![]() The discriminator is given the generator’s predicted image, and the real Bob Ross painting, and it is asked to guess which image is which. The second network is a “discriminator”, and it is the source of the feedback to the generator network. But what makes it different is where it gets that feedback from. Like other neural networks, the generator starts off making its choices at random, and learns to make better choices over time, by getting feedback on its success. For given input data - in our case our Blob Ross paintings - it produces a predicted image - what it thinks Bob Ross would make from that input. Instead of just one neural network, the GAN employs two, working in a kind of competition with each other. While GANs build on the concept of the neural network, they also introduce an extremely interesting innovation. GANs are responsible for some of the most exciting, and the most terrifying recent innovations in computer-generated images, including “deepfake” technologies that can seamlessly paste a new face onto an actor’s (often unclothed) body. ![]() The GAN was first proposed only a handful of years ago, in 2014. But the technique we’re going to use in this essay is a genuinely recent invention: the “Generative Adversarial Network” or “GAN”. Many of the other techniques used in modern machine learning are even older. Neural networks, like the one I used to identify medieval pole arms, were (arguably) first described in the 1940’s. Most of the techniques and algorithms used in artificial intelligence are, relatively speaking, quite old. Most likely it would produce a khaki smudge. A traditional algorithm attempting to predict this would try to split the difference - it would try to find the colour and shape exactly in the middle of all Ross’s mountains. While they’re all recognisably in his style, they’re also quite dramatically different - they are different colours, they have different patterns of light and shade, and they’re very different shapes. Here are some mountains from a few Bob Ross paintings. We’ll take all of the elements of a Bob Ross painting - majestic mountains, quiet rivers, cosy cabins, sweeping clouds, and happy little trees - and render each of them as simple blobs of colour in vaguely the right shape. To produce that, we’ll need to go through a painstaking process of reverse engineering - taking completed Bob Ross paintings, and creating a blobby equivalent for each of them. What we need is a set of training data - a set of blobby paintings paired with a real Bob Ross equivalent. We’re getting closer to turning this into a solvable problem. It needs to translate, effectively, between my semi-incompetent input, and a Bob-Ross-alike output. In this case, we want an algorithm that, given one of my blobby creations, can predict (and display) what the painting would look like, had it been painted by Bob Ross himself. Given an input, they predict an output that is most likely to meet some desired criteria, to minimise error. What I want is something that can turn my garish blobs into something resembling a Bob Ross painting, without the bother of having to learn to do it myself.Īrtificial intelligence algorithms work, for the most part, on the principle of prediction. I want to short-circuit the process of practice. Can we structure the problem in such a way as to make it solvable for a computer? What does it mean to teach a computer to paint? What I want to achieve is a system where I can, with minimal input, produce a novel painting with minimal effort. This is a tricky problem for artificial intelligence. But can I get an algorithm to do that practice for me? Can I teach a computer to paint? Can I take a shortcut? Bob Ross says if I’m willing to practice, I can paint just like him. I have a lot of work to do, and a lot of essays to write. And Ross seems so at peace while painting, so lost in the tiny bucolic Alaskan wilderness he’s creating on the canvas. I’m a firm believer in hard work and practice over some nebulous concept of “talent”. It’s a hugely appealing idea, and it’s a sentiment I tend to share. Anything that you’re willing to practice, you can do.” - Bob Ross One of the more charming aspects of Ross’s delivery is his assurance that, given enough practice, anyone could learn to paint. His gentle delivery and effortless manner are a soothing balm in a hectic world. Despite, or perhaps because of this complete lack of ability with a brush, I find celebrity TV painter Bob Ross completely enthralling. But break out the oil paints and it’s all over - the best I can produce is some indecipherable blobs in garish colours. I can make a credible effort with a pen or pencil, provided you’re not after something tricky like human hands or horses. I am not, I will admit, a terribly good artist.
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