Can Computers be Creative? How? How can [a creative idea] arise, then, if not by magic? And how can one impossible idea be more surprising, more creative, than another? How can creativity happen?
– from Margaret Boden. Creativity and Unpredictability. Stanford Electronic Humanities Review 4(2), 1995.
Abstract: This paper demonstrates emergence of computational creativity in the field of music. Different aspects of creativity such as producer, process, product and press are studied and formulated. Different notions of computational creativity such as novelty, quality and typicality of compositions as products are studied and evaluated. We formulate an algorithmic perception on human creativity and propose a prototype that is capable of demonstrating human-level creativity. We then validate the proposed prototype by applying various creativity benchmarks with the results obtained and compare the proposed prototype with the other existing computational creative systems. I. INTRODUCTION Computational creativity is the modeling or replicating human creativity computationally. Traditionally computational creativity has focused more on creative systems' products or processes, though this focus has widened recently. Research on creativity offers four Ps of creativity (Rhodes, 1961; MacKinnon, 1970; Jordanous, 2016). These four P's are: 1. Person/Producer: a creative agent 2. Process: an activity done by the creative agent 3. Product: the product of the creative process 4. Press/Environment: the overall environment of creativity 110 The proposed methodology addresses all the four P's of creativity unlike most of recent works, which focus on these individually (Saunders, 2012; Gervas & Leon, 2014; Misztal & Indurkhya, 2014; Sosa & Gero, 2015; Besold & Plaza, 2015; Harmon, 2015). Figure 1 gives a simplified view of proposed computational creative system in the context of four P's of creativity.
The game of Go played between a DeepMind computer program and a human champion created an existential crisis of sorts for Marcus du Sautoy, a mathematician and professor at Oxford University. "I've always compared doing mathematics to playing the game of Go," he says, and Go is not supposed to be a game that a computer can easily play because it requires intuition and creativity. So when du Sautoy saw DeepMind's AlphaGo beat Lee Sedol, he thought that there had been a sea change in artificial intelligence that would impact other creative realms. He set out to investigate the role that AI can play in helping us understand creativity, and ended up writing The Creativity Code: Art and Innovation in the Age of AI (Harvard University Press). The Verge spoke to du Sautoy about different types of creativity, AI helping humans become more creative (instead of replacing them), and the creative fields where artificial intelligence struggles most.
I always loved products and technology. But ever since I was a child, I was especially fascinated by these big inventions, powered by transformative technological revolution that changed - everything! So I felt extremely lucky, when about 20 years ago, at the beginning of my career, I was just in time for one of these revolutions: when the Internet happened. Through the connected PC, the world we lived in has been transformed from a "physical world" -- where we used to go to places like libraries, and use things like encyclopedias and paper maps, to a "digital world" -- where we consume digital information and services from the convenience of our home. What was especially amazing, was the rate and scale of this transformation.
Through my Twitter and on LinkedIn feeds I see a lot of postings about technology. Many (primarily technology experts) write about the massive potential of technologies, for example Artificial Intelligence (AI), Blockchain, Cloud, Internet of Things (IoT), mobile and other technologies. In the current blog I will refer specifically to AI, not to other technologies. Other people write about AI in a way that implies that they fear AI; that AI is a risk, maybe more than an opportunity. Articles with titles like "Robots will take our jobs. We'd better plan now, before it's too late" can create fear, especially when non-tech-experts read the title on Twitter, absorb the connotation "robots danger for my job", without reading the full article and doing additional research on the topic.
Human creativity will be key to avoiding the diminishing returns of technology and data as artificial intelligence (AI) and machine learning become an everyday part of our marketing lives. That's the view of IDC research director, Gerry Murray, who took to the stage as the keynote of CMO and CIO's recent Executive Connections events in Melbourne and Sydney to share how AI use cases are starting to proliferate across the marketing and customer engagement sphere. Importantly, he also discussed what marketing and technology leaders should be doing to realise their potential. As Murray pointed out, AI is going into every single marketing tool out there today, and he noted more than 80 use cases identified by IDC as part of recent research. These stretch from virtual sales reps and social sentiment analysis, through to lead scoring, AI-powered content marketing, chatbots, recommendation engines and attribution analysis.
Technology still cannot simulate human intelligence to solve complex problems, in a variable environment and with partial information. But it is getting closer. An example is the case of autonomous vehicles, able to make optimal decisions in real time, thanks to complex algorithms that take into account multiple data. Another example is AlphaZero, the algorithm developed by DeepMind, Google's artificial intelligence division, concerning which Science recently published an article. AlphaZero is able to win in the board games that are the most complex for the human mind: chess, shogi (Japanese chess) and go (traditional Chinese board game).
To the editor: I first saw Andre Previn at the Hollywood Bowl in 1965, and it was love at first sight. I remember going backstage after the concert, where it was crowded with movie stars -- but the autograph I wanted was Previn's. I still have the album of his jazz variations on the "My Fair Lady" score that he signed for me so many years ago. I saw Previn many times at the Hollywood Bowl after that, especially while he was music director of the Los Angeles Philharmonic in the late 1980s. I remember so well Previn conducting Rachmaninoff's Piano Concerto No. 2, Grieg's Piano Concerto in A minor and Gershwin's Concerto in F from the keyboard.
This piece was written by Nicoletta Iacobacci, global ethics catalyst and adjunct professor at Webster University and Jinan University, and author of Exponential Ethics. For more like this, see our digital government newsfeed. Science fiction is becoming science fact as exponential growth in technology happens all around us. Ethics, however, has a hard time keeping pace. While new moral guidelines are defined for existing anomalies, technology surges ahead, giving rise to newer ethical debates -- making it increasingly difficult to keep up with the paradigm shift of our own innovations.
Microsoft has announced two new cloud services to help administrators detect and manage threats to their systems. The first, Azure Sentinel, is very much in line with other cloud services: it's dependent on machine learning to sift through vast amounts of data to find a signal among all the noise. The second, Microsoft Threat Experts, is a little different: it's powered by humans, not machines. Azure Sentinel is a machine learning-based Security Information and Event Management that takes the (often overwhelming) stream of security events--a bad password, a failed attempt to elevate privileges, an unusual executable that's blocked by anti-malware, and so on--and distinguishes between important events that actually deserve investigation and mundane events that can likely be ignored. Sentinel can use a range of data sources.
Fifteen years ago I came to Bangalore, India's Silicon Valley, to do a documentary on outsourcing. One of our first stops was a company called 24/7 whose main business was answering customer service calls and selling products, like credit cards, for U.S. companies half a world away. The beating heart of 24/7 back then was a vast floor of young phone operators, most with only high school degrees, save for a small pool of techies who provided "help desk" advice. These young Indians spoke in the best American English, perfected in a class that we filmed, where everyone had to practice enunciating "Peter Piper picked a peck of pickled peppers" -- and make it sound like they were from Kansas not Kolkata. The operations floor was so noisy from hundreds of simultaneous phone conversations that 24/7 installed a white-noise machine to muffle the din, but even then you could still occasionally hear piercing through the cacophony some techie saying to someone in America, the likes of: "What, Ma'am? Your computer is on fire?"