The following is a conversation with Gavin Miller, he's the head of Adobe Research.
Adobe has empowered artists, designers, and creative minds from all professions,
working in the digital medium for over 30 years with software such as Photoshop, Illustrator,
Premiere, After Effects, InDesign, Audition, Software that work with images, video, and audio.
Adobe Research is working to define the future evolution of these products in a way
that makes the life of creatives easier, automates the tedious tasks, and gives more and more time
to operate in the idea space instead of pixel space. This is where the cutting edge, deep
learning methods of the past decade can really shine more than perhaps any other application.
Gavin is the embodiment of combining tech and creativity. Outside of Adobe Research,
he writes poetry and builds robots, both things that are near and dear to my heart as well.
This conversation is part of the Artificial Intelligence Podcast. If you enjoy it, subscribe
on YouTube, iTunes, or simply connect with me on Twitter at Lex Freedman's spelled F-R-I-D.
And now, here's my conversation with Gavin Miller.
You're head of Adobe Research, leading a lot of innovative efforts and applications of AI,
creating images, video, audio, language, but you're also yourself an artist,
a poet, a writer, and even a roboticist. So while I promise to everyone listening,
that I will not spend the entire time we have together reading your poetry, which I love,
I have to sprinkle it in at least a little bit. So some of them are pretty deep and profound,
and some are light and silly. Let's start with a few lines from the silly variety.
You write in Je ne v'n'écrète rien, a poem that beautifully parodies both.
I did piazz, Je n'écrète rien, and my web at Frank Sinatra. So it opens with,
and now dessert is near. It's time to pay the final total. I've tried to slim all year,
but my diets have been anecdotal. So where does that love for poetry come from for you?
And if we dissect your mind, how does it all fit together in the bigger puzzle of Dr. Gavin Miller?
Well, interesting you chose that one. That was a poem I wrote when I'd been to my doctor,
and he said, you really need to lose some weight and go on a diet. And whilst the rational part
of my brain wanted to do that, the irrational part of my brain was protesting and sort of embraced
the opposite idea. I regret nothing, hence. Yes, exactly. Taken to an extreme, I thought it would
be funny. Obviously, it's a serious topic for some people. But I think for me, I've always been
interested in writing since I was in high school, as well as doing technology and invention.
And sometimes there are parallel strands in your life that carry on, and one is more about
your private life, and one's more about your technological career. And then at sort of happy
moments along the way, sometimes the two things touch, one idea informs the other. And we can
talk about that as we go. Do you think you're writing the art, the poetry, contribute indirectly
or directly to your research, to your work in Adobe? Well, sometimes it does if I, say, imagine
a future in a science fiction kind of way. And then once it exists on paper, I think, well, why
shouldn't I just build that? There was an example where when realistic voice synthesis first started
in the 90s at Apple, where I worked in research. I was done by a friend of mine. I sort of sat down
and started writing a poem which each line I would enter into the voice synthesizer and see how it
sounded and sort of wrote it for that voice. And at the time, the agents weren't very sophisticated.
So they'd sort of add random intonation. And I kind of made up the poem to sort of match the tone
of the voice. And it sounded slightly sad and depressed. So I pretended it was a poem written
by an intelligent agent, sort of telling the user to go home and leave them alone. But at the same
time, they were lonely and wanted to have company and learn from what the user was saying. And at
the time, it was way beyond anything that AI could possibly do. But since then, it's becoming more
within the bounds of possibility. And then at the same time, I had a project at home where I did
sort of a smart home. This was probably 93, 94. And I had the talking voice who'd remind me when
I walked in the door of what things I had to do. I had buttons on my washing machine because I was
a bachelor and I'd leave the clothes in there for three days and they'd go moldy. So as I got up in
the morning and say, don't forget the washing and so on, I made photographic photo albums that
use light sensors to know which page you were looking at would send that over wireless radio
to the agent who would then play sounds that match the image you were looking at in the book.
So I was kind of in love with this idea of magical realism and whether it was possible to do that
with technology. So that was a case where the sort of the agent sort of intrigued me from a literary
point of view and became a personality. I think more recently, I've also written plays and when
plays you write dialogue and obviously you write a fixed set of dialogue that follows a linear
narrative. But with modern agents as you design a personality or a capability for conversation,
you're sort of thinking of, I kind of have imaginary dialogue in my head. And then I think,
what would it take not only to have that be real, but for it to really know what it's talking about.
So it's easy to fall into the uncanny valley with AI where it says something it doesn't really
understand, but it sounds good to the person. But you rapidly realize that it's kind of just
stimulus response. It doesn't really have real world knowledge about the thing it's describing.
And so when you get to that point, it really needs to have multiple ways of talking about
the same concept. So it sounds as though it really understands it. Now, what really understanding
means is in the eye of the beholder, right? But if it only has one way of referring to something,
it feels like it's a canned response. But if it can reason about it or you can go at it from
multiple angles and give a similar kind of response that people would then it starts to
seem more like there's something there that's sentient.
You can say the same thing multiple things from different perspectives. I mean, with the
automatic image captioning that I've seen the work that you're doing, there's elements of that,
right? Being able to generate different kinds of right. So one in my team, there's a lot of work
on turning a medium from one form to another, whether it's auto-tagging imagery or making up
full sentences about what's in the image, then changing the sentence, finding another image
that matches the new sentence or vice versa. And in the modern world of GANs, you sort of
give it a description and it synthesizes an asset that matches the description. So
I've sort of gone on a journey. My early days in my career were about 3D computer graphics,
the sort of pioneering work sort of before movies had special effects done with 3D graphics,
and sort of rode that revolution. And that was very much like the renaissance where people
would model light and color and shape and everything. And now we're kind of in another
wave where it's more impressionistic and it's sort of the idea of something can be used to
generate an image directly, which is sort of the new frontier in computer image generation using
AI algorithms. So the creative process is more in the space of ideas or becoming more in the space
of ideas versus in the raw pixels? Well, it's interesting. It depends. I think at Adobe,
we really want to span the entire range from really, really good, what you might call low-level
tools by low-level as close to, say, analog workflows as possible. So what we do there is
we make up systems that do really realistic oil paint and watercolor simulation. So if you want
every bristle to behave as it would in the real world and leave a beautiful analog trail of water
and then flow after you've made the brushstroke, you can do that. And that's really important for
people who want to create something really expressive or really novel because they have
complete control. And then a certain other tasks become automated. It frees the artists up to focus
on the inspiration unless of the perspiration. So thinking about different ideas, obviously,
once you finish the design, there's a lot of work to, say, do it for all the different
aspect ratio of phones or websites and so on. And that used to take up an awful lot of time for
artists. It still does for many, what we call content velocity. And one of the targets of AI
is actually to reason about, from the first example of what are the likely intent for these
other formats, maybe if you change the language to German and the words are longer, how do you
reflow everything so that it looks nicely artistic in that way? And so the person can focus on the
really creative bit in the middle, which is what is the look and style and feel and what's the message
and what's the story and the human element. So I think creativity is changing. So that's one way
in which we're trying to just make it easier and faster and cheaper to do so that there can be more
of it, more demand, because it's less expensive. So everyone wants beautiful artwork for everything
from a school website to Hollywood movie. On the other side, as some of these things have
automatic versions of them, people will possibly change role from being the hands-on artist and
to being either the art director or the conceptual artist. And then the computer will be a partner
to help create polished examples of the idea that they're exploring. Let's talk about Adobe products
versus AI and Adobe products. Just so you know where I'm coming from, I'm a huge fan of Photoshop
for images, Premiere for video, Audition for audio. I'll probably use Photoshop to create the thumbnail
for this video, Premiere to edit the video, Audition to do the audio. That said, everything I do is
really manually and I set up, I use this old school Kinesis keyboard and I have auto hotkey
that just, it's really about optimizing the flow of just making sure there's as few clicks as possible.
So just being extremely efficient. It's something you started to speak to. So before we get into the
fun sort of awesome deep learning things, where does AI, if you could speak a little more to it,
AI or just automation in general, do you see in the coming months and years or in general
prior in 2018 fitting in to making the life, the low level pixel work flow easier?
Yeah, that's a great question. So we have a very rich array of algorithms already in Photoshop,
just classical procedural algorithms as well as ones based on data. In some cases, they end up
with a large number of sliders and degrees of freedom. So one way in which AI can help is just
an auto button which comes up with default settings based on the content itself rather than
default values for the tool. At that point, you then start tweaking. So that's a very kind of
make life easier for people whilst making use of common sense from other example images.
So like smart defaults.
Smart defaults, absolutely. Another one is something we've spent a lot of work over the last 20 years
I've been at Adobe 19 thinking about selection, for instance, where with the quick select you
would look at color boundaries and figure out how to sort of flood fill into regions that you
thought were physically connected in the real world. But that algorithm had no visual common
sense about what a cat looks like or a dog. It would just do it based on rules of thumb which
were applied to graph theory. And it was a big improvement over the previous work we had sort
of almost click everything by hand or if it just did similar colors, it would do little tiny regions
that wouldn't be connected. But in the future, using neural nets to actually do a great job
with say a single click or even in the case of well-known categories like people or animals,
no click, where you just say select the object and it just knows the dominant object is a person
in the middle of the photograph. Those kinds of things are really valuable if they can be robust
enough to give you good quality results or they can be a great start for like tweaking it.
So for example, background removal, one thing I'll in a thumbnail, I'll take a picture of you
right now and essentially remove the background behind you. And I want to make that as easy as
possible. You don't have flowing hair. At the moment, yes. Rich sort of.
How did in the past it may come again in the future but for now.
So that sometimes makes it a little more challenging to remove the background. How
difficult do you think is that problem for AI for basically making the quick selection tool
smarter and smarter and smarter? Well, we have a lot of research on that already.
If you want a sort of quick, cheap and cheerful, look, I'm pretending I'm in Hawaii,
but it's sort of a joke, then you don't need perfect boundaries. And you can do that today
with a single click for the algorithms we have. We have other algorithms where with a little bit
more guidance on the boundaries, like you might need to touch it up a little bit.
We have other algorithms that can pull a nice mat from a crude selection. So we have combinations
of tools that can do all of that. And at our recent Max conference at AB Max, we demonstrated how
very quickly just by drawing a simple polygon around the object of interest, we could not
only do it for a single still, but we could pull at least a selection mask from a moving target,
like a person dancing in front of a brick wall or something. And so it's going from hours to
a few seconds for workflows that are really nice. And then you might go in and touch up a little.
So that's a really interesting question. You mentioned the word robust.
You know, there's like a journey for an idea, right? And what you presented probably at Max
has elements of just sort of it inspires the concept. It can work pretty well in a majority
of cases. But how do you make something that works? Well, in majority of cases, how do you make
something that works maybe in all cases? Or it becomes a robust tool?
There are a couple of things. So that really touches on the difference between academic
research and industrial research. So in academic research, it's really about who's the person to
have the great new idea that shows promise. And we certainly love to be those people too.
But we have sort of two forms of publishing. One is academic peer review, which we do a lot of,
and we have great success there as much as some universities. But then we also have shipping,
which is a different type of and then we get customer review, as well as, you know, product
critics. And that might be a case where it's not about being perfect every single time,
but perfect enough at the time, plus a mechanism to intervene and recover where you do have mistakes.
So we have the luxury of very talented customers. We don't want them to be
overly taxed doing it every time. But if they can go in and just take it from 99 to 100
with the touch of a mouse or something, then for the professional end, that's something that we
definitely want to support as well. And for them, it went from having to do that tedious task all
the time to much less often. So I think that gives us an out. If it had to be 100% automatic all the
time, then that would delay the time at which we could get to market. So on that thread, maybe you
can untangle something. Again, I'm sort of just speaking to my own experience. Maybe that is the
most useful idea. So I think Photoshop, as an example or premiere, has a lot of amazing features
that I haven't touched. And so what's the, in terms of AI, helping make my life or the
life of creatives easier? How this collaboration between human and machine, how do you learn to
collaborate better? How do you learn the new algorithms? Is it something that where you have
to watch tutorials and you have to watch videos and so on? Or do you ever think, do you think
about the experience itself through exploration being the teacher?
We absolutely do. So I'm glad that you brought this up. We sort of think about two things.
One is helping the person in the moment to do the task that they need to do. But the other is
thinking more holistically about their journey, learning a tool. And when it's like, think of it
as Adobe University, where you use the tool long enough, you become an expert. And not necessarily
an expert in everything. It's like living in a city. You don't necessarily know every street,
but you know the important ones you need to get to. So we have projects in research,
which actually look at the thousands of hours of tutorials online and try to understand
what's being taught in them. And then we had one publication at CHI, where it was looking at,
given the last three or four actions you did, what did other people in tutorials do next?
So if you want some inspiration for what you might do next, or you just want to watch the
tutorial and see, learn from people who are doing similar workflows to you, you can without
having to go and search on keywords and everything. So really trying to use the
context of your use of the app to make intelligent suggestions, either about choices that you might
make, or in a more assistive way, where it could say, if you did this next, we could show you.
And that's basically the frontier that we're exploring now, which is, if we really deeply
understand the domain in which designers and creative people work, can we combine that with AI
and pattern matching of behavior to make intelligent suggestions, either through
verbal possibilities, or just showing the results of if you try this. And that's really the sort of,
I was in a meeting today thinking about these things.
So it's still a grand challenge. We'd all love an artist over one shoulder and a teacher over
the other. And we hope to get there. And the right thing to do is to give enough at each
stage that it's useful in itself, but it builds a foundation for the next level of expectation.
Are you aware of this gigantic medium of YouTube that's creating just a bunch of creative people,
both artists and teachers of different kinds? Absolutely. And the more we can understand
those media types, both visually and in terms of transcripts and words, the more we can bring
the wisdom that they embody into the guidance that's embedded in the tool.
That would be brilliant to remove the barrier from having to yourself type in the keyword,
searching, so on. Absolutely. And then in the longer term,
an interesting discussion is, does it ultimately not just assist with learning the interface we
have, but does it modify the interface to be simpler? Or do you fragment into a variety of
tools, each of which has a different level of visibility of the functionality? I like to say
that if you add a feature to a GUI, you have to have yet more visual complexity confronting the
new user. Whereas if you have an assistant with a new skill, if you know they have it,
so to ask for it, then it's sort of additive without being more intimidating. So we definitely
think about new users and how to onboard them. Many actually value the idea of being able to
master that complex interface and keyboard shortcuts like you were talking about earlier,
because with great familiarity, it becomes a musical instrument for expressing your visual
ideas. And other people just want to get something done quickly in the simplest way possible. And
that's where a more assistive version of the same technology might be useful, maybe on a different
class of device, which is more in context for capture, say, whereas somebody who's in a deep
post-production workflow maybe want to be on a laptop or a big screen desktop and have more
knobs and dials to really express the subtlety of what they want to do.
So there's so many exciting applications of computer vision and machine learning that Adobe
is working on, like scene stitching, sky replacement, foreground, background removal, spatial object
based image search, automatic image captioning, like we mentioned, project cloak, project deep fill
filling in parts of the images, project scribbler, style transfer video, style transfer faces and
video with Project Puppetron, best name ever. Can you talk through a favorite or some of them
or examples that popped in mind? I'm sure I'll be able to provide links to other ones we don't
talk about, because there's visual elements to all of them that are exciting.
Why they're interesting for different reasons might be a good way to go. So I think sky replace
is interesting because we talked about selection being sort of an atomic operation. It's almost
if you think of an assembly language, it's like a single instruction. Whereas sky replace is a
compound action where you automatically select the sky, you look for stock content that matches
the geometry of the scene, you try to have variety in your choices so that you do coverage of different
moods. It then mats in the sky behind the foreground, but then importantly it uses the
foreground of the other image that you just searched on to recolor the foreground of the
image that you're editing. So if you say go from a midday sky to an evening sky, it will actually
add sort of an orange glow to the foreground objects as well. I was a big fan in College of
Magritte and he has a number of paintings where it's surrealism because he'll do a composite,
but the foreground building will be at night and the sky will be during the day. There's one
called The Empire of Light which was on my wall in college and we're trying not to do surrealism.
It can be a choice, but we'd rather have it be natural by default rather than it looking
fake and then you have to do a whole bunch of post-production to fix it. So that's a case where
we're kind of capturing an entire workflow into a single action and doing it in about a second
rather than a minute or two. And when you do that, you can not just do it once, but you can do it
for say like 10 different backgrounds and then you're almost back to this inspiration idea of
I don't know quite what I want, but I'll know it when I see it and you can just explore the design
space as close to final production value as possible. And then when you really pick one,
you might go back and slightly tweak the selection mask just to make it perfect and
do that kind of polish that professionals like to bring to their work.
So then there's this idea of, as you mentioned, the sky replacing it to different stock images
of the sky. In general, you have this idea of- Or it could be on your disk or whatever.
Disk right. But making even more intelligent choices about ways to search stock images,
which is really interesting. It's kind of spatial. Absolutely.
Being all the specialists. Right. So that was something we called concept canvas.
So normally when you do say an image search, I assume it's just based on text. You would
give the keywords of the things you want to be in the image and it would find the nearest one that
had those tags. For many tasks, you really want to be able to say, I want a big person in the
middle or in a dog to the right and umbrella above the left because you want to leave space for the
text or whatever. And so concept canvas lets you assign spatial regions to the keywords.
And then we've already pre-indexed the images to know where the important concepts are in the
picture. So we then go through that index matching to assets. And even though it's just another
form of search, because you're doing spatial design or layout, it starts to feel like design.
You sort of feel oddly responsible for the image that comes back as if you invented it a little.
It's a good example where giving enough control starts to make people have a sense of ownership
over the outcome of the event. And then we also have technologies in Photoshop where you physically
can move the dog in post as well. But for concept canvas, it was just a very fast way to sort of
loop through and be able to lay things out. And in terms of being able to remove objects
from a scene and fill in the background automatically. So that's extremely exciting.
And that's a neural network to step in there. I just talked this week, Ian Goodfellow.
So the Gans for doing that is definitely one approach. So is that a really difficult problem?
Is it as difficult as it looks, again, to take it to a robust product level?
Well, there are certain classes of image for which the traditional algorithms like content
aware fill work really well. Like if you have a naturalistic texture like a gravel path or
something, because it's patch based, it will make up a very plausible looking intermediate thing and
fill in the hole. And then we use some algorithms to sort of smooth out the lighting so you don't
see any brightness contrast in that region. Or you've gradually ramped from one from dark to
light if it straddles a boundary. Where it gets complicated is if you have to infer invisible
structure behind the person in front. And that really requires a common sense knowledge of the
world to know what, you know, if I see three quarters of a house, do I have a rough sense of
what the rest of the house looks like? If you just fill it in with patches, it can end up sort of
doing things that make sense locally. But you look at the global structure and it looks like it's
just sort of crumpled or messed up. And so what GANs and neural nets bring to the table is this
common sense learned from the training set. And the challenge right now is that the
generative methods that can make up missing holes using that kind of technology are still only stable
at low resolutions. And so you either need to then go from a low resolution to a high resolution
using some other algorithm, or we need to push the state of the art and it's still in research to
get to that point. Of course, if you show it something, say it's trained on houses and then
you show it an octopus, it's not going to do a very good job of showing common sense about
octopuses. So again, you're asking about how you know that it's ready for prime time. You really
need a very diverse training set of images. And ultimately, that may be a case where you put it
out there with some guard rails where you might do a detector which looks at the image and sort of
estimates its own competence of how well a job could this algorithm do. So eventually,
there may be this idea of what we call an ensemble of experts where any particular
expert is specialized in certain things and then there's either they vote to say how confident
they are about what to do. This is sort of more future looking or there's some dispatcher which
says you're good at houses, you're good at trees. So I mean, all this adds up to a lot of work
because each of those models will be a whole bunch of work. But I think over time, you'd gradually
fill out the set and initially focus on certain workflows and then sort of branch out as you
get more capable. You mentioned workflows and have you considered maybe looking far into the future?
First of all, using the fact that there is a huge amount of people that use Photoshop, for example,
and have certain workflows, being able to collect the information by which they basically get
information about their workflows, about what they need, the ways to help them, whether it is
houses or octopus that people work on more. Like basically getting a beat on what kind of
data is needed to be annotated and collected for people to build tools that actually work well
for people. Absolutely. And this is a big topic and the whole world of AI is what data can you
gather and why. At one level, the way to think about it is we not only want to train our customers
in how to use our products, but we want them to teach us what's important and what's useful.
At the same time, we want to respect their privacy. And obviously, we wouldn't do things
without their explicit permission. And I think the modern spirit of the age around this is you
have to demonstrate to somebody how they're benefiting from sharing their data with the tool.
Either it's helping in the short term to understand their intent so you can make better
recommendations. Or if they're friendly to your cause or your tool or they want to help you evolve
quickly because they depend on you for their livelihood, they may be willing to share some
of their workflows or choices with the dataset to be then trained. There are technologies for
looking at learning without necessarily storing all the information permanently so that you can
sort of learn on the fly but not keep a record of what somebody did. So we're definitely exploring
all of those possibilities. And I think Adobe exists in a space where Photoshop, if I look at
the data I've created and own, I'm less comfortable sharing data with social networks than I am with
Adobe because just exactly as you said, there's an obvious benefit for sharing the data that I use
to create in Photoshop because it's helping improve the workflow in the future as opposed to
it's not clear what the benefit is in social networks. It's nice of you to say that. I mean,
I think there are some professional workflows where people might be very protective of what
they're doing such as if I was preparing evidence for a legal case, I wouldn't want any of that
phoning home to help train the algorithm or anything. There may be other cases where people
are, say, having a trial version or they're doing some, I'm not saying we're doing this today, but
that's a future scenario where somebody has a more permissive relationship with Adobe where
they explicitly say, I'm fine, I'm only doing hobby projects or things which are non-confidential
and in exchange for some benefit, tangible or otherwise, I'm willing to share very fine-grain
data. Another possible scenario is to capture relatively crude high-level things from more
people and then more detailed knowledge from people who are willing to participate. We do that
today with explicit customer studies where we go and visit somebody and ask them to try the tool
and we human observe what they're doing. In the future, to be able to do that enough to be able
to train an algorithm, we'd need a more systematic process, but we'd have to do it very consciously
because one of the things people treasure about Adobe is a sense of trust and we don't want to
endanger that through overly aggressive data collection. We have a chief privacy officer
and it's definitely front and center of thinking about AI rather than an afterthought.
Well, when you start that program, sign me up.
Okay, happy to.
Is there other projects that you wanted to mention that I didn't perhaps that pop into mind?
Well, you covered the number. I think you mentioned Project Puppetron. I think that one is interesting
because you might think of Adobe as only thinking in 2D and that's a good example where we're
actually thinking more three-dimensionally about how to assign features to faces so that
we can, you know, if you take, so what Puppetron does, it takes either a still or a video of a
person talking and then it can take a painting of somebody else and then apply the style of the
painting to the person who's talking in the video. And it's unlike a sort of screen door
post-filter effect that you sometimes see online. It really looks as though it's
sort of somehow attached or reflecting the motion of the face. And so that's a case where even to do
a 2D workflow like stylization, you really need to infer more about the 3D structure of the world.
And I think as 3D computer vision algorithms get better, initially they'll focus on particular
domains like faces where you have a lot of prior knowledge about structure and you can maybe have
a parameterized template that you fit to the image. But over time, this should be possible for more
general content. And it might even be invisible to the user that you're doing 3D reconstruction
under the hood, but it might then let you do edits much more reliably or correctly than you would
otherwise. And, you know, the face is a very important application, right? Absolutely.
So making things work. And a very sensitive one. If you do something uncanny, it's very disturbing.
That's right. You have to get it. You have to get it right. So in the space of augmented
reality and virtual reality, what do you think is the role of AR and VR in the content we consume
as P-Bus consumers and the content we create as creators today?
No, that's a great question. Let me think about this a lot too. So I think VR and AR serve
slightly different purposes. So VR can really transport you to an entire immersive world,
no matter what your personal situation is. To that extent, it's a bit like a really,
really widescreen television, where it sort of snaps you out of your context and puts you in a
new one. And I think it's still evolving in terms of the hardware I actually worked on,
VR in the 90s, trying to solve the latency and sort of nausea problem, which we did,
but it was very expensive and a bit early. There's a new wave of that now, I think,
and increasingly those devices are becoming all in one rather than something that's tethered to a
box. I think the market seems to be bifurcating into things for consumers and things for professional
use cases, like for architects and people designing where your product is a building,
and you really want to experience it better than looking at a scale model or a drawing,
I think, or even than a video. So I think for that, where you need a sense of scale
and spatial relationships, it's great. I think AR holds the promise of sort of taking
digital assets off the screen and putting them in context in the real world on the table in
front of you, on the wall behind you. And that has the corresponding need that the assets need
to adapt to the physical context in which they're being placed. I mean, it's a bit like having a
live theater troupe come to your house and put on Hamlet. My mother had a friend who used to do
this at Stately Homes in England for the National Trust, and they would adapt the scenes and even
they'd walk the audience through the rooms to see the action based on the country house they
found themselves in for two days. And I think AR will have the same issue that if you have a tiny
table and a big living room or something, it'll try to figure out what can you change and what's
fixed. And there's a little bit of a tension between fidelity, where if you captured Sanyurayov
doing a fantastic ballet, you'd want it to be sort of exactly reproduced and maybe all you could
do is scale it down. Whereas somebody telling you a story might be walking around the room doing
some gestures, and that could adapt to the room in which they were telling the story.
And do you think fidelity is that important in that space, or is it more about the storytelling?
I think it may depend on the characteristic of the media. If it's a famous celebrity,
then it may be that you want to catch every nuance and they don't want to be reanimated by
some algorithm. It could be that if it's really, you know, a lovable frog telling you a story,
and it's about a princess and a frog, then it doesn't matter if the frog moves in a different
way. I think a lot of the ideas that have sort of grown up in the game world will
now come into the broader commercial sphere once they're needing adaptive characters in AR.
Are you thinking of engineering tools that allow creators to create in the augmented world,
basically making a Photoshop for the augmented world?
Well, we have shown a few demos of sort of taking a Photoshop layer stack and then expanding it
into 3D. That's actually been shown publicly as one example in AR. Where we're particularly
excited at the moment is in 3D. 3D design is still a very challenging space. And we believe that
it's a worthwhile experiment to try to figure out if AR or immersive makes 3D design more
spontaneous. Can you give me an example of 3D design, just applications?
Well, literally a simple one would be laying out objects, right? So on a conventional screen,
you'd sort of have a plan view and a side view and a perspective view, and you'd sort of be
dragging it around with the mouse. And if you're not careful, it would go through the wall and all
that. Whereas if you were really laying out objects, say in a VR headset, you could literally move
your head to see a different viewpoint. They'd be in stereo, so you'd have a sense of depth,
because you're already wearing the depth glasses, right? So it would be those sort of big gross
motor, move things around, kind of skills seem much more spontaneous, just like they are in the
real world. The frontier for us, I think, is whether that same medium can be used to do fine
grain design tasks, like very accurate constraints on, say, a CAD model or something. That may be
better done on a desktop, but it may just be a matter of inventing the right UI.
So we're hopeful that because there will be this potential explosion of demand for 3D assets
that's driven by AR and more real-time animation on conventional screens,
that those tools will also help with, or those devices will help with designing the content
as well. You've mentioned quite a few interesting sort of new ideas. And at the same time, there's
old timers like me that are stuck in their old ways. I think I'm the old timer.
Okay. All right. But the opposed all change at all costs, kind of. Is there, when you're thinking
about creating new interfaces, do you feel the burden of just this giant user base that loves
the current product? And so anything new you do that any new idea comes at a cost that you'll
be resisted? Well, I think if you have to trade off control for convenience, then our existing
user base would definitely be offended by that. I think if there are some things where you have
more convenience and just as much control, that may be more welcome. We do think about
not breaking well-known metaphors for things. So things should sort of make sense.
Photoshop has never been a static target. It's always been evolving and growing.
And to some extent, there's been a lot of brilliant thought along the way of how it works
today. So we don't want to just throw all that out. If there's a fundamental breakthrough,
like a single click is good enough to select an object rather than having to do lots of strokes,
that actually fits in quite nicely to the existing tool set, either as an optional mode or as a
starting point. I think where we're looking at radical simplicity, where you could encapsulate
an entire workflow with a much simpler UI, then sometimes that's easier to do in the context
of either a different device like a mobile device where the affordances are naturally different
or in a tool that's targeted at a different workflow where it's about spontaneity and
velocity rather than precision. And we have projects like Rush, which can let you do professional
quality video editing for a certain class of media output that is targeted very differently
in terms of users and the experience. And ideally, people would go, if I'm feeling like doing
Premiere, big project, I'm doing a four-part television series, that's definitely a premier
thing. But if I want to do something to show my recent vacation, maybe I'll just use Rush because
I can do it in the half an hour. I have free at home rather than the four hours I need to do it at
work. And for the use cases, which we can do well, it really is much faster to get the same
output. But the more professional tools obviously have a much richer toolkit and more flexibility
in what they can do. And then at the same time, with the flexibility control, I like this idea of
smart defaults of using AI to coach you to like what Google has, I'm feeling lucky button.
Right. Or one button kind of gives you a pretty good
set of settings. And then you almost, that's almost an educational tool.
Absolutely. Yeah.
To show, because sometimes when you have all this control, you're not sure about the
correlation between the different bars that control different elements of the image and so
on. And sometimes there's a degree of, you don't know what the optimal is.
And then some things are sort of on demand like help, right? Where I'm stuck. I need to know what
to look for. I'm not quite sure what it's called. And something that was proactively making helpful
suggestions or, you know, you can imagine a make a suggestion button where you'd use all of that
knowledge of workflows and everything to maybe suggest something to go and learn about or just
to try or show the answer. And maybe it's not one intelligent to pothole, but it's like a variety
of defaults. And then you go, I like that one. Yeah. Yeah. Several options. Yeah. So back to
poetry. Ah, yes. We're going to interleave. So first few lines of a recent poem of yours
before I ask the next question. This is about the smartphone. Today left my phone at home
and went down to the sea. The sand was soft, the ocean glass, but I was still just me.
So this is a poem about you leaving your phone behind and feeling quite liberated because of it.
So this is kind of a difficult topic. And let's see if we can talk about it, figure it out.
But so with the help of AI, more and more, we can create sort of versions of ourselves,
versions of reality that are in some ways more beautiful than actual reality.
You know, and some of the creative effort there is part of doing this, creating this illusion.
So of course, this is inevitable, but how do you think we should adjust this human
beings to live in this digital world that's partly artificial, that's better than the world
that we lived in 100 years ago when you didn't have Instagram and Facebook versions of ourselves
and the online. Oh, this is sort of showing off better versions of ourselves. We're using the
tooling of modifying the images or even with artificial intelligence ideas of deep fakes and
creating adjusted or fake versions of ourselves and reality.
I think it's an interesting question. Well, sort of historical bent on this.
So I actually wonder if 18th century aristocrats who commissioned famous painters to paint portraits
of them had portraits that were slightly nicer than they actually looked in practice.
Well played, sir.
So a human desire to put your best foot forward has always been true. I think it's
interesting. You sort of framed it in two ways. One is if we can imagine alternate realities
and visualize them, is that a good or bad thing? In the old days, you do it with storytelling
and words and poetry, which still resides sometimes on websites. But, you know, we've
become a very visual culture in particular. In the 19th century, we were very much a text-based
culture. People would read long tracks, political speeches were very long. Nowadays,
everything's very kind of quick and visual and snappy.
I think it depends on how harmless your intent. It's a lot of it's about intent. So if you
have a somewhat flattering photo that you pick out of the photos that you have in your inbox to say,
this is what I look like, it's probably fine. If someone's going to judge you by how you look,
then they'll decide soon enough when they meet you whether the reality, you know.
I think where it can be harmful is if people hold themselves up to an impossible standard,
which they then feel bad about themselves for not meeting. I think that's definitely
can be an issue. But I think the ability to imagine and visualize an alternate reality,
which sometimes, which you then go off and build later, can be a wonderful thing too.
People can imagine architectural styles, which they then, you know, have a startup,
make a fortune, and then build a house that looks like their favorite video game. Is that
a terrible thing? I think I used to worry about exploration, actually, that part of the joy of
going to the moon when I was a tiny child, I remember it, and grainy black and white,
was to know what it would look like when you got there. And I think now we have such good
graphics for knowing, for visualizing the experience before it happens, that I slightly
worry that it may take the edge off actually wanting to go, you know what I mean? Because we've seen
it on TV, we kind of, oh, you know, by the time we finally get to Mars, we go, oh yeah,
there's Mars, that's what it looks like. But then, you know, the outer exploration, I mean,
I think Pluto was a fantastic recent discovery where nobody had any idea what it looked like,
and it was just breathtakingly varied and beautiful. So I think expanding the ability
of the human toolkit to imagine and communicate on balance is a good thing. I think there are
abuses, we definitely take them seriously and try to discourage them. I think there's a parallel
side where the public needs to know what's possible through events like this, right? So that
you don't believe everything you read and print anymore, and it may over time become
true of images as well. Or you need multiple sets of evidence to really believe something
rather than a single media asset. So I think it's a constantly evolving thing, it's been
true forever. There's a famous story about Anne of Cleves and Henry VIII where, luckily for Anne,
they didn't get married, right? Or they got married and broke up in it.
What's the story?
Oh, so Holbein went and painted a picture and then Henry VIII wasn't pleased and, you know,
history doesn't record whether Anne was pleased, but I think she was pleased not to be married
more than a day or something. So I mean, this has gone on for a long time, but I think it's
just part of the magnification of human capability.
You've kind of built up an amazing research environment here, research culture, research
lab, and you've written that the secret to a thriving research lab is interns. Can you unpack
that a little bit?
Oh, absolutely. So a couple of reasons. As you see looking at my personal history, there are
certain ideas you bond with at a certain stage of your career, and you tend to keep revisiting
them through time. If you're lucky, you pick one that doesn't just get solved in the next five years
and then you're sort of out of luck. So I think a constant influx of new people brings new ideas
with it. From the point of view of industrial research, because a big part of what we do is
really taking those ideas to the point where they can ship us very robust features,
you end up investing a lot in a particular idea. And if you're not careful, people can get
too conservative in what they choose to do next knowing that the product teams will want it.
And interns let you explore the more fanciful or unproven ideas in a relatively lightweight way,
ideally leading to new publications for the intern and for the researcher. And it gives you
then a portfolio from which to draw, which idea am I going to then try to take all the way through
to being robust in the next year or two to ship. So it sort of becomes part of the funnel. It's
also a great way for us to identify future full-time researchers. Many of our greatest researchers
were former interns. It builds a bridge to university departments so we can get to know
and build an enduring relationship with the professors and we often do academic give funds
to as well as an acknowledgement of the value the interns add and their own collaborations.
So it's sort of a virtuous cycle. And then the long-term legacy of a great research lab,
hopefully, will be not only the people who stay, but the ones who move through and then go off and
carry that same model to other companies. And so we believe strongly in industrial research and how
it can complement academia. And we hope that this model will continue to propagate and be
invested in by other companies, which makes it harder for us to recruit, of course, but that's
a sign of success and a rising tide lifts all ships in that sense. And where's the idea born
with the interns? Is there brainstorming? Is there discussions about, you know, like what?
Where do the ideas come from? Yeah, as I'm asking the question, I realize how dumb it is, but I'm
hoping you have a better answer than a better. A question I ask at the beginning of every summer.
So what will happen is we'll send out a call for interns. They'll, we'll have a number of
resumes come in, people will contact the candidates, talk to them about their interests.
They'll usually try to find somebody who has a reasonably good match to what they're already
doing, or just has a really interesting domain that they've been pursuing in their PhD. And we
think we'd love to do one of those projects too. And then the intern stays in touch with the
mentor, as we call them. And then they come and in the, at the end of two weeks, they have to
decide. So they'll often have a general sense by the time they arrive. And we'll have internal
discussions about what are all the general ideas that we're wanting to pursue to see whether
two people have the same idea, and maybe they should talk and all that. But then once the
intern actually arrives, sometimes the idea goes linearly. And sometimes it takes a giant left
turn and we go, that sounded good. But when we thought about it, there's this other project,
or it's already been done, and we found this paper, we were scooped. But we have this other
great idea. So it's pretty, pretty flexible at the beginning. One of the questions for research
labs is, who's deciding what to do? And then who's to blame if it goes wrong, who gets the
credit if it goes right? And so in Adobe, we push the needle very much towards freedom of choice
of projects by the researchers and the interns. But then we reward people based on impact. So
if the projects ultimately end up impacting the products and having papers and so on.
And so your alternative model, just to be clear, is that you have one lab director who thinks he's
a genius and tells everybody what to do, takes all the credit if it goes well, blames everybody
else if it goes badly. So we don't want that model. And this helps new ideas percolate up.
The art of running such a lab is that there are strategic priorities for the company.
And there are areas where we do want to invest in pressing problems. And so it's a little bit of a
trickle down and filter up meets in the middle. And so you don't tell people you have to do X,
but you say X would be particularly appreciated this year. And then people reinterpret X through
the filter of things they want to do and they're interested in. And miraculously, it usually comes
together very well. One thing that really helps is Adobe has a really broad portfolio of products.
So if we have a good idea, there's usually a product team that is intrigued or interested.
So it means we don't have to qualify things too much ahead of time.
Once in a while, the product teams sponsor an extra intern because they have a particular
problem that they really care about. In which case, it's a little bit more, we really need one of
these. And then we sort of say, great, I get an extra intern. We find an intern who thinks
that's a great problem. But that's not the typical model. That's sort of the icing on the cake,
as far as the budget's concerned. And all of the above end up being important. It's really hard
to predict at the beginning of the summer, which we all have high hopes of all of the intern projects.
But ultimately, some of them pay off and some of them sort of are a nice paper, but don't turn
into a feature. Others turn out not to be as novel as we thought, but they'd be a great feature,
but not a paper. And then others, we make a little bit of progress and we realize how much
we don't know. And maybe we revisit that problem several years in a row until it finally we have
a breakthrough. And then it becomes more on track to impact a product.
Jumping back to a big overall view of Adobe Research, what are you looking forward to
in 2019 and beyond? What is, you mentioned there's a giant suite of products,
a giant suite of ideas, new interns, a large team of researchers. Where do you think
the future holds? In terms of the technological breakthroughs?
Technological breakthroughs, especially ones that will make it into product will get to
impact the world. So I think the creative or the analytics assistance that we talked about where
they're constantly trying to figure out what you're trying to do and how can they be helpful and
make useful suggestions is a really hot topic. And it's very unpredictable as to when it'll be ready,
but I'm really looking forward to seeing how much progress we make against that. I think
some of the core technologies like generative adversarial networks are immensely promising
and seeing how quickly those become practical for mainstream use cases at high resolution with
really good quality is also exciting. And they also have this sort of strange way of even the
things they do oddly are odd in an interesting way. So it can look like dreaming or something.
So that's fascinating. I think internally we have a Sensei platform, which is a way in which
we're pooling our neural net and other intelligence models into a central platform, which can then be
leveraged by multiple product teams at once. So we're in the middle of transitioning from a,
you know, once you have a good idea, you pick a product team to work with and you sort of hand
design it for that use case to a more sort of Henry Ford, stand it up in a standard way, which
can be accessed in a standard way, which should mean that the time between a good idea and impacting
our products will be greatly shortened. And when one product has a good idea, many of the other
products can just leverage it too. So it's sort of an economy of scale. So that's more about the
how then the what, but that combination of this sort of renaissance in AI, there's a comparable
one in graphics with real time ray tracing and other really exciting emerging technologies.
And when these all come together, you'll sort of basically be dancing with light, right,
where you'll have real time shadows, reflections, and as if it's a real world in front of you,
but then with all these magical properties brought by AI where it sort of anticipates or
modifies itself in ways that make sense based on how it understands the creative task you're trying
to do. That's a really exciting future for creative for myself, too, as a creator. So first of all,
I work in autonomous vehicles. I'm a roboticist. I love robots. And I think you have a fascination
with snakes, both natural and artificial robots. I share your fascination. I mean, their movement
is beautiful, adaptable. The adaptability is fascinating. There are, I looked it up, 2900
species of snakes in the world. Wow. The 175 venomous, some are tiny, some are huge.
Saw that there's one that's 25 feet in some cases. So what's the most interesting thing
that you connect with in terms of snakes, both natural and artificial? Why, what was the connection
with robotics AI in this particular form of a robot? Well, it actually came out of my work
in the 80s on computer animation where I started doing things like cloth simulation and other kind
of soft body simulation. And you'd sort of drop it and it would bounce and then it would just sort
of stop moving. And I thought, well, what if you animate the spring lengths and simulate muscles?
And the simplest object I could do that for was an earthworm. So I actually did a paper in 1988
called The Motion Dynamics of Snakes and Worms. And I read the physiology literature on both
hell snakes and worms move and then did some of the early computer animation examples of that.
So your interest in robotics started with graphics?
Came out of simulation and graphics. When I moved from Alius to Apple, we actually did a
movie called Her Majesty's Secret Serpent, which is about a secret agent snake that parachutes
in and captures a film canister from a satellite, which tells you how old-fashioned we were thinking
back then. Sort of classic 1950s or 60s Bond movie kind of thing. And at the same time,
I'd always made radio-controlled ships when I was a child and from scratch. And I thought, well,
how can it be to build a real one? And so then started what turned out to be like a 15-year
obsession with trying to build better snake robots. And the first one that I built just sort of
slithered sideways, but didn't actually go forward. Then I added wheels and building things in real
life makes you honest about the friction. The thing that appeals to me is I love creating the
illusion of life, which is what drove me to animation. And if you have a robot with enough
degrees of coordinated freedom that move in a kind of biological way, then it starts to cross
the Yankani Valley into seeming like a creature rather than a thing. And I certainly got that
with the early snakes by S3. I had it able to sidewind as well as go directly forward.
My wife-to-be suggested that it would be the ring bearer at our wedding. So it actually
went down the aisle carrying the rings and got in the local paper for that, which was really fun.
And this was all done as a hobby. And then I, at the time that onboard compute was incredibly
limited, it was sort of-
Yeah. So you should explain that these snakes, the whole idea is that you would, you're trying to
run it autonomously.
Autonomously. Onboard now or on board right. And so the very first one, I actually built the
controller from discrete logic because I used to do LSI circuits and things when I was a teenager.
And then the second and third one, the 8-bit microprocessors were available with like a whole
256 bytes of RAM, which you could just about squeeze in. So they were radio controlled rather
than autonomous and really were more about the physicality and coordinated motion.
I've occasionally taken a sidestep into, if only I could make it cheaply enough, bake a great toy,
which has been a lesson in how clockwork is its own magical realm that you venture into and
learn things about backlash and other things you don't take into account as a computer
scientist, which is why what seemed like a good idea doesn't work. So it's quite humbling.
And then more recently, I've been building S9, which is a much better engineered version of
S3 where the motors wore out and it doesn't work anymore. And you can't buy replacements,
which is sad given that it was such a meaningful one. S5 was about twice as long and
looked much more biologically inspired. Unlike the typical roboticist, I taper my snakes.
They're a good mechanical reasons to do that, but it also makes them look more biological,
although it means every segment's unique rather than a repetition, which is why most engineers
don't do it. It actually saves weight and leverage and everything. And that one is currently on
display at the International Spy Museum in Washington, DC. None of it has done any spying.
It was on YouTube and it got its own conspiracy theory where people thought that it wasn't real
because I work at Adobe, it must be fake graphics. And people would write to me,
tell me it's real. They say the background doesn't move and it's on a tripod. So that one,
but you can see the real thing. So it really is true. And then the latest one is the first one
where I could put a Raspberry Pi, which leads to all sorts of terrible jokes about pythons and
things. But this one can have onboard compute. And then where my hobby work and my work work
are converging is you can now add vision accelerator chips, which can evaluate neural
nets and do object recognition and everything. So both for the snakes and more recently for
the spider that I've been working on. Having desktop level compute is now opening up a whole
world of true autonomy with onboard compute, onboard batteries. And still having that sort
of biomimetic quality that appeals to children in particular, they are really drawn to them.
And adults think they look creepy, but children actually think they look charming.
And I gave a series of lectures at Girls Who Code to encourage people to take an interest
in technology. And at the moment, I'd say they're still more expensive than the value
that they add, which is why they're a great hobby for me, but they're not really a great product.
It makes me think about doing that very early thing I did at alias with changing the muscle
rest lengths. If I could do that with a real artificial muscle material, then the next snake
ideally would use that rather than motors and gearboxes and everything. It would be
lighter, much stronger and more continuous and smooth. So I like to say being in research
is a license to be curious. And I have the same feeling with my hobby. It forced me to read biology
and be curious about things that otherwise would have just been natural geographic specials.
Suddenly, I'm thinking, how does that snake move? Can I copy it? I look at the trails that
sidewinding snakes leave in sand and see if my snake robots would do the same thing.
So out of something inanimate, I like why you put a try to bring life into it and beauty.
Absolutely. And then ultimately, give it a personality, which is where the intelligent
agent research will converge with the vision and voice synthesis to give it a sense of having
not necessarily human level intelligence. I think the Turing test is such a high bar. It's
a little bit self-defeating, but having one that you can have a meaningful conversation with,
especially if you have a reasonably good sense of what you can say.
So not trying to have it so a stranger could walk up and have one, but so as a pet owner or
a robot pet owner, you could know what it thinks about and what it can reason about.
Or sometimes just the meaningful interaction. If you have the kind of interaction you have
with the dog, sometimes you might have a conversation, but it's usually one way.
Absolutely.
And nevertheless, it feels like a meaningful connection.
And one of the things that I'm trying to do in the sample audio that we'll play you is
beginning to get towards the point where the reasoning system can explain why it knows something
or why it thinks something. And that, again, creates the sense that it really does know
what it's talking about, but also for debugging. As you get more and more elaborate behavior,
it's like, why did you decide to do that? How do you know that?
I think the robots really are my muse for helping me think about the future of AI and
what to invent next.
So even at Adobe, that's mostly operating in the digital world.
Correct.
Do you ever, do you see a future where Adobe even expands into the more physical world,
perhaps, so bringing life, not into animations, but bringing life into physical objects with
whether it's, well.
I have to say at the moment, it's a twinkle in my eye. I think the more likely thing is that we
will bring virtual objects into the physical world through augmented reality and many of
the ideas that might take five years to build a robot to do, you can do in a few weeks with
digital assets. So I think when really intelligent robots finally become commonplace,
they won't be that surprising because we'll have been living with those personalities
for in the virtual sphere for a long time. And then they'll just say, oh, it's, you know,
Siri with legs or Alexa, Alexa on hooves or something.
So I can see that welcoming. And for now, it's still an adventure. And we don't know
quite what the experience will be like. And it's really exciting to sort of see all of
these different strands of my career converge.
Yeah, in interesting ways. And it is definitely a fun adventure.
So let me end with my favorite poem, the last few lines of my favorite poem of yours
that ponders mortality. And in some sense, immortality, you know, as our ideas live through
the ideas of others, through the work of others, it ends with, do not weep or mourn.
It was enough the little atomies permitted just a single dance.
Scatter them as deep as your eyes can see. I'm content they'll have another chance.
Sweeping more centered parts along to join a jostling lifting throng as others danced in me.
Beautiful poem, beautiful way to end it. Gavin, thank you so much for talking today.
And thank you for inspiring and empowering millions of people like myself for creating
amazing stuff.
Oh, thank you. It's great conversation.