have these
ideas of circles of empathy
so you and everyone else cares more
about the people around
you than everything else you don't care
about the bug
you don't care about even if you say you
do you don't care about people in
minnesota
unless you live there or you have
relatives there it's just a fact of
human nature that we care more about our
children
and our spouses and our families than we
do about the rest of the world
and right now machines are in virtually
no one's circle of empathy
nobody cares we don't consider them to
have any
uh status i suppose in the conversation
about uh what it means to empathize
and that's has to change at some point
when
these machines start being a little bit
more like us and then a lot like us and
then maybe even more than us
the empathy that we need to develop for
them
as you know fellow pieces of bundles of
matter that move through the world
uh is gonna have to grow and it's gonna
have to grow not for altruistic reasons
but for survival reasons is it in in a
in a in a landscape where you have
thousands of intelligent things
wandering around all with different
goals
the one thing that's going to make
everything work is
figuring out how to work together and
the the
you have to give up these ideas that
you're going to have control because you
don't even know
no matter who you are i don't care who
you are every single human in the world
does not
have freedom in the sense of being able
to do whatever the hell
you like you live within a set of rules
and those rules you can think of as
losing control and you do it for a
reason it's because it's better for you
to be within these these bounds it's
better for you personally to be within
these bounds no matter who you are
so the the this idea of circles of
empathy is something that i it keeps
coming back to me in terms of what how
we have to think about the world is
empathize with your fellow matter it
doesn't have to just be things like
you know dogs and cats and squirrels and
your kids
the the world around you is this very
complex mysterious thing
somehow we have a property that most of
the matter around us doesn't
but that could change when it does the
world is going to become a very strange
place in order for us to find our place
and that we have to
we have to rely on the angels of our
better nature and not our
the demons of our evolutionary ancestry
uh in order to help guide us through it
i wonder it's too in that respect i mean
it really seems like the
the um embodied cognition strategy seems
like it would be important to the extent
that humans are
i imagine we'll have a harder time like
developing empathy for
gpt 5 than for some physically embodied
system
that like we can at least relate to
because it has a physical form
um is that part of the calculation there
as well
yeah well again going to this analogy of
an alien
intelligence so let's say you know the
aliens from zebulon 5 come down and
they're like clouds of gas
can we have empathy for them if so why i
mean they don't have bodies like we do
what makes it so that we would think
that they were like us
so and now ask the question of gpt3
running around inside the uh you know
the voltage potentials of a bunch of
computers
what is different about the alien from
zebulon5 and these voltages running
around in a computer why do you feel
differently about those two things
and so i think a lot of this has to do
with the mental models we have
what it means to be a person or a thing
you know we have
we have no problem again our
evolutionary
history tells us why we have no problem
ascribing personhood to a dog
because we've lived with dogs for
thousands of years they've been our
companions going back
you know to the dawn of civilization and
even before
the uh the the the fact that we would
have trouble ascribing personhood to
something as abstract
as a as a computer program is more a
failing
of our imagination than it is about some
fact about the world
so the the um you know the future will
have a lot of things like that
call them alien intelligences that we
wouldn't even think
usually about ascribing anything related
to what we think of intelligence too but
maybe we should
and there will be things that are
embodied like little robots
wandering around talking to us doing
things um
and the the whether we ascribe
personhood to them is a big challenge
and i think a matter of
again it's in this gray area you know if
your driverless car can take you from
one place to another
uh is it really like us
maybe sort of but probably not
but now let's say it starts talking to
me
and i have conversations with it is that
what we mean
where is the line like what is the
property where we say okay now this
thing is over the line
i have to start thinking about it like a
fellow being is it the ability to feel
pain or discomfort what does that even
mean
because you know we live inside our
brains right
all pain and discomfort is is a bunch of
electrical signals traveling on your
nerves to this
wet meaty thing inside your skull so
presumably you could ascribe the same
qualia or you know perspective
to an ai if it was sufficiently like us
to say this ai is in pain or suffering
or somehow
is not doing well should we feel
sympathy or empathy i think the answer
is yes and again
why it's because if we have what we
think is a moral compass
it doesn't only apply to us that is a
very toxic idea and it's at the root of
all of the worst ideas in human history
you mentioned it before this idea of the
other
it's very easy to say i'm okay
i'm fully human but you over there who
don't look like me
you're not fully human you don't have
rights i don't care how much you're
suffering because you're not you're not
like me
this this this deep seated
thing that we seem to have which we've
overcome to a certain extent but not all
the way
is not just about say racism or sexism
that's one manifestation of the
hatred and fear of the other but it's
not the only
thing and in some not too far away
future
uh we're going to have to start thinking
very deeply
about what it means to really be a moral
and ethical person
or entity it's not just about things
that look like you
it's about a much broader uh
circle so what's in the circle and i
don't know
and i don't think that the circle is a
bright sharp line it's gray
certain things you know what are clearly
in the circle
certain things maybe are clearly outside
but the middle is a very difficult thing
and it moves over time
you know we wouldn't have been having
this conversation 500 years ago if we
were
burned at the stake uh but it you know
now maybe it's not
not just a bunch of um you know talk
over beers
now we're talking about being able to
potentially build things like this and
it's a real conversation about the
future of
what uh what a good and fair society
looks like
not just for us but for for all
entities that should be inside the
circle do you think that we need
a theory of consciousness that's
reliable that we have confidence in
before we can get to that point
or can we do it more empirically uh no i
think you i think that's something that
would be part of this is that you'd say
you know okay so we think of the world
and we
we are our thing in the world in a
particular way maybe we decide some
moral philosopher thinks
that particular thing is is necessary in
order to have the properties that we
want to ascribe to
you know the things inside the circle so
you have to have
certain properties that are reflective
of this idea that we have an internal
model of a certain sort
so presumably if you're good enough to
be able to build one of those things
you understand it well enough you could
ascribe a measure
to uh anything like a dog or a cup or
whatever and you could literally go in
and take your consciousness meter and
apply it to that thing and say
okay this score is 0.3 that's not enough
because it needs to be one or else it's
not
it's not a person now maybe that's a way
that you could do it
i think in the short term uh it's
unlikely that anyone would accept any
prem
any measurement that you proposed no
matter how well-founded it was
so if i came out tomorrow and i said
okay here's how we measure consciousness
we do blah blah blah blah blah blah and
here's the answer
no matter how potentially true it was or
defensible
no one is even going to pay attention
for one thing because
you know the what difference does it
make this is just some weird thing
just like a bunch of other weird things
you know most people who study
consciousness
i think again are well-meaning but
it's it's the sort of thing that's
really hard to convince somebody that
you've got a good answer no matter what
it is
uh at some point maybe you'll have this
idea that there are certain things that
are
that are have it and some that don't i'm
i'm more i suppose like
intuitively pan psychic when it comes to
this
the idea that the these things every
every property of the mind
that we have names for are uh exist on
a spectrum in many dimensions they're
not a fixed
yes or no you have a little bit or a lot
of it in a lot of different ways
uh whether you're an oak tree or a human
you you possess
every single property that we think of
in terms of
uh you know the the various words that
we use to describe the internal
experience
but you have them maybe in different
amounts and potentially in different
dimensions uh and by the way that
doesn't make us greater than
it makes us different then and this this
idea that the
biological creatures are different than
and not greater than is a
really key idea as we transition into
this
next phase where we now have this
this new form of life like machine life
didn't exist before now it will
um the the idea that
we're not necessarily the best at
everything but that doesn't mean that
we're less than
so we can make something that can can
calculate the product of two
integers way faster than any human can
does that mean we're
less than no just means we're different
we just don't we can't compute
products of integers as fast as a
computer can
uh we're better at other things and even
if we weren't who cares
if there were machines that were better
than us at everything it doesn't change
who we are
it's kind of like that cucumber grape
experiment it's like you know if you
give
if you give the monkey the the cucumber
oh he's happy
but if you give the other monkey the
grape he's pissed because the other
monkey got the grape and why'd you give
me the cucumber
so i think that that the this idea that
you can be content with what you have
even if you're not the best at
everything is a very important idea you
know like i could never
play in the nba you know i just can't
there's no way i ever could i could have
i could have trained every minute
of every day of my life and i would
never have been able to do that
there are humans on the planet who are
just better than me at basketball
and if you go down at every single human
endeavor
every single one there's somebody alive
today that's better than me at all
every single one does that make me feel
worse about my life
no and it should when we're talking
about machines
in some future where there's a machine
that's better than you and everything
it's no different than now
you're already not the best at
everything you do and so this
this idea that the the being better then
is somehow like
an objective is ridiculous it goes back
to this earlier thing about like why are
we here what's our purpose and all
that so i maybe have gone through an
evolution am i thinking about this as
i've gotten older
but being better at everything is not
the purpose of your life
it's just not because you're going to
fail
if the purpose of your life was to
always win at everything
your life is a failure by definition if
you want to be the olympic gold medalist
in freestyle wrestling at 82 kilos which
i did for a long time
the fact that i didn't achieve that and
i never will
if that was the defining element of my
life means that
everything that i've done in my life is
a failure just because i'm not the best
but that's the wrong way to look at
things like the the
the path through your life has ups and
downs
you're gonna have things that you view
as successes and things that you view as
failures
all these things are relative they're
not absolute and you can do
all of the things that you've ever done
in the background
of having a bunch of intelligent
machines running around nothing is
stopping you from still doing what
you're doing
even if there's a machine that's better
than cleaning your carpet than you are
it doesn't matter in fact the machine
being better at cleaning your carpet
than you are
frees you up to not have to worry about
cleaning your carpet
or you know if the the robot dentist is
better at keeping your teeth healthy
than the human dentist
the human dentist doesn't have that work
anymore but by god you've got great
teeth
and in the in the main what's more
important
even if you're a dentist you could do
something else
you know there there's so there's an
enormous infinite number of things that
you can do
that don't necessarily boil down to
who's the best at it in fact there's
everything is like this
um that i i don't have any uh concern at
all for the the future in terms of like
work replacement things being better
than us i mean let's just
accept that it's going to happen and
figure out how to make
the best possible outcome and and
there's a ton of very very
cool outcomes that could come of this
well it's a fascinating topic and a very
exciting vision for the future that you
have and i'm i'm glad you you
stopped by to share it we're going to
have to do this again there's just too
much to talk about
uh but thank you so much jordy for
making the time i really appreciate it
well thanks it was it was fun uh have me
on again and i'll go on for another hour
i'm i'm sure we will yeah i'm sure we
will actually is there do you want to
share any links i know sanctuary is sort
of uh
sort of secretive right now but are
there any links that you want or that
you can share
for that or any of your other work uh
well so we we've
got gone to great lengths to not have
anything
i did uh
anything that you can find about
sanctuary is not what we do uh
at least anymore so the the you know
there isn't any
um the i'm not hard to find myself
personally
um if anybody wants to get a hold of me
uh there's a lot of people especially in
the
the um the machine learning community
who know what i get a hold of me so just
ask somebody if you want my email or
whatever and uh
i'll respond perfect and i'll share your
social media as well i think you're
you're on twitter right i
am yeah it's for my my uh
the only reason i'm on twitter is to
follow ashley vance who's absolutely
hilarious
uh there's really no reason
i my outlet for watching
you know muppet videos and and cat
memes that's that's probably mentally
healthier than the reason most people
are on twitter these days so uh you're
definitely to be commended on that great
well thanks so much shorty