Teaching Machines to Draw
research.googleblog.com
research.googleblog.com
* It's a different way to represent drawings. Alternative to pixels
* Perhaps making it easier to render old-school animation at one point?
It's interesting to think about, regardless. Stuff like this gets me excited (and motivated!) to delve into machine learning and AI.
That's what I was referring to happening in 20 years. :)
Hm. So a machine trained exclusively by men/women would make different drawings?
So the Why seems self-evident to me: if you want to understand intelligence, try to make machines do intelligent things.
That's not to say I think that approach will necessarily lead to a system capable of general intelligence. But I'm assuming that is their current approach.
Might also be helpful for captchas?
GPS was invented by a few guys "just playing around" with the signals put out by sputnik.
> ...we created a model that potentially has many applications, from assisting the creative process of an artist, to helping teach students how to draw.
For vector output, there's also a subtle but important distinction between machine-drawn images (based on rasterized data) converted to vectors, and generating machine-drawn vector images. The latter could be more useful in (as you mention) animation, as well as producing vector images with clearly isolated elements - e.g., a vector image wherein occluded elements are represented with full masked shapes (preserving editable layers) rather than a single "flattened" vectorized layer.
1. It's not computer art. I believe in the possibility of artificial intelligence, and when we encounter it it will be so different from human intelligence that asking it to make imitations of human art will seem like an insult. We probably won't understand its art very well either at first.
2. I'm getting really tired of people racing to automate every damn thing. even if we establish an economic utopia and nobody has to work any more what are people supposed to do all day if every human activity can be performed 'better' by a machine?
3. It won't really be 'better' though, it will just be more popular because so many programmers are trapped in a quantitative mindset and thus treat every problem they encounter like a nail to be hammered in. Imitative digital technologies will always be correlated with popularity, limiting creative innovation because developers can't think of a reason to optimize for or nurture anything that is initially unpopular.
Creative prostheses that all require the same amount of effort to deploy (ie none) will be hailed as 'allowing everyone to be an artist' without requiring them to in best any meaningful time or effort in ideas that don't pay off or that fail. The result, which we are already seeing, is a plethora of new material created with little effort that is as superficial as it is ephemeral, whose volume and variety will obscure its stultifying conventionality.
This is no more art than Cheese Whiz is food. It's Art-flavored mechanical product that functions to do no more than alleviate the masses' thirst for self-actualization without any adjustment of power structures and is thus fundamentally limited to reproduction of the cultural conditions from which it originates.
Art has always been "ineffable" and will remain so, as long as humans have thoughts and feelings. Bad art, or lazy productions will be as ignored as ever.
When machines learn something - they start being able to replicate it perfectly across as many machines as they want, parallelize it and execute it without mistakes more times than humans have ever done in history.
So that means - a nearly infinite glut of amazing art, jokes, music and movies. And not just that... but attacks on all our systems including reputation, trust, voting and so on.
Today our systems depend on the assumption that attackers are limited in their ability to proceed and expand quickly. How would things work if attackers were not? You already prefer to ask google more than your parents. What if software made better jokes, drawings and had sex better? And simulated emotions better?
I'm not a professional researcher, but here's what I gather from reading other articles:
One major problem with image recognition in machines is that while they are generally able to recognize real images correctly, they are 1) Easily fooled 2) Unable to have human-type image understanding. For example, you can recognize an animated character as a human, even though you may never have seen that particular style of drawing before.
One major problem that people realized is that deep neural networks have a tendency to recognize individual facets, for example, a nose. So if you want a neural network to believe something is a human, pepper it with as many noses as you can. It knows that humans have noses so the more noses the more human it must be. Of course, if we saw an image like this we wouldn't think that it was a human because we know a human has only one nose.
To me it seems the primary thrust of this research is to generate a NN that can recognize, like a human, that if an entity has 10 eyes, it's probably not a cat. This is alluded to with the house of horrors cat photographs at the beginning of the article. You can see that when passed an image of a cat with 3 eyes, this neural network correctly removed one of the eyes to make it more realistic.
Here is an article explaining this problem: https://arxiv.org/pdf/1412.1897.pdf
A bit of a shame that most commenters on HN are focusing on more metaphysic discussions and the eternal "This is not AI. You are just [insert something that people considered AI 6 months ago here]."
You can also see my question as a rebuttal to the "it is useless" argument.
I was also fascinated at trying to understand how we are able to translate a vague concept in our minds, into a sequence of motor actors that doodles out this concept onto a piece of paper. We also take into account some feedback information during the doodling process. For example, I compare what I already doodled with what I actually want to draw, and decide I'll doodle next based on this information.
So we thought one way to study this ability of going from concept -> sketch, is to construct a very simple model of this doodling process, and try to train the model to doodle. We model this "vague concept" as a vector of floating point numbers. To model this "vagueness", we add noise to this vector. This way the model must learn to work with noisy concepts. The model takes this floating point vector as an input and (randomly) samples an output sequence of simple motor actions that doodles out an object. The sampling process is random (the model's outputs are the parameters of a pdf at each timestep), so the model can produce many different outputs given the same input. During the sampling process, the model feeds back into its input what it has just drawn, and process this information to decide what to draw next.
We show that our simplified model of the doodling process is also able to go from concept -> sketch, and also from sketch -> concept, and show that the concepts can be augmented, to alter the sketch the model produces in a meaningful way. We tried to make this model simple and robust enough so that in the future, we can incorporate it into more complicated models that try to do more than just doodling a simple object.
When skynet shows up I'm blaming you
The input sketches look a lot like the doodles that people sketched in quick draw (o)
If true that they reused this data I commend their resourcefulness and their clever way of turning data entry into an unaware fun game
One thing interesting about this kind of automatic/AI-generated art is that it forces us to examine preconceptions about human creativeness. What does art mean when an algorithm can paint or draw as well?
The process is more important than the end result.
said no one ever about their favorite record, movie, painting, or book.
means the same thing it does before. :) we're just not the exclusive authors of it.
Today's AI can replicate works of art and simulate the technical processes, but we're far from the cognitive depth required for creative artistic expression.
You think art is just some stimulus-response thing because American psychology has been mired in behaviorism for decades and lacks a coherent theory of mind. But art is much more than the whimsical reproduction of presented stimuli to varying levels of accuracy, it is about making selections that foreclose other possibilities and which embody a certain perversity.
I paint, among other things, and one thing I especially enjoy about painting is that it's solitary rather than performative so I don't have to interact with other people while I slap colored goop onto sheets of fabric. While I'm painting, I think intensely about the part I'm working on now, (duh) and also why I'm making that painting, and what decisions about the painting are coming up that will be impossible to undo.
Why did you paint this and why did you paint it this way are questions that cannot be answered by automation. Nor does the answer lie in technique. There's no shortage of technically astonishing work that is semantically empty; I die a little every time I see a Facebook video of some impressive new graphic technique that is then used to reproduce some lowest-common-denominator pop icon for maximum recognizability. There's no feeling there and the resultant work is about as thrilling as a robocall or a display mannequin. The level of craftsmanship is very high indeed, but the level of artistry is close to zero. In short, it's eye candy that never activates anything much past your visual cortex, or at most tugs on some existing semantic relationship.
When I talk about feeling, I mean the desire of the artist that the work embodies. That isn't something that comes along after a certain level of technical accomplishment has been reached. It is what motivates the act of creation in the first place.
That covers a lot of ground. No-one prompts us directly to draw or tells us what we must draw. Still, something does prompt us. Something does determine what we draw. It's our reactions to those somethings that defines us.
The unanswered question is whether there is something in our nature that differentiates our "initiative" and our "reactions" in a qualitative way from what can be achieved with current computational concepts.
I think the answer to that is probably yes. But still this work is impressive and every step like this elucidates the argument more clearly, reducing it to its fundamentals - rather than to crude heuristics about what constitutes a particularly human ability.
They didn’t have much trouble
teaching the ape to write poems:
first they strapped him into the chair,
then tied the pencil around his hand
(the paper had already been nailed down).
Then Dr. Bluespire leaned over his shoulder
and whispered into his ear:
“You look like a god sitting there.
Why don’t you try writing something?”
We look like gods sitting here.Interesting thought, how would a super-intelligence deal with the philosophical question of free will?
> "For example, these models sometimes produce amusing images of cats with three or more eyes, or dogs with multiple heads."
Yeah..."amusing".
How is that not the title of the blog post?
This is more than just making pretty pictures, the machine understands cats and pigs in a human way. It knows how many legs and eyes they're supposed to have, and where they go. And this isn't some human made algorithm, it learned that on its own.
The language of the paper even implies that the researchers don't quite know how the machine can do it. If this is a prequel to research on strong AI humanity is completely fucked.
I love DL but on the topic of visceral reactions of terror (or fear) to AI research, for me it was the recognition of the hot research on facial recognition(age, gender, ethnicity) with CNN's. Immediately, I had this vision of a dystopian future (informed by WW2) where some dictatorship had cameras that looked for people of a particular ethnicity and alerted soldiers of the targets position.
http://www.aaronshome.com/aaron/aaron/index.html
https://en.wikipedia.org/wiki/AARON
Pamela McCorduck's book about Cohen's work, Aaron's Code, is also a good read.
In this case, Google has spent shareholder resources on a project that really, could be done at any university, on a product that does not put the user first, and Google owns the IP. In fact, wake up -- you the public should view this product as a mechanism for Google to simply collect more data from people. The more people use this, the better Google's algorithms get at drawing. That's all there is to it. Thankfully, this product is not even fun.
There is a dangerous creed currently executed by Google leadership. Consider the Verily Study Watch: https://blog.verily.com/2017/04/introducing-verily-study-wat... . The watch shows to the wearer only one thing: the time. However, it collects all kinds of data, ostensibly for medical research (at least to start). Forget about putting the user first, the Verily blog post literally talks about "user compliance".
Of any project that comes out of Google, you should ask: Does this project even put the user first? Does this project even put Google's shareholders first? And if you are a current Google shareholder (as many of their current and former employees are, if they haven't sold), you should agitate that Google start accepting and focusing on becoming a value company, if all the further growth opportunities they are able to execute is just further user exploitation.
Google would prefer not to risk losing a shedload of money just so that their sketch-processing neural network can amuse people by correctly recognizing penises.
It's just this one subject because few others get people so upset.
(And contemplating others that might suggests that actually it's not strictly just this one subject. False positives for "decapitated corpse" or "big pile of excrement", say, might be just as problematic. Want to guess whether the system is good at recognizing decapitated corpses and piles of excrement?)
I know this is just an experiment right now but I want to put sex/nudity on the table as a subject of debate because it is central to artistic endeavor because arbitrary standards can become almost universal and institutionalized through path dependence (such as the QWERTY keyboard you are probably using right now). Imagine a not-too-distant future with a Magic Brush that easily allows you to paint the colors and shapes of your choice with the aid of some technological wizardry, but prevents the creation of nude or sexualized figures. That would not be a healthy development.
People, grow up.
Having both ways to encode and decode is useful for interfacing with models and visualizing their internal states. It also leads to unsupervised learning of representations. Instead of generating simple labels, now we can generate very complex data.