Archivo de la categoría: Tecnologías

LA METAMORFOSIS DE LAS TABLETAS. YURISANDER GUEVARA

YURISANDER GUEVARA

YURISANDER GUEVARA

Cuando Steve Jobs presentó el primer iPad en 2010, los gurús de la tecnología a nivel global auguraron que las computadoras de escritorio, y hasta las móviles, desaparecerían. Sería este producto el encargado de reconfigurar nuestra forma de entender la computación y la manera de interactuar con esta.

Si bien cada iPad ha tenido un buen mercado a su disposición, y en general durante el primer lustro las tabletas desataron una fiebre por su uso, con el tiempo se desinflaron sus ventas para convertirse de «matadores de ordenadores» a ser llamados como un «mercado efímero».

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Los que sí irrumpieron con fuerza en la vida de miles de millones de personas durante este tiempo fueron los móviles, a tal punto que hoy existe en funcionamiento más de uno por cada habitante del planeta.

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THE NEW NEURAL INTERNET IS COMING. OLEKSANDR SAVSUNENKO

How it all began / The Landscape

Generative Adversarial Networks progress

Think of the typical and well-studied neural networks (such as image classifier) as a left hemisphere of the neural network technology. With this in mind, it is easy to understand what is Generative Adversarial Network. It is a kind of right hemisphere — the one that is claimed to be responsible for creativity.

The Generative Adversarial Networks (GANs) are the first step of neural networks technology learning creativity. Typical GAN is a neural network trained to generate images on the certain topic using an image dataset and some random noise as a seed. Up until now images created by GANs were of low quality and limited in resolution. Recent advances by NVIDIA showed that it is within a reach to generate photorealistic images in high-resolution and they published the technology itself in open-access.

Examples of GAN images. Some are good, some are bad.

Conditional GANs and variational auto encoders

There is a plethora of GANs types of various complexity, architectures, and strange acronyms. We are mostly interested here in conditional GANs and variational autoencoders. Conditional GANs are capable of not just mimicking the broad type of images as “bedroom”, “face”, “dog” but also dive into more specific categories. For example, the Text2Image network is capable of translation textual image description into the image itself.

By varying random seed that is concatenated to the “meanings” vector we are able to produce an infinite number of birds image, matching description.  Seguir leyendo THE NEW NEURAL INTERNET IS COMING. OLEKSANDR SAVSUNENKO