We now experience life through an algorithmic lens. Whether we realize it or not, machine learning algorithms shape how we behave, engage, interact, and transact with each other and with the world around us.
Deep learning is the next advance in machine learning. While machine learning has traditionally been applied to textual data, deep learning goes beyond that to find meaningful patterns within streaming media and other complex content types, including video, voice, music, images, and sensor data.
Deep learning enables your smartphone’s voice-activated virtual assistant to understand spoken intentions. It drives the computer vision, face recognition, voice recognition, and natural language processing features that we now take for granted on many mobile, cloud, and other online apps. And it enables computers—such as the growing legions of robots, drones, and self-driving vehicles—to recognize and respond intelligently and contextually to the environment patterns that any sentient creature instinctively adapts to from the moment it’s born.
But those analytic applications only scratch the surface of deep learning’s world-altering potential. The technology is far more than analytics that see deeply into environmental patterns. Increasingly, it’s also being used to mint, make, and design fresh patterns from scratch. As I discussed in , deep learning is driving the application logic being used to create new video, audio, image, text, and other objects. Check out for a nice visual narrative of how deep learning is radically refabricating every aspect of human experience.
by generating and superimposing onto the original any visual elements that were missing, obscure, or misleading.
Clearly, the power to construct is also the power to reconstruct, and that’s tantamount to having the power to fabricate and misdirect. Though we needn’t sensationalize this, deep learning’s reconstructive potential can prove problematic in cognitive applications, given the potential for algorithmic biases to cloud decision support. If those algorithmic reconstructions skew environmental data too far from bedrock reality, the risks may be considerable for deep learning applications such as self-driving cars and prosthetic limbs upon which people’s very lives depend.
, we should be instrumenting deep learning applications to facilitate identification of the specific algorithmic path (such as the end-to-end graph of source information, transformations, statistical models, metadata, and so on) that was used to construct a specific artifact or take a particular action in a particular circumstance.
Just as important, every seemingly realistic but algorithmically generated artifact that we encounter should have that fact flagged in some salient way so that we can take that into account as we’re interacting with it. Just as some people wish to know if they’re consuming genetically modified organisms, many might take interest in whether they’re engaging with algorithmically modified objects.
If we’re living in an algorithmic bubble, we should at the very least know how it’s bending and coloring whatever rays of light we’re able to glimpse through it.