Sublime

Paradoxically, my concerns about being mistaken for some kind of industry insider ran parallel to my now decades-long struggle as an outsider.

When those influences are allowed to change over time, growing stronger or weaker in response to success or failure in completing a task, a network of neurons can, in essence, learn.

Adversarial examples became even more intriguing to the research community when Szedegy et al. [288] showed that deep neural networks used for image classifcation can be easily manipulated, and adversarial examples were visualized. In the context of image classifcation, the perturbation of the original sample must be small so that a human cannot ob

We can see the gears of visual understanding turn in the piercing contrast between the chiaroscuros of Caravaggio and the soft shadows of Vermeer and Zorn. We can transcend realism to distill the iconography of everyday life with van Gogh and the stylized portraiture of Kahlo. We even feel its presence in the comparatively opaque statements of mode

Understanding Image Processing AI

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But beyond the numbers lay the accomplishment that moved me most: the realization of a true ontology of the world, as conceptual as it was visual, curated from the ground up by humans for the sole purpose of teaching machines.

A system like AlexNet, Dietterich argues, that is trained on images tagged as one of a thousand categories, “implicitly assumes that the world consists of only, say, a thousand different kinds of objects.”