It's easy to mistake this photo for some kind of surreal landscape painting, but this image in fact shows off the imagination of Google's advanced image detection software. Similar to an artist with a blank canvas, Google's software constructed this image out of nothing, or essentially nothing, anyway. This photo began as random noise before software engineers coaxed this pattern out of their machines. How is it possible for software to demonstrate what appears to be an artistic sensibility? It all begins with what is basically an artificial brain.When Art Meets Science: Photos
Artificial neural networks are systems consisting of between 10 and 30 stacked layers of synthetic neurons. In order to train the network, "each image is fed into the input layer, which then talks to the next layer, until eventually the 'output' layer is reached,"the engineers wrote in a blog post detailing their findings
. The layers work together to identify an image. The first layer detects the most basic information, such as the outline of the image. The next layers hone in on details about the shapes. The final output layer provides the "answer," or identification of the subject of an image. Shown is Google's image software before and after processing an image of two ibis grazing to detect their outlines.How Face Recognition Tech Will Change Everything
Searching for shapes in clouds isn't just a human pastime anymore. Google engineers trained the software to identify patterns by feeding millions of images to the artificial neural network. Give the software constraints, and it will scout out patterns to recognize objects even in photos where the search targets are not present. In this photo, for example, Google's software, like a daydreamer staring at the clouds, finds all kinds of different animals in the sky. This pattern emerged because the neural network was trained primarily on images of animals.Cloud-Gazing: Learn Your Cloud Types
How the machine is trained will determine its bias in terms of recognizing certain objects within an otherwise unfamiliar image. In this photo, a horizon becomes a pagoda; a tree is morphed into building; and a leaf is identified as a bird after image processing. The objects may have similar outlines to their counterparts, but all of the entries in the "before" images aren't a part of the software's image vocabulary, so the system improvises.Facial Recognition System Detects Pain
When the software acknowledges an object, it modifies a photo to exaggerate the presence of that known pattern. Even if the software is able to correctly recognize the animals it has been trained to spot, image detection may be a little overzealous in identifying familiar shapes, particularly after the engineers send the photo back, telling the software to find more of the same, and thereby creating a feedback loop. In this photo of a knight, the software appears to recognize the horse, but also renders the faces of other animals on the knight's helmet, globe and saddle, among other places.Photo First: Light Captured as Both Particle and Wave
Taken a step further, using the same image over several cycles in which the output is fed through over and over again, the artificial neural network will restructure an image into the shapes and patterns it has been trained to recognize. Again borrowing from an image library heavy on animals, this landscape scene is transformed into a psychedelic dream scene where clouds are apparently made of dogs.Plants Thrive in Psychedelic, Underground Farms
At its most extreme, the neural network can transform an image that started as random noise into a recognizable but still somewhat abstract kaleidoscopic expression of objects with which the software is most familiar. Here, the software has detected a seemingly limitless number of arches in what was a random collection of pixels with no coherence whatsoever.Digital 'Head Dome' Immerses You in Art
This landscape was created with a series of buildings. Google is developing this technology in order to boost its image recognition software. Future photo services might recognize an object, a location or a face in a photo. The engineers also suggest that the software could one day be a tool for artists that unlocks a new form of creative expression and may even shed light on the creative process more broadly.New Google Initiative Targets Classical Music Lovers
Thinking about doing something that you shouldn’t? Those bad intentions originate from a specific part of the brain, and a new study published in Nature Neuroscience identified just where that is.
The warning signs of premediated violence turn up in the hypothalamus, a part of the brain that also regulates temperature, hunger and sleep. Specifically, the ventrolateral part of the ventromedial hypothalamus, or VMHvl, is the area responsible for our ill will.
Understanding the neurological underpinning of aggressive actions in the brain could provide scientists with new therapeutic techniques for controlling these behaviors, which has implications for violent crime prevention. However, any treatments to that effect are “only a distant possibility, even if related ethical and legal issues could be resolved,” according to researcher Dayu Lin of the Neuroscience Institute at NYU Langone.
Because of those same “ethical and legal issues,” the subjects used in the NYU study were not humans but instead mice, which share many of our brain structures. The researchers monitored aggression in male mice trained to attack weaker rodents.
The aggressive mice would have to choose between two options: It could stick its snout an empty port, yielding no reward, or a hole leading to another mouse, which resulted in a food reward.
In the angry mice, just before they literally were about to stick their noses in another mouse’s business, nerve cell activity in the VMHvl spiked. Activity increased 10 times normal levels after the aggressive rodents spotted the weaker ones.
After the researchers genetically inhibited VMHvl activity, the once-aggressive mice calmed down, though retained some of their learned behaviors associated with the training in order to get a food reward.
“Our study pinpoints the brain circuits essential to the aggressive motivations that build up as animals prepare to attack,” Lin said in a statement.
The latest study continues a thread of research probing the neurological roots of aggression. Last month, the same scientists identified what they described as the origin of rage in the male animal brain. Damage to the lateral septum, a part of the brain linked to control of anxiety and fear, triggers a domino effect in the brain that leads to “septal rage,” or outbursts of unprovoked violence.
In addition to identifying the structure associated with rage, the researchers proved able to control aggressive outbursts, starting and stopping them using a surgically inserted probe.
Septal rage has not been seen in people, but the study of the neurological patterns underling this condition could provide a window that allows scientists to understand violent behavior in humans.