Doesn't it look like an unknown work of Rembrandt? Would you believe it if I said that the painting was generated by an engine driven by artificial intelligence (AI)? And what if I say that painting was created just by carefully chosen words? They say a picture is worth a thousand words. Well, in this case, several words make up a picture.

Artificial intelligence (AI) is not only affecting industries like business and healthcare. It is also playing an increasing role in the creative industries by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists. We often hear that AI is going to automate away or take over all human tasks, including those in art, film, and other creative industries. But this is far from the case. AI is a supplemental tool that artists can use to explore new creative territory.

This article is about the cognitive science of visual art. Artists create physical artifacts (such as sculptures or paintings) which depict people, objects, and events. These depictions are usually stylized rather than photo-realistic. How is it that humans are able to understand and create stylized representations? Does this ability depend on general cognitive capacities or an evolutionary adaptation for art? What role is played by learning and culture? Machine Learning can shed light on these questions. It's possible to train convolutional neural networks (CNNs) to recognize objects without training them on any visual art. If such CNNs can generalize to visual art (by creating and understanding stylized representations), then CNNs provide a model for how humans could understand art without innate adaptations or cultural learning. I argue that Deep Dream and Style Transfer show that CNNs can create a basic form of visual art, and that humans could create art by similar processes. This suggests that artists make art by optimizing for effects on the human object-recognition system. Physical artifacts are optimized to evoke real-world objects for this system (e.g. to evoke people or landscapes) and to serve as superstimuli for this system.

Pretrained Artificial Neural Networks used to work like a Blackbox: You hand them an input and they predict an output with a certain probability -- but without us knowing the internal processes of how they came up with their prediction. A Neural Network to recognize images usually consists of around 20 neuron layers, trained with millions of images to tweak the network parameters to give high quality classifications. The layers consist of neurons that are trained to only forward information if they recognize one specific image feature, resulting in an action potential that serves as an input for the neurons of the next deeper layer. Each layer gets the information of the previous layer and supplies information to the next one until the output layer states the networks prediction. How many neurons of a certain layer fired their action potential implies how strongly the layer recognized its training features in the provided image. One of the little things we know about the functionality of Neural Network that recognize images is that each additional layer extracts higher level features of the image: While the first layer looks for edges and corners, the middle layers recognize shapes, the last layers whole objects and compositions.

Shan Carter, a researcher at Google Brain, recently visited his daughter's second-grade class with an unusual payload: an array of psychedelic pictures, filled with indistinct shapes and warped pinwheels of color. He passed them around the class, and was delighted when the students quickly deemed one of the blobs a dog ear. A group of 7-year-olds had just deciphered the inner visions of a neural network. Carter is among the researchers trying to pierce the "black box" of deep learning. Neural networks have proven tremendously successful at tasks like identifying objects in images, but how they do so remains largely a mystery.

When people think of the greatest artists who've ever lived, they probably think of names like Beethoven or Picasso. No one would ever think of a computer as a great artist. But what if one day, that was indeed the case. Could computers learn to create incredible drawings like the Mona Lisa? Perhaps one day a robot will be capable of composing the next great symphony. Some experts believe this to be the case. In fact, some of the greatest minds in artificial intelligence are diligently working to develop programs that can create drawing and music independently from humans. The use of artificial intelligence in the field of art has even been picked up by tech giants the likes of Google. The projects that are included in this paper could have drastic implications in our everyday lives. They may also change the way we view art.

Over the last few years Deep Learning was applied to hundreds of problems, ranging from computer vision to natural language processing. In many cases Deep Learning outperformed previous work. Deep Learning is heavily used in both academia to study intelligence and in the industry in building intelligent systems to assist humans in various tasks. The goal of this post is to share amazing applications of Deep Learning that I've seen. I hope this will excite people about the opportunities this field brings, as well as remind us that every new technology carries with it potential dangers. I believe the latter is especially true about Deep Learning, and I hope that by exposing people to all these amazing results I can encourage more discussion on the topic. There are many different applications and this list below is in no way exhaustive. So if you know of other cool applications I would appreciate it if you can mention them in the comments.

Back in high school, when I was being counseled on where to put my energy as I transitioned into college, many adults scoffed at the art degree I wanted. My grades were weighted with APs and I loved school, so my GPA was actually more than a 4.0. Adults argued that I could certainly find something more practical and lucrative with my grades; they treated art as something expendable, a last resort. That's not what art is at all. Now, more than a decade later, I am so glad that I got two degrees in art.

Michelangelo's'Creation of Adam' as seen through Google's Deep Dream Created by digital artist Kyle McDonald using Google's Deep Dream program. Have you ever wondered how your computer sees the world? Spoiler alert: it's the stuff of psychedelic nightmares, as the internet found out last month when Google revealed that in order to sort and categorize images online, it uses an artificial intelligence program that looks for patterns and sometimes gets things wrong, finding random dog faces, swirls, and hands where there are none. Google opened the source code up to developers under the name "DeepDream," and now, a couple new websites have sprung up, including a recent one from Psychic VR Lab and (h/t: Prosthetic Knowledge) and earlier, one from entrepreneur Zain Shah called Deep Neural Net Dreams, or #DeepDream for short. Both take code from Google's AI and let you upload your own photos, transforming them into eerie, computerized dreamscapes.

Technological innovations can be so deceptively smart that they become boring. There's only so much talk about innovation causing another marketing transformation or a piece of technology breaking barriers and disrupting the status quo before that feeling of wonder goes away. But there's always a story that can be told--and, as a marketer for a tech enterprise, it's your task to recapture that sense of awe. Imagine this scenario: Despite your best efforts, engagement for your company's digital publication has fallen--much to your horror. Time on page has shrunk.