Machine Learning

“The powers that be no longer have to stifle information. They can now overload us with so much of it, there’s no way to know what’s factual or not. The ability to be an informed public is only going to worsen with advancing deep fake technology.” J. Andrew Schrecker All of us have heard Donald Trump refer to some television stations as ‘fake news’ and that we shouldn’t listen to them.
Last year we published our ML workflow landscape. One category we’ve seen continued interest in is data labeling, the process of attaching meaning to different types of digital data like audio files, text, images, and videos. Our research suggests themes in the data labeling segment include: 1) data is the new oil, 2) dark data is valuable, 3) deep learning algorithms are a driver, 4) hand labeling can be expensive, and 5) automation is important.
Art has always existed in a complex, symbiotic and continually evolving relationship with the technological capabilities of a culture. Those capabilities constrain the art that is produced, and inform the way art is perceived and understood by its audience. Like the invention of applied pigments, the printing press, photography, and computers, we believe machine intelligence is an innovation that will profoundly affect art. As with these earlier innovations, it will ultimately transform society in ways that are hard to imagine from today’s vantage point; in the nearer term, it will expand our understanding of both external reality and our perceptual and cognitive processes.