YouSaid · the spoken record

Gavin Miller

lines on the record: 86
first: 2019-06-10
most recent: 2019-06-10
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“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”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 guardrails 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 sort of a, 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 And so, I mean, it's”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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. Again, you were asking about how you know that it's ready for prime time.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 learnt from the training set.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 dark to light if it straddles a boundary. Where it gets complicated is if you have to infer invisible structure behind.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“So 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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 yeah”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, so that was something we called Concept Canvas. So normally when you do an image search, assuming 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“I was a big fan in college of Magritte, and he has a number of paintings where it's you realism 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 is 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”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 like a 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. 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 And dials to really express the subtlety of what they want to do.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. And then in the longer term, 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 you know to ask for it, then it 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 talk.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“It's still a grand challenge. You know, we'd all love an artist over one shoulder and a teacher over the other, right? 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 and 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, you know, I was in a meeting today thinking about these things.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“We absolutely do. So we're 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 at all. And we're just 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 Kai where it was looking at”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“That might be a case where it's not about being perfect every single time, but perfect enough of 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 But then we also have shipping, which is a different type of. And then we get customer review, as well as product critics.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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, Adobe 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 a MAT, well, 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. Workflows that are really nice. And then you might go in and touch up a little.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 with 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 as 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“Another one is something we've spent a lot of work over the last 20 years. I've been at Adobe or 19 thinking about selection, for instance, With 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 where you 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 artisan 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 lightning 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, it's interesting. It depends. I think at Adobe, we really want to spend 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 task become automated. It frees the artists up to focus on the inspiration unless of the perspiration. So thinking about different ideas, obviously”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“I sort of gone on a journey. My early days in my career were about 3D computer graphics, the sort of pioneering work 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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 holder, 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”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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...”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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, it would say, don't forget the washing and so on. I made 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”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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, it 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, you know, since then.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, exactly. Take into 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 the 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.”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2019-06-10 · Lex Fridman Podcast · Gavin Miller: Adobe Research · IDENTIFIED FROM THE TRANSCRIPT · source