Alan Turing is considered the father of artificial intelligence, and rightfully so. Marrying mathematical study with computer science, Turing was the first to contend that computers could think like humans, and he pioneered the concept of machines that could perform tasks on par with human experts – a bedrock concept of modern AI computer science to this day. Given the intense interest in AI of recent years, Turing is more famous now than he was at the time of his death, 15 days shy of his 42nd birthday in 1954. I'm constantly amazed by Turing's prescience in laying the theoretical groundwork for what he called thinking computers, those that exhibit intelligent behavior equal to or indistinguishable from that of a human. However, Turing's work occurred more than 65 years ago, and -- give the guy a break -- while several of his predictions are uncannily on the mark, he wasn't able to foresee all the advances that are shaping life in 2019.
Just as manufacturing automation cuts into human jobs, the prospect of creative artificial intelligence raises the specter of robot writers, robot artists and robot musicians who never sleep and always agree with their patron. Robert and Christian discuss some possibilities in this episode of the Stuff to Blow Your Mind podcast.
Are machine learning and creativity at odds? And don't just take it from us. We sat down with Justin Billingsley, CEO at Publicis Emil; Dawn Winchester, chief digital officer at Publicis North America; Andrew Shebbeare, co-founder and chairman of Essence; and Vijay Sharma, FlipKart's head of digital media and brand marketing. They explain how creativity is being empowered by the most recent advances in technology, and why great creatives love data and automation.
The marketplace for definitions and theories of creativity is crowded indeed. No hard consensus exists on the elements of an all-embracing theory, or on what specific sub-processes and representations are required to support creativity, either in humans or in machines. Yet commonalities do exist across theories: a search for novelty and utility is implied by most theories, as is the notion that an innovation can be considered creative only if it is not too novel, and can be adequately grounded in the familiar and the understandable. Computational creativity (CC) is the pursuit of creative behavior in machines, and seeks inspiration from both AI and from human psychology. As a practical engineering endeavor, CC can afford to adopt a cafeteria approach to theories of creativity, taking what it needs from different theories and frameworks. In this paper we present a vision for CC research in the age of the Web, in which CC is provided on tap, via a suite of Web services, to any third-party application that needs it. We argue that this notion of Creativity as a Service – which is already a popular business model for human organizations – will allow CC researchers and developers to build ad-hoc mash-ups of whatever processes and representations are most suited to a given application. By offering CC as a centralized service, we can collect statistics on the most useful mash-ups, and therein obtain a new empirical basis for theorizing about creativity in humans and in machines.
In this brief communication I first give an overview of for different branches in creativity research: investigating psychological/cognitive mechanisms of creativity; designing creativity support tools; metaphysical / philosophical / anthropological explorations on the nature of creativity; and computational models of creativity. Then I discuss their relations and complementarity and finally, in the conclusion, I suggest that an attempt to create a unified framework for creativity research would benefit the field as a whole.