generator


Can We Teach a Computer to Be Creative?

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Creativity is no small task. We do our best to cultivate and cherish the imagination of young children, and struggle to maintain those senses as we get older. But what exactly is creativity? Can its rules be written down? Is there an algorithm for it?


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Online fashion tech startup Vue.ai is selling technology that analyzes pieces of clothing and automatically generates an image of the garment on a person of any size, shape, or wearing any kind of shoes. Neural networks, the technology that GANs are built on, are an approximation of how our brain works: Millions of tiny, distributed neurons, processing data and passing them along to the next neuron. These networks are trained on thousands of images, and the neurons learn to distinguish different kinds of elbows, hips, and colors. Through trial and error, the two engineers figured out exactly the right neurons to alter the size, weight, or shape of a person, or the hardest part, the shoes they're wearing.


Artificial intelligence can say yes to the dress

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Online fashion tech startup Vue.ai is selling technology that analyzes pieces of clothing and automatically generates an image of the garment on a person of any size, shape, or wearing any kind of shoes. Neural networks, the technology that GANs are built on, are an approximation of how our brain works: Millions of tiny, distributed neurons, processing data and passing them along to the next neuron. These networks are trained on thousands of images, and the neurons learn to distinguish different kinds of elbows, hips, and colors. Through trial and error, the two engineers figured out exactly the right neurons to alter the size, weight, or shape of a person, or the hardest part, the shoes they're wearing.


datas-frame – Scalable Machine Learning (Part 2): Partial Fit

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Scikit-learn supports out-of-core learning (fitting a model on a dataset that doesn't fit in RAM), through it's partial_fit API. If you aren't familiar with dask, its arrays are composed of many smaller NumPy arrays (blocks in the larger dask array). We've seen a way to use scikit-learn's existing estimators on larger-than-memory dask arrays by passing the blocks of a dask array to the partial_fit method. If you're interested in contributing, I think a library of basic transformers that operate on NumPy and dask arrays and pandas and dask DataFrames would be extremely useful.


How artificial intelligence will change energy - Drax

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At the beginning of 2016, the world's most sophisticated artificial intelligence (AI) beat World Champion Lee Sedol at a game called'Go' – a chess-like board game with more move combinations than there are atoms in the universe. Earlier this year the UK's National Grid revealed it's making headway in integrating AI technology into Britain's electricity system. An undoubtedly large factor in the growing sophistication of AI in the energy space is the amount of energy use data now being captured. Yet to be operational in the UK, this sort of automation and peer-to-peer energy supply hints at the increasing decentralisation of energy grids, which are moving away from relying only on a number of large generators.


Artificial intelligence just made guessing your password a whole lot easier

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The work could help average users and companies measure the strength of passwords, says Thomas Ristenpart, a computer scientist who studies computer security at Cornell Tech in New York City but was not involved with the study. Giuseppe Ateniese, a computer scientist at Stevens and paper co-author, compares the generator and discriminator to a police sketch artist and eye witness, respectively; the sketch artist is trying to produce something that can pass as an accurate portrait of the criminal. The scientists fed each tool tens of millions of leaked passwords from a gaming site called RockYou, and asked them to generate hundreds of millions of new passwords on their own. Using GANs to help guess passwords is "novel," says Martin Arjovsky, a computer scientist who studies the technology at New York University in New York City.


Finding meaning in generative adversarial networks

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The current wave of generative AI research builds on the generative adversarial network, or GAN, a neural network structure introduced by Ian Goodfellow and his collaborators in 2014. A generative adversarial network consists of two neural networks: a generator that learns to produce some kind of data (such as images) and a discriminator that learns to distinguish "fake" data created by the generator from "real" data samples (such as photos taken in the real world). The generator and the discriminator have opposing training objectives: the discriminator's goal is to accurately classify real and fake data; the generator's goal is to produce fake data the discriminator can't distinguish from real data. Neural networks are good at making simple inferences on rich data; with multiple layers of neurons, they're able to organize themselves to detect patterns at multiple levels, from fragments of texture down to fundamental structure, and they can catch patterns that a human might miss.


2017 beginner's review of GAN architectures

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In this article, we aim to give a comprehensive introduction to general ideas behind Generative Adversarial Networks (GANs), show you the main architectures that would be good starting points and provide you with an armory of tricks that would significantly improve your results. Let's add another network that will learn to generate fake images that Discriminator would misclassify as "genuine." As you remember from statistical learning theory, that essentially means learning the underlying distribution of data. So, for our networks, it means that if we train them long enough, the Generator will learn how to sample from true "distribution," meaning that it will start generating real life-like images, and the Discriminator will not be able to tell fake ones from genuine ones.


Generative Machine Learning on the Cloud – Artists and Machine Intelligence – Medium

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With this backdrop of increasingly user-friendly AI, I spent the summer working with Google's Artists & Machine Intelligence (AMI) program on a cloud-based tool to make generative machine learning and synthetic image generation more accessible, especially to artists and designers. The end-to-end system design allows a user to provide a custom dataset of images to train a Variational Autoencoder Generative Adversarial Network (VAE-GAN) model on Cloud ML. The CVAE is trained by appending the one-hot encoded vector representing the label of the input image (so if the input image is a 9, the label vector is [0,0,0,0,0,0,0,0,0,1]) to the input image and the latent space vector. Then to request a specific generated number, the user can input a random embedding sampled from the unit gaussian distribution combined with the one-hot encoded vector of the number desired.


Why GANs give artificial intelligence wonderful (and scary) capabilities - Orange Silicon Valley

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Many of the arguments on both sides are informed by the results stemming from a technique called Generative Adversarial Networks (GANs) that has given AI anthropomorphic qualities often associated with human motivations. These two entities are trained over a large number of iterations improving the ability of both entities. Eventually, the discriminator learns to tell fake images from real images, and the generator uses the feedback from the discriminator to learn to produce convincing fake images. In a more multiplayer gaming context, an AI upgrade to "Elite: Dangerous," a multiplayer space simulation, made the AI a significant threat to players; spaceships became incredibly powerful, were better in fights, pulled players into brawls, and attacked them with upgraded super weapons created by the AI – features and behavior that the designers never intended.